Papers

Permanent electric dipole moments (EDMs) of nucleons, nuclei, atoms, and molecules are among the most sensitive probes of CP violation beyond the Standard Model and are intimately connected to the strong CP problem and the origin of the matter-antimatter asymmetry of the universe. This review presents the theory of EDMs from the bottom up, tracing the chain of connections that links CP-violating interactions at level of elementary particles to observable EDMs across a wide range of systems. Starting from a general CP-odd effective Lagrangian at the quark-gluon level comprising the QCD theta term, quark EDMs and chromo-EDMs, the Weinberg operator, and CP-odd four-fermion interactions, I show how chiral perturbation theory organizes the nonperturbative QCD dynamics into a small set of hadronic low-energy constants, whose relative sizes are determined by the chiral representation of the underlying source. These hadronic interactions feed into calculations of nuclear EDMs and Schiff moments, which in turn enter atomic and molecular structure calculations that connect to experimentally accessible observables in diamagnetic and paramagnetic systems. Special attention is given to the recently identified sensitivity of paramagnetic systems to hadronic CP violation, which opens a new and relatively unexplored window on the quark-gluon sector. The complementarity of the full EDM portfolio including the neutron, light nuclei, atoms, and molecules, and the role of theory in disentangling the underlying source of CP violation is discussed throughout.

We present a laser system referenced to a room-temperature ultrastable cavity employing crystalline AlGaAs coatings. We demonstrate a fractional frequency instability of $4.2 \times 10^{-17}$, which is one of the lowest for room temperature systems and surpasses the limit imposed by Brownian noise if dielectric coatings were employed. For the first time in a room temperature system we identified the spontaneous fluctuations of the coating birefringence as a leading contribution to frequency instability. At optimized conditions we achieve an ultrastable cavity with an eigenfrequency that is highly immune to power fluctuations. As acceleration noise is the main noise contribution, we demonstrated that a feed-forward method can reduce the influence of accelerations on the cavity-stabilized laser frequency by a factor of four.

This paper explores the selective suppression of Laguerre-Gaussian modes that are most vulnerable to atmospheric turbulence. Decomposing these modes into an orthogonal Zernike polynomial basis reveals significant differences in stability depending on the radial and azimuthal indices. We demonstrate that modes with a higher radial index exhibit minimal distortion of the transverse beam profile, providing a clear criterion for filtering out less resilient modes in turbulent media. Furthermore, we derive an analytical expression relating the required receiver aperture to the radial and azimuthal indices.

Polymeric stresses in viscoelastic flows arise from the deformation of polymer chains and are commonly computed using Eulerian constitutive models, in which the conformation tensor is evolved as a transported field over the entire domain. This approach is computationally intensive, prone to numerical instabilities, and not directly applicable to experimentally measured velocity fields. In this work, we develop a Lagrangian integration scheme that reconstructs the polymeric stress field from the deformation-gradient history along fluid element trajectories in a known, steady velocity field. This approach avoids solving the full Eulerian constitutive transport equation, which we develop for the nonlinear FENE-P model as well as the Oldroyd-B model as a reference case. After validation on unidirectional, canonical flows, the scheme is applied to non-trivial channel flows past circular obstacles using velocity fields quantified from both numerical simulations and microfluidic experiments. The reconstructed stress fields across both experiments and simulations are in agreement with traditional Eulerian reference solutions. Not only does this new Lagrangian scheme enable the quantification of stress fields directly from experimental velocity field data, but it also enables partial or whole-field mapping of stresses without solving fully-coupled viscoelastic constitutive equations.

We present new and improved calculations of Stark-broadened profiles for ionized helium, a key ingredient in the spectroscopic analysis of helium-atmosphere DO white dwarfs. Our approach builds upon the computer simulation framework previously developed for neutral helium, which fully accounts for the dynamical interactions of both ions and electrons with the emitting helium atom. We extend this theoretical formalism by relaxing the assumption of straight-line trajectories for the perturbing particles (electrons and ionized helium) and adopting the hyperbolic trajectories appropriate for their interaction with a charged emitter, thereby accounting for their dynamical influence on the line-broadening process. In this exploratory study, we focus on the He II 4686 line, the strongest absorption feature observed in the spectra of DO white dwarfs. We present the resulting Stark profiles and perform a detailed comparison with those available in the literature.

Quantum mechanical (QM) cluster models provide an effective framework for mechanistic studies of enzymatic reactions but remain computationally demanding. Neural network potentials (NNPs) offer a promising route to reduce this cost, but enzymes present challenges beyond small molecules, including large system sizes, implicit-solvent environments, substantial polarization, and charge transfer. Here, we present an integrated software framework for efficient NNP training for mechanistic studies of enzymes, demonstrated on QM cluster models of S-adenosyl-L-methionine-dependent methyltransferases (MTases). Our Enerzyme code introduces modular electrostatics-aware NNP architectures and combines automated QM-cluster construction with reactive dataset generation. The Enerzymette subpackage automates reaction pathway exploration at both NNP and DFT levels. We show that iterative flexible scans and nudged elastic band calculations impose stricter requirements on NNPs than conventional dataset metrics. Nevertheless, NNPs trained on fewer than 1,000 system-specific datapoints reproduce reaction energetics and transition-state structures for MTase clusters containing up to 545 atoms with near-chemical accuracy. Direct supervision of atomic charges and consistent dielectric screening substantially improve simulation stability and accuracy, while multitask-learned atomic charges capture charge transfer and polarization trends and provide chemically meaningful descriptors of reactivity. Finally, transferability across chemically diverse catechol O-methyltransferase substrates indicates that NNPs learn generalizable reactivity patterns as training data expand across multiple enzymes. Together, these results establish a foundation for accelerating enzyme mechanistic studies and guide future NNP development for biomolecular reactivity.

We present a low-cost laboratory for investigating the dynamics of a vertical mass-spring oscillator using real-time computer vision. The proposed system automatically tracks the motion of the oscillating mass with a smartphone camera, displays the displacement in real time, and exports the recorded data for further analysis. Representative experiments include damped oscillations, oscillation period measurements, spectral analysis, and phase-space reconstruction. The system also enables the investigation of nonlinear phenomena, including harmonic generation, frequency mixing, and energy exchange between coupled oscillation modes. Owing to its low cost, ease of construction, and automated data acquisition, the proposed apparatus extends the traditional mass-spring experiment to include topics in nonlinear dynamics that are rarely explored in undergraduate laboratories.

This paper presents a new hybrid MC/deterministic method for solving the one-group steady-state Boltzmann transport equation based on decomposition of solution in macro and micro components. The macro component captures the large-scale structure of the solution. It is represented by angular moments of the high-order transport solution. The $P_1$ approximation is applied to define the macro component. The first two angular moments are obtained as a solution of hybrid low-order moment equations with exact closures. The equation for the micro component is solved using a MC simulation. The hybrid two-level system of equations for macro and micro components is solved by fixed-point iteration scheme. Numerical results are presented to demonstrate variance reduction of stochastic numerical solution and improvement in computational efficiency.

What would it be like to be in a superposition of yesterday, today, and tomorrow? This question may seem at best entertaining, but it is necessary, and exploring it allows us to understand how exact irreversible clocks and change are possible, despite the Unruh-Wald and Hegerfeldt-Ruijsenaars no-go theorems forbidding them. Unruh and Wald (1989) proved that if energy is bounded from below, no observable can increase monotonically with the Schrödinger time parameter t. Perfectly monotonic clocks and irreversible observable changes (Hegerfeldt-Ruijsenaars, 1980) seem impossible. From the perspective of the Schrödinger time, the world appears in a superposition of different intrinsic clock states indicating different times and opposite time directions. This seems to directly contradict our daily experiences of time and change. I show that there is no contradiction: from an intrinsic perspective of the world, sharp irreversible changes do happen, because the macroscopic pointer states resolve the superposition of different times. Large-scale time-reversing or discontinuous transitions are not internally observable in the records. From the intrinsic perspective, an unbounded intrinsic-time translation generator plays the role of the Hamiltonian, generating only forward time evolution with respect to the intrinsic time, but not to the Schrödinger parameter t, which is thus not justified to play the role of time. This allows sharp time observables even if the external Hamiltonian is bounded from below. In addition, this leads to a stationary wavefunction of the universe satisfying a Wheeler-DeWitt-type equation, without assuming gravity.

The canonical formulation of physical theories with irregular nomic structure is as constrained Hamiltonian theories within which ill-posedness of the equations of motion is connected to a pernicious form of surplus representational capacity. Such theories can be converted into theories with regular nomic structure and a well-posed initial value problem via the process of symplectic reduction. We analyse, synthesise, and contrast different approaches to the presentation and analysis of constrained Hamiltonian theories, drawing upon recent work on formalisation of nomic structure on model spaces (Gryb and Thébault 2024) and comparisons of theoretical structure and representational capacity via category theory (Bradley and Weatherall 2020; Bradley 2025b). We suggest that the case of irregular nomic structure is most naturally suited to a category theoretic presentation in which state spaces are arrows and symplectic reduction is arrow composition (Landsman 2005). Under this approach one obtains the natural results that theories with isomorphic state spaces are equivalent and theories whose reduced state spaces are isomorphic are equivalent at the level of the regular representations of their nomic structure. This analysis provides a suitable foundation for the case of quantization of theories with irregular nomic structure, which will be in a companion paper.

Gravitational collapse creates large amounts of kinetic energy that could potentially seed turbulence. If such turbulence were also suitable to initiate dynamo action, the resulting magnetic field would further modify the dynamics, especially on small length scales. However, a small-scale dynamo is believed to require vortical turbulence, whereas the collapse produces mainly irrotational motions, which may not be efficient for dynamo action. Here, we study the efficiency of vorticity production during a turbulent collapse. We adopt a barotropic equation of state, where pressure and density gradients are parallel, and no magnetic field, so that vorticity can only be produced by viscosity and by vortex dynamo-type amplification. Using direct numerical simulations of gravitational collapse, we show that, for the parameter space accessible to our numerical resolution, these effects are related to the initial irrotational turbulence and are not induced by the collapse. Vorticity production along with the associated small-scale dynamo action are still expected to occur for sufficiently large Reynolds numbers, especially when baroclinic effects are allowed for, but some of the earlier numerical evidence in the literature using a barotropic equation of state is now found to be the result of subgrid scale modeling and not reproduced in direct numerical simulations.

The novelty of this work is that it takes a result of macromolecular theory that is not objective, and fixes it. To do so we use an objective vorticity tensor to obtain a fully frame invariant form of the classical constitutive equation for FENE dumbbell fluids obtained within the conceptual framework of kinetic theory for polymer solutions. The influence of such an objective formulation is discussed for steady shear flows.

We present the verification of a thermo–hydrologic–mechanical capability implemented within the PFLOTRAN framework, with emphasis on benchmark-based assessment of the THM implementation. The thermal–hydrologic (TH) equations for mass and energy balance are solved on control-volume blocks or Voronoi cells, while the quasi-static momentum balance is solved on an element-based dual mesh. The coupling is achieved using a strictly sequential, non-iterative fixed-stress split strategy in which the TH system is solved implicitly for pressure and temperature, followed by a mechanics update for the displacement unknowns. Several verification problems are set up against poroelastic and thermo-poroelastic benchmarks, demonstrating agreement with analytical or semi-analytical benchmark responses for pressure diffusion, the temperature field, and mechanical deformation. In addition, we propose a treatment for discontinuities (e.g., fractures) based on mapping between mechanical and flow degrees of freedom, and validate the approach by comparison to an analytical solution. This work establishes the basis for thermo-poroelastic coupling in PFLOTRAN and provides a solid modeling foundation for a range of applications (e.g., enhanced geothermal systems and other subsurface energy storage) involving coupled thermal–hydrologic–mechanical (THM) processes in geologic porous media.

Accurate 3D localization of radiation interactions in scintillation detectors is essential for nuclear and particle physics, safeguards, and medical imaging, but remains difficult in light-starved regimes with limited photon statistics. We present PRISM, a multifocal plenoptic imaging system designed for millimeter-scale 3D position reconstruction in a single-volume scintillator. PRISM uses a multifocal microlens array with diverse focal lengths and high effective numerical aperture to balance photon collection with spatial and depth encoding. A Cram'er–Rao lower bound analysis shows that the multifocal design improves axial sensitivity over conventional unifocal plenoptic systems under photon-limited conditions. We build a prototype system, calibrate its optical response with a tunable light source, and form photon-limited measurements with $\mathcal{O}(100)$ detected photons. For sparse single-vertex events, we reconstruct interaction locations using an Alternating Descent Conditional Gradient-inspired algorithm and demonstrate an average 3D localization error of approximately 1 mm. We also provide an initial evaluation of double-vertex events, showing that localization improves as the axial separation between interactions increases. These results demonstrate that multifocal plenoptic imaging can mitigate the traditional trade-off between light collection and spatial resolution, providing a photon-efficient approach to 3D reconstruction in scintillation detectors and a foundation for future multi-scattering event reconstruction.

We present a symmetric Lanczos method for computing charge-changing QRPA strength functions in atomic nuclei. Starting from the finite-amplitude-method formulation of the QRPA linear-response problem, we derive equivalent spectral representations and, in the real case, a reduced eigenvalue problem involving the matrix products $MK$ and $KM$, where $M\equiv A+B$ and $K\equiv A-B$ are formed from the usual QRPA matrices $A$ and $B$. The resulting formulation enables a matrix-free Lanczos approximation of the Lorentzian-smeared strength function over a broad energy interval from a single Krylov run, in contrast to conventional frequency-by-frequency response calculations. Numerical tests for $^{112}$Sn and $^{150}$Nd first show that GMRES reproduces the converged iterative FAM strength profiles while requiring fewer iterations. Using GMRES as the frequency-by-frequency reference, we then show that the Lanczos approximation reproduces the same strength profiles with reduced overall cost. These results indicate that symmetric Lanczos projection provides an efficient and accurate approach for QRPA strength-function calculations when spectral information is required over an extended frequency range.

Valley coherence refers to a phase-coherent superposition of inequivalent momentum valleys, in which quantum information can be encoded in the relative valley phase. Chiral nematic liquid crystals, by imposing a flip of the spin angular momentum upon light reflection, provide an effective photonic environment for optically coupling excitons of the K and K' valleys in monolayer semiconducting transition-metal dichalcogenides. We experimentally demonstrate that using such liquid crystal as a substrate, it is possible through nearfield interaction to engineer a room temperature mechanism for inducing the intervalley coupling. Our results show that this approach provides a simple and scalable route toward valleytronic functionalities based on controlled coherent emission from valleys with opposite Berry curvature.

Direct Numerical Simulations (DNS) of a spatially developing supersonic turbulent shear layer are conducted for a range of convective Mach numbers ($M_c$) and velocity parameters ($\lambda$) to examine the effects of compressibility and advection on the growth rate, self-similarity, flow statistics, asymmetry, and entrainment of the layer. At distant downstream locations, self-similarity is attained for all cases. The self-similar region is identified by the collapse of normalized mean streamwise velocity, the constant peak of normalized Reynolds stresses, and the linear growth rate of the shear layer thickness and momentum thickness. Despite significant variations in lower-order and higher-order statistics across different $M_c$ and $\lambda$ values, profiles of all turbulence quantities examined collapse within the self-similar region using our proposed self-similar scalings. The self-similar forms of continuity, momentum, and energy equations have been formulated, incorporating compressibility and centerline shifts. The self-similar normalized density distribution inside the layer is used to explain the effects of compressibility on various flow statistics, including the far-field cross-stream velocity. The density variation is linked to dissipation effects as revealed by our analysis of the self-similar energy equation. An approximate equation for the cross-stream velocity is developed, and the profiles of cross-stream velocity obtained from this equation show good agreement with the DNS results. A geometric interpretation of the entrainment ratio is presented, and the approximate equation for the cross-stream velocity is used to provide a general closed-form expression of the entrainment ratio. The entrainment ratio increases with $M_c$ and $\lambda$, favoring excess entrainment on the high-speed side.

Weaving patterns in fabrics, traditionally used for aesthetic purposes, present a largely untapped opportunity to create metamaterials that serve as passive layers for sensing, filtering, and signal processing. However, the hierarchical architecture of fabrics makes structural design and wave prediction challenging. Here, we establish fully woven fabrics as phononic crystals that passively filter and route elastic vibrations. Using double weaving, we integrate a soft cotton weave with stiff woven copper inclusions to form periodic fabric lattices with engineered dispersion. A multiscale modeling framework that combines homogenization of weave blocks with an effective-property macroscale model enables computationally efficient design of phononic crystals. Simulations and experiments confirm a pronounced phononic bandgap for out-of-plane vibrations in a finite fabric crystal, while an equivalent pure cotton weave shows no band suppression in the corresponding frequency range. Building on the same platform, we realize a fully woven higher-order topological insulator. Modal analysis and transmission measurements reveal in-gap edge states and localized corner states. These results show that phononic bandgaps and topological states can be directly encoded through weaving patterns and material contrast, enabling passive vibration management layers and multifunctional waveguiding fabrics for sensing, haptic interfaces, robotics, and noise mitigation.

Chemical gradients are ubiquitous in porous and crowded environments, including soils, filters, fabrics, tissues, hydrogels, biofilms and living cells. They arise from displacement fronts, dissolution and precipitation, ion exchange, metabolism, root exudation, evaporation, gas dissolution, freeze–thaw cycles and externally imposed chemical treatments. These gradients can drive colloids, macromolecules and emulsion droplets by diffusiophoresis, while simultaneously driving diffusioosmotic flows along confining surfaces. Classical models of colloid transport in porous media emphasize hydrodynamic dispersion, surface interactions, straining, deposition, detachment and filtration. This chapter places diffusiophoresis within that broader transport framework and reviews how porous media generate, stretch, disperse and sustain the solute gradients that drive phoretic motion. We first discuss sources of chemical gradients and the distinction between spreading and mixing, then summarize classical colloid transport, the minimal physicochemical model for diffusiophoresis and diffusioosmosis, and the experimental platforms used to study these effects. Particular emphasis is placed on recent results showing that diffuse solute fronts can enhance phoretic removal from dead-end pores by prolonging the duration of forcing, and that cross-streamline migration within flowing pathways can change macroscopic breakthrough and dispersion by orders of magnitude. We close by discussing emulsion droplets, multiphase flows, confined and living media, and open problems, including the transition from algebraic mixing in two-dimensional micromodels to chaotic mixing in three-dimensional porous media.

Radio-frequency (RF) sensing is a central modality for counter-unmanned-aerial-system (counter-UAS) defence because it exploits the control, telemetry, and video links between a drone and its operator. Reported accuracies for RF-based drone detection and identification are often very high, but many are obtained using cross-validation that splits a small number of continuous recordings into short segments. This can place near-duplicate slices of the same recording in both training and test partitions, creating data leakage. We study this leakage problem through theory and measurement. We formalise the optimism of segment-level cross-validation and show, using Cover's function-counting theorem, that a classifier can exactly memorise the recording-to-label map when the number of independent recordings, R, is small relative to the feature dimension, d. In particular, this can occur when 2R is less than or approximately equal to d. Under these conditions, naive accuracy approaches 1, and the inflation gap approaches 1 - ACC*, where ACC* is the Bayes accuracy. The inflation eases only once R grows beyond this separability threshold. A controlled synthetic experiment with 10 seeds confirms the predicted curves: naive balanced accuracy rises from the Bayes level toward 1.0 as recording-specific nuisance variation grows, while honest recording-grouped evaluation declines to chance, with a gap reaching about 0.5. On the public DroneRF dataset, pooled leave-one-recording-out cross-validation shows drone type identification, AR versus Bebop, collapsing from a naive macro-F1 of 0.74 to 0.46, the two-class chance level. A leakage-pathway ablation attributes essentially all of the inflation to segment-level leakage.

In this paper, we develop a physics-informed convolutional neural network (PICNN) assisted physics-preserving method for a thermodynamically consistent model of incompressible and immiscible two-phase flow in porous media. Following the physics-preserving prediction-correction scheme of Li et al. \cite{li2025class}, the prediction step is performed by a PICNN trained with finite-volume residuals, where the interfacial fluxes are evaluated by the two-point flux approximation (TPFA) using two-point difference quotients of neighboring cell-centered unknowns to approximate interfacial normal gradients. The PICNN output is further corrected by a post-processing procedure to obtain energy-stable, mass-conservative, and bounds-preserving solutions. Numerical results show that the finite-volume residuals trained PICNN can replace the traditional prediction solver within the physics-preserving framework. Compared with conventional physics-informed neural networks (PINNs), the PICNN better captures local spatial interactions between each control volume and its neighboring cells, while the finite-volume residuals accommodate discontinuous permeability fields and interfacial flux continuity.

We determine with high accuracy the energy of the inner-shell transition $1s^2 2s^2~{}^1\mathrm{S}_0 \rightarrow 1s~2s^2~2p_{3/2}~{}^1\mathrm{P}_1$ ${}^{16}\mathrm{O}_{K\alpha}^{4+}$ at $554.372(3)~\mathrm{eV}$ ($\lambda$ = $22.36480(12)~\unicode{x212B}$) as well as its small shift of $2.2 \pm 1.3~\mathrm{meV}$ ($\Delta \lambda$ = $0.089(52)~\mathrm{m}\unicode{x212B}$) for the ${}^{18}\mathrm{O}$ isotope. This transition blends with a $K_\alpha$ line of $\mathrm{O}^{5+}$ used in astrophysical diagnostics, potentially affecting its reliability. In contrast to our experimental uncertainty of $\pm 3~\mathrm{meV}$, advanced electronic structure predictions for this four-electron system, including quantum electrodynamic (QED) corrections on the order of $100~\mathrm{meV}$, still scatter by more than $\pm 250~\mathrm{meV}$. Ions generated and stored in an electron beam ion trap were excited at the ELETTRA synchrotron facility with monochromatic soft x rays, with photon energies corrected by an additional spectrometer. Upon resonant excitation of $\mathrm{O}^{4+}$ and subsequent autoionization, we separate the photoions of each isotope by a time-of-flight measurement. This way, we resolve soft x-ray isotopic shifts of a few meV, obtain very accurate data on an essential astrophysical ion, and test calculations down to the level of QED contributions.

High-order coupled-cluster theories with iterative triples (CCSDT), perturbative quadruples [CCSDT(Q)], and iterative quadruples (CCSDTQ) provide benchmark-quality correlation energies, but their steep computational scalings, $O(N^8), O(N^9)$, and $O(N^{10})$, together with the large memory requirements of high-order amplitude tensors, have historically limited their application to small molecules. In this work, we develop efficient open-source implementations of spin-restricted CCSDT (RCCSDT), RCCSDT(Q), RCCSDTQ, and spin-unrestricted CCSDT (UCCSDT) within the PySCF package. The shared-memory implementation combines compact triangular storage of the highest-order amplitude tensors with the multithreaded tensor contraction backend pytblis, enabling efficient use of modern many-core CPU architectures. This design delivers near-ideal thread scaling up to 90 cores and achieves wall times shorter than or comparable to existing single-node implementations for representative benchmark molecules. We further extend RCCSDT, RCCSDT(Q), and RCCSDTQ to distributed-memory architectures using MPI-based algorithms. By distributing compact high-order amplitudes across MPI ranks and overlapping communication with computation through nonblocking data transfers, the distributed implementation achieves near-ideal strong scaling on up to 32 nodes, corresponding to approximately 3,000 CPU cores. These developments substantially extend the practical reach of canonical high-order CC theory, enabling CCSDT(Q) calculations with approximately 100 correlated electrons in 450 orbitals and CCSDTQ calculations with approximately 50 correlated electrons in 115 orbitals. Applications to $\pi$-stacked noncovalent dimers, the CO dissociation energy of Cr(CO)$_6$, and the Cope rearrangement of semibullvalene demonstrate that canonical high-order CC benchmarks are now feasible for chemically realistic molecular systems.

The flow and density field characteristics around a bubble curtain in a laboratory scale lock-exchange setup are investigated using two-phase large-eddy simulations. We study the detailed hydrodynamics and show that there are three qualitatively distinct (sub)regimes within the previously classified breakthrough regime. The occurrence of these regimes depends not only on air Froude number that characterises the relative strength of the bubble curtain and the gravity current, but also on an additional non-dimensional parameter: the density ratio between the salt and fresh water. The dependence on this additional parameter is also observed in how effective bubble curtains are in blocking the transport of salt to the fresh part of the lock. Hence, it has important implications for the optimisation of bubble curtains in ship locks.

Phytofluidic platforms have enabled controlled studies of plant roots, however, most existing systems either impose geometric confinement without flow or introduce hydrodynamics in single-channel devices that limit throughput and disrupt downstream analysis. New experimental platforms are therefore needed to investigate how roots integrate mechanical confinement and hydrodynamic nutrient transport, two defining features of the rhizosphere that remain difficult to reproduce under controlled laboratory conditions. Here, we present the Plant-on-a-Disc (POD), a phytofluidic platform that enables the parallel cultivation of eight seedlings under controlled hydrodynamic conditions while allowing non-invasive, in situ multimodal analysis of the intact root-shoot system. The device is fabricated in PDMS using a cost-effective wire-drawing technique to generate radial microchannels that converge into a central sump beneath an optical window. This design enables sequential bright-field, fluorescence, and Raman measurements using a single microscope objective without disturbing neighbouring seedlings. Dimensionless transport analysis and finite-element modelling confirm that the radial architecture equalizes hydraulic resistance across channels, establishing creeping laminar flow with convection-dominated nutrient transport under physiologically safe shear conditions. Using Brassica seedlings, we show that hydrodynamic flow drives coordinated root responses across multiple scales. Roots grown in flow condition exhibit accelerated elongation, substantial ROS generation and anisotropic cortical cell expansion, accompanied by carotenoid signatures detected by Raman spectroscopy.

Electric double-layer capacitors (EDLCs) rely on the dynamical response of confined electrolytes to store and release charge, yet the interplay between ion transport, electrostatic interactions, and electrode metallicity remains poorly understood at the nanoscale. We develop a comprehensive Brownian dynamics framework to compute the frequency-dependent admittance of nanocapacitors, explicitly accounting for salt concentration and the finite screening length of electrodes (modeled via Thomas-Fermi theory). We derive the fluctuation-dissipation relation connecting the dynamics of equilibrium charge fluctuations to the linear response of the system quantified by the frequency-dependent admittance. Specifically, we obtain two estimators for the admittance, based on ionic positions and forces, and combine them via a control variate method to reduce uncertainty across all frequencies. We show that the admittance exhibits a low-frequency regime dominated by capacitive effects, and a high-frequency one governed by the ideal Nernst-Einstein conductivity. The crossover between these regimes is characterized by a timescale that depends on both the electrode metallicity and salt concentration, highlighting the role of ion-wall collisions and electrostatic interactions. Comparisons with analytical models show that while mean-field theories capture qualitative trends, they systematically overestimate low-frequency admittance and underestimate high-frequency behavior, underscoring the necessity of explicit ion-ion and ion-wall interactions. This work connects microscopic dynamics to macroscopic electrochemical observables, offering a tool to interpret impedance spectra in nanoscale systems. Beyond charge storage in EDLCs, our framework provides insights for sensing applications in nanofluidic devices, where charge/current fluctuations enable the detection of electrochemically active species.

In previous work we sought to address the fundamental question “What is life?”. Building on that conceptual foundation, we here integrate traditional criteria for living systems with our recent proposals on physical identity and self, and apply them to concrete biological cases. We suggest that life can be characterized as the preservation of a well-defined identity “at all costs” together with the presence of a physically grounded self, and we show how this perspective illuminates the organization and social behavior of Dictyostelium and other multicellular systems.

Convective heat transfer is traditionally visualized from a Eulerian perspective using scalar temperature fields, offering limited insight into the underlying transport mechanisms. A Lagrangian view, analogous to mass transport along fluid paths, can reveal coherent structures and transport routes invisible from a Eulerian view of temperature. However, heat transport is aperiodic and non-conservative, hampering the application of fluid mixing and transport visualization techniques, developed primarily for time-periodic, conservative transport. We present a particle-based visualization technique that addresses these challenges by advecting massless particles along a time-reparameterized spacetime formulation of thermal transport, accumulating path contributions to reveal coherent transport routes and finite-time attracting and repelling structures that conventional methods cannot show.

TD-Link is an optical communication architecture that combines high-throughput data readout and sub-nanosecond timing synchronization over a single fiber for large-scale detector systems. It adopts a multidrop daisy-chain ring topology connecting a Data Concentrator to up to sixteen FERS front-end boards per link, with up to eight links per concentrator. Operating at 3.125 Gb/s, TD-Link carries data, synchronization, and control traffic in the same serial stream through a token-based streaming protocol that minimizes per-hop latency and supports on-the-fly payload fragmentation. Deterministic latency is enforced at two levels. On the concentrator, transmitter lanes are aligned by using the half-full flag of the multi-gigabit transceiver (MGT) elastic buffer as a one-bit phase detector: a firmware state machine steps the transmit phase interpolator (PI) until the buffer pointer difference crosses half-depth, locking every lane to the same phase condition. On each front-end board, where the synchronization pulse must also cross from the recovered-clock to the transmit domain, the residual three-to-five-cycle ambiguity of the synchronizer is removed by a calibration-free multiphase oversampling time-to-digital converter (TDC) with 0.8 ns bins, which time-stamps the incoming edge on the transmit time base so that a scheduler regenerates it at a fixed, programmable latency. A Digital Dual Mixer Time Difference (DDMTD) circuit measures the inter-concentrator phase. Tests with CERN PicoTDC-equipped FERS boards show a board-to-board synchronization $\sigma$ of 24 ps on the same ring, 27 ps across transceiver quads, and 28 ps across two cascaded concentrators, against an intrinsic front-end floor of 7 ps. Over more than one thousand cold boots without re-calibration, the boot-to-boot spread of the board-to-board skew stayed below 20 ps, consistent with the 18.6 ps phase-interpolator step.

Attosecond technologies provide unique opportunities to study electron dynamics and electron correlation on their intrinsic timescales. From a theoretical perspective, this places strong constraints as an accurate treatment of electron correlation is required. Recently, it was demonstrated that time-dependent density-functional theory (TDDFT) is capable of correctly predicting correlation-driven charge migration arising from hole mixing following ionization of the highest occupied molecular orbital (HOMO). Given the ability of TDDFT to treat large-scale systems, this approach offers promising perspectives for investigating electron-correlation-driven mechanisms in complex molecules. In this work, we assessed the constraints and limitations associated with using TDDFT to study this mechanism. We found that the charge-migration dynamics are already correctly reproduced using local-density approximation for the exchange-correlation functional, provided the states involved in the coherent superposition are well described within the TDDFT. However, for dynamics triggered by the ionization of orbitals below the HOMO, artificial ultrafast dynamics may appear on top of the charge-migration dynamics. These artifacts indicate that careful analysis of the simulated dynamics is required in order to reliably predict phenomena that could be observed experimentally.