Papers

We use direct numerical simulations to investigate fluid-solid interactions in suspensions of rigid fibres settling under gravity in a quiescent fluid. The solid-to-fluid density ratio is $\mathcal{O}(100)$, while the Galileo number ($Ga$) and fibre concentration ($n\ell_f^3$) are varied over the ranges $Ga \in [180, 900]$ and $n\ell_f^3 \in [0.36, 23.15]$; $\ell_f$ denotes the fibre length and $n$ the number density. At high $Ga$ and/or low $n\ell_f^3$, fibres cluster into gravity-aligned streamers with elevated concentrations and enhanced settling velocities, disrupting the flow homogeneity. As $Ga$ increases and/or $n\ell_f^3$ decreases, the fluid-phase kinetic energy rises and the energy spectrum broadens, reflecting enhanced small-scale activity. The flow anisotropy is assessed by decomposing the energy spectrum into components aligned with and transverse to gravity. Vertical fluctuations are primarily driven by fluid-solid interactions, while transverse ones are maintained by pressure-strain effects that promote isotropy. With increasing $Ga$, nonlinear interactions become more prominent, producing a net forward energy cascade toward smaller scales, punctuated by localised backscatter events. Analysis of the local velocity gradient tensor reveals distinct flow topologies: at low $Ga$, the flow is dominated by axisymmetric compression and two-dimensional straining; at high $Ga$, regions of high fibre concentration are governed by two-dimensional strain, while voids are associated with axisymmetric extension. The fluid motion is predominantly extensional rather than rotational.

A unitary coupled-cluster (UCC)-based self-consistent electron propagator theory (EPT) is proposed for the description of electron-detached and electron-attached states. Two practical schemes, termed IP/EA-UCC3 and IP/EA-qUCCSD, are developed and implemented within the UCC singles and doubles (UCCSD) framework using the perturbative and commutator-based truncation strategies for the similarity-transformed Hamiltonian $\bar{H}$. The numerical performance of these UCC-based EPT methods is evaluated primarily using full configuration interaction (FCI) reference data and compared with established approaches, including IP/EA-ADC(3), IP/EA-ADC(4) and IP/EA-EOM-CCSD. Benchmark calculations demonstrate that IP-qUCCSD achieves the highest overall accuracy among Hermitian ionized-state methods for one-hole (1h)-dominated IPs of closed-shell systems, with a mean absolute deviation (MAD) of 0.19 eV and standard deviation (SD) of 0.13 eV. Remarkably, despite the absence of triple-excitation contributions, IP-qUCCSD outperforms the higher-order ADC(4) method. For one-particle (1p)-dominated EA calculations, all tested methods exhibit comparable accuracy.

Planetary primary producers use stellar or internally generated free energy to maintain biomass and produce new material. We formulate the associated productivity ceiling while separating environmental losses, basal maintenance, retained biomass energy and net dissipation from heritable copying. Maintenance copy-reset cycles and copies retained in new biomass have distinct thermodynamic costs. For a specified feasible producer population, the normalized ceiling is $(1-x_0)/(1+q)$, where $x_0$ is the baseline copying fraction and $q$ is the growth-associated copying cost relative to other production costs. Exact interval bounds and global sensitivity analyses quantify both effects without assigning empirical probabilities to unmeasured biological parameters. Observation-constrained planetary resources and strain-matched genome and cellular-carbon measurements provide separate physical and biological benchmarks. For the two mean inventories, a 10% growth-copying correction requires an effective copying cost $e_g\chi$ about 63–84 times a published DNA fuel benchmark at unit production efficiency; the benchmark is not a measured dissipation cost. We also show that partitioning an already measured total maintenance budget cannot change the absolute productivity ceiling, and that population adjustment can preserve information-load fractions as supply falls. The framework provides conditional feasibility tests, inverse thresholds and measurement priorities, rather than a calibrated prediction of extraterrestrial productivity.

The explosive growth of artificial intelligence, cloud computing, and large-scale machine learning is driving an urgent demand for short-reach optical interconnects featuring large bandwidth, low power consumption, high integration density, and low cost preferably adopting complementary metal-oxide-semiconductor (CMOS) processes. Heterogeneous integration of silicon photonics and thin-film lithium niobate (TFLN) combines the advantages of both platforms, and enables co-integration of high-performance modulators, photodetectors, and passive photonic components, offering an ideal route to meet these requirements. However, process incompatibilities have constrained the direct integration of TFLN with only passive silicon photonics. Here, we demonstrate the first heterogeneous back-end-of-line integration of TFLN with a full-functional and active silicon photonics platform via trench-based die-to-wafer bonding. This technology introduces TFLN after completing the full CMOS compatible processes for silicon photonics. Si/SiN passive components including low-loss fiber interfaces, 56-GHz Ge photodetectors, 100-GHz TFLN modulators, and multilayer metallization are integrated on a single silicon chip with efficient inter-layer and inter-material optical coupling. The integrated on-chip optical links exhibit greater than 60 GHz electrical-to-electrical bandwidth and support 128-GBaud OOK and 100-GBaud PAM4 transmission below forward error-correction thresholds, establishing a scalable platform for energy-efficient, high-capacity photonic systems.

We study the dynamics and clustering of dust particles with inertia in shock-dominated compressible turbulence using the two-dimensional, stochastically forced Burgers equation. At small Stokes numbers, shock trapping leads to extreme density inhomogeneities and nearly singular aggregation, with correlation dimensions approaching zero. With increasing inertia, particles undergo inertial escape and intermittently cross shock fronts, producing a sharp crossover from shock-dominated trapping to quasi-ballistic dynamics. This crossover is accompanied by a pronounced reduction in density fluctuations, a continuous increase of the correlation dimension from zero to the embedding dimension, and a power-law dependence of density fluctuations on the Stokes number over an extended intermediate regime. In this regime, particle distributions show scale-free coarse-grained density statistics arising from repeated trap–escape dynamics. This behaviour is qualitatively distinct from inertial-particle clustering in incompressible turbulence and is directly relevant to dust concentration in shock-rich regions of protoplanetary discs and other compressible astrophysical environments.

Precise modeling of extended sources is a central challenge in modern optical engineering, laser physics, and computational lithography. Unlike ideal point sources or completely incoherent thermal radiation sources, real-world light sources – such as high-power laser diode arrays, superluminescent diodes (SLD), extreme ultraviolet (EUV) lithography sources, and beams transmitted through atmospheric turbulence – typically exhibit partial spatial coherence. Traditional geometric optics based on ray tracing ignores diffraction and interference effects; while classical wave optics is accurate, the computational cost of handling four-dimensional correlation functions for partially coherent fields is enormous. To balance computational efficiency and physical accuracy, phase space optics provides a unified theoretical framework. By introducing the Wigner distribution function (WDF), we can map the light field into a joint space-time-spatial frequency domain $(\bm{r}, \bm{p})$. This description not only retains all the information of wave optics (including interference terms) but also naturally transitions to the ray description of Hamiltonian optics in the short-wavelength limit, governed by Liouville's theorem of phase space volume conservation. This report aims to establish optimal modeling methods based on phase space and Hamiltonian optics for different types of extended sources such as partially coherent light, fully coherent light, and quasi-homogeneous light. The report will derive in detail the mathematical models for each source type and provide strict criteria for the applicability of geometric optics models using mathematical tools such as the Moyal expansion and generalized Fresnel number.

Micro-pattern gas detectors (MPGDs) are widely utilized in physics experiments owing to their excellent spatial resolution and high-rate capabilities. Within the PandaX-III experiment, which aims to investigate neutrinoless double beta decay, Micromegas detectors serve as charge readout devices. High energy resolution is a critical requirement for the readout plane in this context, and gain uniformity significantly impacts the achievable resolution, primarily because of the extended tracks of primary ionization electrons. However, scaling up MPGDs to larger active areas exacerbates the challenge of maintaining gain uniformity, and effectively controlling the uniformity of the avalanche gap is a key factor in the detector manufacturing process via the thermal bonding method. This study demonstrates that optimizing the thermal bonding films specifically at the detector edges can effectively improve the gain uniformity, achieving a gain uniformity of < 5% over the entire 200*200 mm2 active area in a 1 bar Ar/isobutane (96.5/3.5) gas mixture. Additionally, the gain uniformity of approximately 14% was characterized at high pressures of up to 10 bar, revealing promising potential for high resolution measurements in the PandaX-III experiment and other high-pressure applications.

We studied the structural alterations between healthy and diseased brain tissues using a multiparametric framework combining fractal analysis, fractal functional transformation, multifractal analysis, and the Inverse Participation Ratio (IPR) analysis. Accurate characterization of brain tissue microstructure is crucial for early detection and diagnosis of cancer. By applying box-counting methods on brightfield microscopy images, we estimated the fractal dimension (Df) and its logarithmic (ln(Df)) and functional (ln(Dtf)) forms to highlight spatial irregularities in the tissue architecture. While Df and ln(Df) exhibited long-tailed distributions distinguishing healthy from cancer tissues, ln(Dtf) provided significantly improved differentiation by emphasizing local structural variations. Additionally, multifractal analysis revealed broader f({\alpha}) vs {\alpha} curves in cancerous samples, reflecting higher heterogeneity. IPR analysis based on light localization further demonstrated increased nanoscale variations in mass density, reflecting higher structural disorder in cancer tissues. Combining these complementary approaches creates a robust framework for measuring tissue complexity and holds great potential to improve microscopic diagnostic methods for brain cancer detection.

Significance: Superconducting nanowire single-photon detectors (SNSPDs; also known as SSPDs) show enormous promise for low-light biomedical imaging by offering exceptional sensitivity, picosecond timing resolution, and broad spectral coverage. Aim: This perspective evaluates the role of SNSPDs by comparing their performance with other photon-counting detectors for emerging biomedical imaging applications. Approach: We outline the need for ultrasensitive detectors for biophotonics, summarize SNSPD operating principles and compare their performance with established photon-counting devices. We highlight applications in which SNSPDs enable new imaging capabilities and discuss system-level challenges and technological developments that are critical to future applications, including clinical translation. Results: SNSPDs offer advantages in signal-to-noise ratio, temporal precision, and detection bandwidth, enabling deeper tissue imaging, high-precision fluorescence lifetime measurements, and quantum-enhanced imaging modalities. Advances in scalable arrays, cryogenic miniaturization, and improved signal collection are reducing barriers to widespread adoption. Conclusions: SNSPDs are poised to transform photon-limited biomedical imaging. As device performance and system integration continue to advance, their adoption in imaging platforms is expected to accelerate. Combining SNSPDs with advancements in the excitation pathway, such as structured-light excitation with Bessel beams, aberration correction, and wavefront shaping, shows promise for delivering unprecedented imaging capabilities and broadening both the preclinical and clinical utility of these detectors.

This article suggests that thinking about the role of reference frames can provide new insight into Extended Wigner's Friend scenarios. This involves appealing to symmetries to make a principled distinction between properties of a system which are meaningful only relative to an external reference system and properties which are meaningful without further relativization. Thus we may propose that there are always well-defined facts about what observers have observed, but there are not necessarily well-defined facts about the relations between their reference frames, so there will not always exist a joint distribution over their outcomes which can meaningfully be compared to the predictions of quantum mechanics. In addition, this approach also offers a general argument against the idea that there should be a regress of relativization.

With growing interest in space exploration, optimized airfoil design has become increasingly important. However, airfoil design in rarefied gas flows remains underexplored because solving the Boltzmann equation formulated in a six dimensional phase space is time consuming. To address this problem, a solver-in-the-loop Bayesian optimization framework for symmetric, thickness-only airfoils is developed. First, airfoils are parameterized using a class shape transformation that enforce geometric admissibility. Second, a Gaussian process expected improvement surrogate is coupled in batches to a fast converging, asymptotic preserving Boltzmann solver for sample efficient exploration. Drag minimizing airfoils are identified in a wide range of gas rarefaction. It is found that, at Mach numbers Ma=2 and 4, the streamwise force increases with the gas rarefaction and shifts from pressure dominated to shear dominated drag, while optimization reduces drag at all conditions. The benefit of optimization peaks in the weakly rarefied regime, about 30% at Ma=2 and 40 to 50% at Ma=4, and falls to a few percent in transition and free-molecular flow regimes. Drag decomposition shows that these gains come mainly from reduced pressure drag, with viscous drag almost unchanged. The optimal airfoils form a coherent rarefaction-aware family: they retain a smooth, single-peaked thickness profile, are aft-loaded at low gas rarefaction, and exhibit a forward shift of maximum thickness and thickness area toward mid-chord as gas rarefaction increases. These trends provide a physically interpretable map that narrows the design space.

Shock-boundary layer interactions (SBLI) on hypersonic cone step flows exhibit a range of intrinsic unsteady behaviors, from shear-layer oscillations to large-scale pulsations. This work investigates the unsteadiness in a cone-step geometry at Mach 6 under quiet flow conditions at different freestream Reynolds numbers using time-resolved Schlieren imaging and spectral proper orthogonal decomposition (SPOD). Experimental results are compared with high-fidelity axisymmetric and three-dimensional simulations. Results demonstrate regime transition in the parameter space, across the unsteadiness boundary, all the way from shear-layer breakdown to shock system oscillations and ultimately to large-amplitude pulsations. The dominant mode in the experiments and the simulations corresponds to a Strouhal number St ~ 0.17 for small oscillations reducing to St ~ 0.13 for large pulsations. A detailed description of the unsteady shock dynamics, the instability of the shear layer during onset of unsteadiness and an analysis of the nonlinear limit cycle is presented.

Statistically correcting measured cross sections for detector effects is an important step across many applications. In particle physics, this inverse problem is known as unfolding. In cases with complex instruments, the distortions they introduce are often known only implicitly through simulations of the detector. Modern machine learning has enabled efficient simulation-based approaches for unfolding high-dimensional data. Among these, one of the first methods successfully deployed on experimental data is the OmniFold algorithm, a classifier-based Expectation-Maximization procedure. In practice, however, the forward model is only approximately specified, and the corresponding uncertainty is encoded through nuisance parameters. Building on the well-studied OmniFold algorithm, we show how to extend machine learning-based unfolding to incorporate nuisance parameters. Our new algorithm, called Profile OmniFold, is demonstrated using a Gaussian example as well as a particle physics case study using simulated data from the CMS Experiment at the Large Hadron Collider.

Alzheimer's disease (AD) is characterized by progressive microstructural deterioration in brain tissue, yet conventional imaging and histopathology often lack the sensitivity needed to detect subtle early-stage changes. Here, we present a multiparametric framework combining fractal and multifractal analysis and their distributions to quantify structural alterations in human brain tissue affected by AD. Moreover, from the fractal and multifractal formalism, we introduced an innovative fractal functional distribution method, a novel technique that transforms fractal distribution into a Gaussian form. Statistically, these distribution parameters are easy to interpret and can distinguish between control and diseased tissues. Across samples, we identify pronounced threshold-dependent behavior of fractal and multifractal parameters, reflecting the intrinsic sparsity and heterogeneous intensity landscape of brain tissue. These threshold-sensitive signatures provide a framework for quantitative stage detection and may serve as biomarkers for early pathological transitions. In addition, we studied structural disorder and complexity using our established light localization technique, inverse participation ratio (IPR) analysis. IPR-based analysis demonstrates that increasing IPR pixel size highlights the elevation of structural alterations with disease progression. Together, these integrative analyses establish a robust, multi-scale quantitative framework for detecting microstructural alterations in AD, providing a promising foundation for early diagnosis and improved pathological assessment.

While the energy-dependent neutron diffusion equation is widely employed in nuclear engineering, its status as an approximation to the transport equation is not yet completely understood, and several different approximations are in use to determine the diffusion coefficients. Past work on the theory underlying the diffusion approximation has often made use of asymptotic arguments; in the energy-dependent case, however, papers have appeared that differ substantially in their findings. Here we present a formal asymptotic derivation of the multigroup diffusion equation which addresses these differences, along with the varying and sometimes physically stringent assumptions employed in these works. Further, we show a way to exactly invert the relationship between flux and current in the P1 approximation, giving a matricial expression for the multigroup diffusion coefficient which is formally exact, has clear physical meaning, and which can be easily computed to arbitrary precision on the basis of cross-section data already produced by lattice calculations. The resulting 2-group diffusion coefficient for an infinite medium of hydrogen is calculated with Monte Carlo, and compared to the those deriving from the Cumulative Migration Method and from the out-scatter approximation.

It is well known that the Coriolis force due to Earth's rotation can induce wind veer in the mean flow velocity of an atmospheric boundary layer (ABL), but much less is known about its effects on turbulent coherent structures. In this work, large-eddy simulation (LES) is employed to investigate the effects of the Coriolis force on the characteristics of turbulent coherent structures in the conventionally neutral atmospheric boundary layers (CNBL). Variation of the Coriolis force is realized by changing latitude or geostrophic wind speed. We found that the Coriolis force causes distinct deflection of coherent velocity structures to the geostrophic wind direction, which is not aligned with the direction of either the mean wind or the mean shear. When a relative structure deflection angle is plotted against a relative local wind veer angle, a universal relationship between them can be established regardless of latitude or geostrophic wind speed. Moreover, we also studied the effect of the Coriolis force on the inclination of large-scale turbulent structures. It is found that the inclination angle in the surface layer varies under different conditions, which is probably due to the deflection of turbulent structures caused by the Coriolis force.

Due to the computationally demanding nature of fluid-structure interaction simulations, heart valve simulation is a complex task. A simpler alternative is to model the valve as a resistive flow obstacle that can be updated dynamically without altering the mesh, but this approach can also become computationally expensive for large meshes. In this work, we present a fast method for computing the resistive flow obstacle of a heart valve. The method is based on a parametric surface model of the valve, which is defined by a set of curves. The curves are adaptively sampled to create a polyline representation, which is then used to generate the surface. The surface is represented as a set of points, allowing for efficient distance calculations to determine whether mesh nodes belong to the valve surface. We introduce three algorithms for computing these distances: minimization, sampling, and triangulation. Additionally, we implement two mesh traversal strategies: exhaustive node iteration and recursive neighbor search. The latter significantly reduces the number of distance calculations by only considering neighboring nodes. Our pipeline is demonstrated on both a previously reported aortic valve model and a newly proposed mitral valve model, highlighting its flexibility and efficiency for rapid valve shape updates in computational simulations.

Over the last three years, research on the application of renewable energies in the agricultural sector has grown significantly. In this study, we conducted a bibliometric analysis of global scientific production from 2019 to 2021 to identify trends, leading contributors, and emerging research areas. Based on 1378 documents retrieved from Scopus, we observed a clear upward trend in publications, with a peak in 2021. India, China, the United States, Italy, and the United Kingdom were the most productive countries, while key institutions from China, Iran, and the Netherlands led the research output. Our results reveal five major thematic clusters: renewable energy technologies in agriculture, bioenergy, sustainable agriculture, biomass energy, and the environmental impact of agricultural activities. Notable advances include agrovoltaic systems, the use of agricultural and livestock waste for biogas production, and the development of agricultural robots powered by renewable energy sources. Additionally, there is increasing interest in examining the links among agriculture, renewable energy use, and greenhouse gas emissions, aligned with global sustainability goals. This analysis highlights the evolution of the field, international collaboration patterns, and the most influential research lines, offering valuable insights to guide future scientific developments in integrating renewable energies within the agricultural sector.

We investigate Schrödinger and Klein-Gordon (KG) oscillators in the spacetime of a global monopole (GM) within Eddington inspired Born-Infeld (EiBI) gravity, including, in the relativistic sector, the coupling to a Wu-Yang magnetic monopole (WYMM). By reducing the radial equations to the confluent Heun form and enforcing termination of the Heun series, we obtain conditionally exact solutions in which the radial eigenfunctions truncate to polynomials of degree $(n+1)\geq 1$. This truncation imposes algebraic constraints that quantize the oscillator frequency and restrict the values allowed for the orbital angular momenta $\ell$. In the lowest nontrivial case $n=0$, the degree-one Heun polynomial yields a closed analytic expression for the frequency and determines a finite upper bound on $\ell$, dictated jointly by the EiBI deformation and the GM deficit. The resulting parametric correlations reveal a sharp geometric control of the spectrum: EiBI nonlinearities and the angular deficit fix the admissible bound states through polynomial truncation conditions. The confluent Heun correspondence is made explicit, providing a rigorous and reproducible framework for extracting analytical solutions from otherwise non-polynomial Heun structures. Applying the same method to the KG oscillator with a WYMM, we derive conditionally exact particle and antiparticle energies in a closed form. The relativistic spectrum exhibits perfect charge symmetry and a precise dependence on the WYMM strength, the EiBI parameter and the angular momentum constraint. To the best of our knowledge, this constitutes the first unified and fully consistent treatment of conditionally exact Schrödinger and Klein-Gordon oscillators in EiBI gravity based on a degree-one confluent Heun polynomial.

We investigate the role of symmetry, exact coherent structures (ECSs), and their invariant manifolds in organizing flow reversals and turbulent dynamics in channel-confined two-dimensional active nematics. In compact periodic channels, we use equivariant bifurcation theory to trace the origin of dynamically relevant ECSs through symmetry-constrained local and global bifurcations. At low activity, we identify periodic and relative-periodic families created through local and global bifurcations. The invariant manifolds of these ECSs organize potential heteroclinic pathways between left- and right-flowing nearly uniaxial states. These give rise to several symmetry-dictated reversal mechanisms, with and without vortex-lattice intermediate states. In the high activity turbulent regime, this ECS skeleton persists as a dynamical scaffold for the chaotic attractor. We use symmetry signatures to trace the origin of ECSs embedded in turbulence to their preturbulent branches. We find evidence that typical turbulent trajectories repeatedly shadow these ECSs and their unstable manifolds, resulting in near-heteroclinic transitions and persistent two-way reversals. Our results reinforce the view that active nematic turbulence is organized by a low-dimensional, symmetry-governed network of invariant solutions and their manifolds, and identify the dynamical mechanisms underlying spontaneous flow reversals.

The fine-structure constant alpha approximately 1/137 is traditionally regarded as a fundamental dimensionless parameter. I argue instead that alpha is a scaled quantity that arises only where the structural scales contributed by classical electromagnetism (e), quantum mechanics (h-bar), and special relativity (c) intersect. None of these theories, taken individually, supplies the independent scales required to define alpha. The constant first appears when relativistic corrections are added to the Schrodinger-Bohr description of hydrogen (Sommerfeld), and it becomes the structural coupling in quantum electrodynamics, where quantum and relativistic effects modify the classical electromagnetic interaction. Expressing the governing laws in canonical form reveals this dependence and eliminates representational artifacts that make alpha appear fundamental. The running of alpha in QED further demonstrates its status as a scale-dependent coupling rather than a universal constant. I conclude that alpha is a domain-specific structural ratio reflecting contingent relationships among independent physical scales.

We measure the distribution of energy deposited in a 40x88 mm plastic scintillator by e+ e- pair production of 4.44 MeV gamma-rays. We observe the double-escape peak of 3.42 MeV from pair production by tagging 511 keV annihilation radiation in two high-Z scintillators. The source is a standard commercial neutron source using alpha-emitting 241Am encapsulated with 9Be, which has a reaction branch feeding the first Ipi=2+ state of 12C making the 4.44 MeV gamma-rays. We demonstrate the extraction of the double-escape peak from the large neutron-produced backgound, and explore some of the features and difficulties of this technique with our apparatus.

Haptic interfaces play a critical role in medical teleoperation by enabling surgeons to interact with remote environments through realistic force and motion feedback. Achieving high fidelity in such systems requires balancing the trade-offs among workspace, dexterity, stiffness, inertia, and bandwidth, particularly in applications demanding pure rotational motion. This paper presents the design methodology and kinematic analysis of a Cable-Driven Coaxial Spherical Parallel Mechanism (CDC-SPM) developed to address these challenges. The proposed approach focuses on the mechanical design and parametric synthesis of the mechanism to meet task-specific requirements in medical applications. In particular, the design enables the relocation of the center of rotation to an external point corresponding to the tool-tissue interaction, while ensuring appropriate workspace coverage and collision avoidance. The proposed cable-driven interface design allows for reducing the mass placed at the robot arm end-effector, thereby minimizing inertial loads, enhancing stiffness, and improving dynamic responsiveness. Through parallel and coaxial actuation, the mechanism achieves decoupled rotational degrees of freedom with isotropic force and torque transmission. A prototype is developed to validate the mechanical feasibility and kinematic behavior of the proposed mechanism. These results demonstrate the suitability of the proposed mechanism design for future integration into haptic interfaces for medical applications such as ultrasound imaging.

Experts in quantum field theory (QFT) generally answer the question of the “size of an electron” with “point-like”. On the other hand, QFT recognizes quantum effects, shielding by virtual particles, the so-called polarization cloud, which should describe the size of physical electrons. Scattering experiments with electrons, such as those carried out in high-energy experiments at particle accelerators, should be able to clarify whether physical electrons are really point-like, as claimed by experts and in textbooks. In this article, I show that both the formulas of QFT and the corresponding cross sections are consistent with an extent of the electron of the size of the classical electron radius. The assumption that the relativistic energy of electrons in the high-energy limit consists solely of deformation energy from the extended electron density distribution allows for a simple interpretation of the experimental cross sections. For this reason, I refer to classical models in 1+1 and 3+1 dimensions that have precisely this property. The difference between the terms point-like, structureless, and substructureless is highlighted. The usual objections to the claim that the electron radius is finite and has already been measured in electron scattering experiments are discussed.

Approach to the thermodynamic limit of a non-relativistic ideal gas in a periodic box is investigated. The single particle wave function obeys twisted boundary condition, $\psi(L)=e^{i\theta}\psi(0)$ for which the free particle spectrum is constructed in terms of the twist angle, $\theta$. The exact density of states is utilized to construct finite-size corrections of thermodynamic observables. Leading finite volume corrections in the free energy do not arise due to the boundary – its implication for mixing entropy is examined. Finite volume corrections to the average energy, its fluctuations and the pressure are also examined with corrections arising exclusively through the boundary condition. However, the equation of state, the ratio of pressure to energy density, remains unmodified by the boundary.

Layered perovskite lithium yttrium titanate ($\rm LiYTiO_4$) has recently emerged as a promising low-potential, ultrahigh-rate intercalation-type anode material for lithium-ion batteries; however, its lattice dynamics and thermal transport properties remain poorly understood, limiting a complete evaluation of its practical potential. Here, we combine experimental measurements with theoretical modeling to systematically investigate the anharmonic lattice dynamics and heat transport in $\rm LiYTiO_4$. We employ a neural evolution potential (NEP)-based framework that integrates the temperature-dependent effective potential method with the Wigner thermal transport (WTT) formalism, explicitly including both diagonal and off-diagonal terms of the heat-flux operator. Zero-temperature phonon calculations reveal dynamical instabilities associated with $\rm TiO_6$ octahedral rotation, which are stabilized at finite temperatures through anharmonic renormalization. Using the WTT approach with contributions from phonon propagation and coherence contributions, we predict a room-temperature lattice thermal conductivity ($\kappa_{\rm L}$) of 3.8 $\rm Wm^{-1}K^{-1}$ averaged over all crystal orientations, in close agreement with the measured value of 3.2 \pm 0.08 $\rm Wm^{-1}K^{-1}$ for polycrystalline samples. To further examine the possible influence of ionic motion on high-temperature thermal transport, we compute $\kappa_{\rm L}$ using a Green-Kubo equilibrium molecular dynamics approach based on the same NEP, which yields consistent results with both experiment and WTT predictions, confirming the negligible role of Li-ion mobility in heat conduction. Our study not only identifies the ultralow thermal conductivity of $\rm LiYTiO_4$ as a key limitation for its practical application but also establishes a reliable computational framework for studying thermal properties in battery materials.

The challenge of imbalanced regression arises when standard Empirical Risk Minimization (ERM) biases models toward high-frequency regions of the data distribution, causing severe degradation on rare but high-impact “tail” events. Existing strategies uch as loss re-weighting or synthetic over-sampling often introduce noise, distort the underlying distribution, or add substantial algorithmic complexity. We introduce PARIS (Pruning Algorithm via the Representer theorem for Imbalanced Scenarios), a principled framework that mitigates imbalance by optimizing the training set itself. PARIS leverages the representer theorem for neural networks to compute a closed-form representer deletion residual, which quantifies the exact change in validation loss caused by removing a single training point without retraining. Combined with an efficient Cholesky rank-one downdating scheme, PARIS performs fast, iterative pruning that eliminates uninformative or performance-degrading samples. We use a real-world space weather example, where PARIS reduces the training set by up to 75% while preserving or improving overall RMSE, outperforming re-weighting, synthetic oversampling, and boosting baselines. Our results demonstrate that representer-guided dataset pruning is a powerful, interpretable, and computationally efficient approach to rare-event regression.

We study energy minimization problems in quantum chemistry through the lens of computational algebraic geometry. We focus on minimizing the Rayleigh quotient of a Hamiltonian over a tensor train variety. The complex critical points of this problem approximate eigenstates of the quantum system, with the global minimum approximating the ground state. We call the number of critical points the Rayleigh-Ritz degree. We first study the Rayleigh-Ritz degree and introduce the Rayleigh-Ritz discriminant, which describes Hamiltonians that lead to a deficient number of critical points. We then specialize this framework to tensor train varieties: we identify instances when they are Segre products of projective spaces, report what we know about their defining ideals, and present a birational parametrization from products of Grassmannians. We use homotopy continuation to compute all critical points of this optimization problem over various tensor train and determinantal varieties. Finally, we use these results to benchmark state-of-the-art methods, the Alternating Linear Scheme and Density Matrix Renormalization Group.

Accurate prediction of liquid viscosity is essential for process design and simulation, yet remains challenging for novel molecules. Conventional group-contribution models struggle with isomer discrimination, large molecules, and parameter availability, while purely data-driven graph neural networks (GNNs) demand large datasets and offer limited interpretability. Even when feasible to be applied, purely data-driven models lack thermodynamic consistency in their predictions and are not a reliable solution. This work introduces ExPUFFIN, an extended version of the Path-unifying Feed-Forward Interfaced Network, consisting of a hybrid GNN-based framework that directly predicts temperature-dependent viscosities of pure hydrocarbons from molecular graphs, while enforcing mechanistic inductive biases in the output layer to ensure thermodynamic consistency. Molecular information is given as graph structures, encoded as a graph convolutional network, and mapped to an inductive bias neuron based on two thermophysical correlations: a three-parameter Andrade-type equation and a four-parameter empirical viscosity-temperature relation. The accuracy of these models is compared with a solely data-driven prediction. The Andrade-based ExPUFFIN variant reduces RMSE compared to the purely data-driven baseline of 37 percent and yields smooth, physically consistent interpolation and extrapolation of viscosity-temperature curves, properties that are not observed in purely data-driven models. The empirical ExPUFFIN model provides comparable accuracy while retaining robust trends. Overall, embedding physics-based structure in GNN outputs improves accuracy, robustness, and transferability, enabling reliable viscosity predictions for complex hydrocarbon molecules. The approach is readily extendable to other properties and significantly broader chemical domains.

Amid the growth of social media, students increasingly turn to short-form platforms for study support and advice. This study examines how A-level Physics is portrayed by student creators on TikTok, a popular platform amongst students for peer-to-peer educational guidance, information sharing, and collective knowledge building. Using reflexive thematic analysis of 57 TikTok videos via #alevelphysics, we explore how A-level Physics is characterised and what pathways to success are constructed within these informal discourses. The findings show that the subject is depicted as both intellectually demanding and emotionally taxing, with the examination system perceived as a flawed gatekeeper. Creators narrate their experiences through a blend of hyperboles, frustration, and resilience, offering sophisticated peer-generated pedagogies alongside cautionary tales. Recommendations emphasise strategic resource curation, sustained practice, systematic memorisation, and a mindset of deliberate over-preparation. These portrayals of subject demands and agentic responses resonate with prior research on the cultural construction of physics as hard and exclusionary, while also illustrating how affinity spaces on social media enable students to reframe difficulty through shared feelings and tactics. We discuss the value of student-led online discourses about high-stakes examinations for educators in supporting prospective and current physics students.