Papers

We present numerical evidence that Kelvin waves (KWs) on quantized vortices in superfluid helium can be directly observed in the normal fluid component at finite temperatures. Using the Fully cOUpled loCAl model of sUperfLuid Turbulence (FOUCAULT) model, we analyze the propagation and temperature dependence of KWs by simultaneously measuring the dispersion of waves on the vortex displacement and the normal fluid velocity. The results demonstrate that the normal fluid supports a coherent KW-like response, with a dispersion relation matching that of the vortex filament (VF). Unlike the Schwarz model where there is almost no temperature dependence, in FOUCAULT KWs frequency and damping both depend on temperature, highlighting the role of mutual friction in mediating the coupling between the two fluids. These findings open a pathway for experimental observation of KWs in the normal phase using tracer based visualization.

Water shows numerous thermodynamic, dynamic, and structural anomalies. Recent experiments [Eichler et al. Phys. Rev. Lett. 134, 134101 (2025)], based on measurements of shear and bulk viscosities of liquid water up to 1.6 GPa, have reported the existence of a minimum in the variation of the structural relaxation time {\tau}{\alpha} with pressure at room temperature. Here we investigate this and related properties with molecular dynamics simulations of the TIP4P/2005 water model, performed at extreme pressures commensurate with the experiments. Specifically, we compute dynamic (self-diffusion, shear and bulk viscosities, and structural relaxation time) and structural (oxygen-oxygen radial distribution function and structure factor, translational order parameter) properties down to 220 K and up to 2.7 GPa. We find good agreement with the experimental observations, and confirm the existence of a minimum in {\tau}{\alpha} . The microscopic information obtained from the simulations suggests that this anomaly is connected with the sudden reorganization of the hydrogen bond network induced by pressurization.

Active matter often simultaneously exhibits different kinds of orientational order and, in many cases of biological interest, undergoes continuous material renewal. In renewing nematopolar fluids we find stable topological strings, structures consisting of two nematic point defects connected by a defect line in the polar field. We identify the mechanism underlying string stabilization and unveil how string length is determined. In the presence of active stress, we observe active-string chaos. Our work identifies continuous material renewal as a generic mechanism underlying the stabilization of topological defect structures in systems with mixed order parameters. It could be used for orchestrating living matter during development and other biological processes.

Optically transparent thermal loss mitigators have recently seen renewed research interests owing to their increasing relevance in the realms ranging from smart windows, efficient greenhouse designs and high-performance-low-cost solar thermal systems. In depth understanding of the heat transport mechanisms and their quantification is crucial for building efficient opto-thermal management strategies for optimization of the aforementioned systems. The present work serves to identify and quantify the key heat transfer mechanisms operative in a host of optically transparent thermal loss mitigators. In particular, comprehensive experimental modelling frameworks have been developed to investigate the efficacy of carbon dioxide gas (CO2), air, vacuum (0.07mbar), transparent heat mirrors (Indium tin oxide coated glass) and aerogels (silica-based) in mitigating thermal losses. Detailed and careful experimental modelling reveals that it is imperative to employ more than one thermal loss mitigator and choose correct absorber surface orientation (relative to the irradiation direction) to maximize thermal loss mitigation. Magnitude of absorber surface stagnation temperature has been employed as the figure of merit to quantitatively compare various optically transparent thermal loss mitigators. Under un-evacuated conditions, CO2 has emerged as potent alternative to more sophisticated optically transparent thermal loss mitigators like aerogels and transparent heat mirrors. Enhancements (relative to air) on the order of 2%-7%, 46%-84%, 57%-84% and 66%-86% are observed in case of CO2, vacuum, transparent heat mirrors (vacuum) and aerogel (vacuum) respectively.

Electrochemical devices (batteries, fuel cells, and electrolyzers) are in full development, driven by the green energy transition. Their real-time control requires ms predictions in order to take critical decisions during fast transients or faults. The physics behind include coupled multi-physics phenomena that conventional finite element methods cannot solve so fast with the current CPU technology. This paper evaluates the potential of physics-informed machine learning represented by three frameworks: \ac{pinn}, \ac{pideeponet}, and \ac{pino} by evaluating their training effort, inference speed, and extrapolation capacity. Our analysis reveals valuable performance trade-offs. \acp{pinn} offer simplicity for fixed problem instances but require retraining for parameter changes. \ac{pideeponet} enables operator learning across varying conditions with mesh-free geometric flexibility. \ac{pino} delivers superior performance on regular grids, with the strongest extrapolation capabilities due to spectral derivative computation and resolution invariance. \ac{pideeponet} is particularly suited for irregular, unstructured geometries (e.g., porous electrodes or complex flow fields), while \ac{pino} works best for layered, structured-grid problems (e.g., transport across stacked electrochemical layers) requiring fast inference. Possible future applications include real-time lithium concentration prediction for safe fast-charging and micro short circuit detection, water management in fuel cells, and optimal power management in electrolyzers under intermittent renewable inputs. These findings establish physics-informed operator learning as a transformative approach for next-generation electrochemical device controller technology.

Non-destructive beam diagnostics are essential for low-energy medical cyclotrons, where even thin interceptive devices can severely degrade beam quality. We investigate an external fiber monitor (EFM) based on Ce-doped silica scintillating fibers that detects secondary radiation generated at existing beamline components of the 18 MeV Bern Medical Cyclotron beam transfer line (BTL). Three use cases were studied: (i) beam intensity monitoring around an electrically isolated, water-cooled beam dump; (ii) beam-loss monitoring around a 10 mm collimator under varying the beam focusing; and (iii) by steering a 6.5 mm $\times$ 6.5 mm beam spot on a beam dump. For case (i), the summed EFM signal exhibits a linear dependence on the current on target over nearly three orders of magnitude. In case (ii), a normalized EFM-based beam-loss proxy scales monotonically with an electrical loss proxy across several focusing settings. Furthermore, opposing-fiber signal ratios provide decoupled, monotonic sensitivity to horizontal and vertical beam displacements.

This study proposes the topology optimization method for moving rigid bodies subjected to forces from fluid flow, such as sails and turbines, with an unsteady time-dependent formulation. Unlike existing topology optimization frameworks in which rigid-body motion drives the flow, which is referred to as $\textit{active}$, the present study considers rigid-body motion induced by fluid forces, i.e., $\textit{passive}$. The equations of motion governing the rigid-body dynamics are solved in a coupled manner with the continuity equation and the momentum conservation equations. The rigid body is represented on a design grid that is separated from the analysis grid on which the state and adjoint fields are defined. After updating the rigid body motion, the body is mapped onto the analysis grid. The fluid equations are solved using the lattice kinetic scheme, an extended version of the lattice Boltzmann method, owing to its suitability for unsteady flows. Design sensitivities based on the adjoint variable method are presented and applied to two- and three-dimensional problems involving translational and rotational motions. The optimized shapes for each problem are discussed from a physical perspective and compared with a reference shape or their binarized counterparts, providing insights into the effectiveness of the proposed method as well as its limitations.

To address the difficulty of characterizing the surface layer rigorously, especially the thickness and refractive index (RI) in surface plasmon resonance (SPR) technology, we propose a field-weighted analysis method. This approach enables simultaneous quantitative determination of RI for the bulk solution and the surface layer. This study utilizes the aluminum-based Kretschmann structure with the intensity interrogation technique. We construct the field-weighted model governed by the evanescent field penetration depth to decompose the SPR reflected intensity into the bulk and surface responses. Experiments are conducted using bovine serum albumin (BSA) solution to form a surface adsorbed protein layer, and different concentrations of BSA are tested. Results show that the separated surface response fits well with the Langmuir formula, representing a significant improvement over the untreated SPR signal. The bulk and surface responses are then incorporated into the field-weighted model to determine the RI values of the bulk BSA solution and the surface adsorbed BSA layer at various concentrations. The experimental results of BSA solution match the Abbe refractometer measurements with a maximum error 0.0004 in RI, while the results of the adsorbed BSA layer, both the RI and thickness, aligned well with reported parameters for a single BSA layer. This method eliminates the stage rotation in the common angular interrogation SPR technique and complicated optical design and nano-fabrication in the nano-optics sensing schemes, making it suitable for compact, low-cost SPR platforms for practical applications needing surface layer characterization.

Dielectric metasurfaces provide a unique platform for efficient harmonic generation and optical wavefront manipulation at the nanoscale. While several approaches are available for performing wavefront shaping, the one exploiting geometric phase streamlines significantly the design and fabrication process. It has been recently shown that, in III-V semiconductor alloys, the rotation of the crystal axes affects the phase and amplitude of second-harmonic generation (SHG) induced by circularly polarized light [1]. Based on this notion, we fabricated and characterized two aluminum gallium arsenide metasurfaces displaying the versatility of the geometric phase design approach through nonlinear beam steering and structured-light generation on the harmonic field.

In neural network surrogate solvers for electromagnetic simulations, accurately modeling resonant phenomena remains a central challenge. High-amplitude resonances generate strongly localized field patterns that deviate significantly from the general distribution of non-resonant cases, leading to instability and degraded predictive performance. To address this, we introduce dissipative relaxation transfer learning (DIRTL), a data-efficient training framework that integrates transfer learning with loss-regularized optimization principles from high-Q photonics. DIRTL first pretrains the model on data generated with a small fictitious material loss, which broadens sharp resonant modes and suppresses extreme field amplitudes. This smoothing of the response landscape enables the model to learn global modal features more effectively. The pretrained model is subsequently fine-tuned on the target lossless dataset containing true high-amplitude resonances, allowing stable adaptation based on the pretrained representation. Applied to both the Fourier Neural Operator (FNO) and UNet architectures, DIRTL yields substantial improvements in prediction accuracy, including up to a two-fold error reduction for the FNO variant. Furthermore, DIRTL demonstrates robustness across diverse training conditions and supports multi-tasking performance, suggesting the generalizability and flexibility of the pretrained core. Altogether, these results position DIRTL as a physically grounded curriculum for improving the reliability of neural network surrogate solvers.

The Toner-Tu-Swift-Hohenberg (TTSH) equations are one of the basic equations that are used to model turbulent behaviour in active matter, specifically the swarming of bacteria in suspension. They combine features of the incompressible Navier-Stokes, the Toner-Tu and Swift-Hohenberg equations, together with the important properties that they are linearly driven, and that the Laplacian diffusion is taken to be negative in combination with hyper-dissipation. We prove that the TTSH equations possess a finite-dimensional compact global attractor on the periodic domain $\mathbb{T}^d$ ($d=2,3$) and we establish explicit estimates for its Lyapunov dimension which agree with the heuristic prediction based on the Swift-Hohenberg length scale. The predominance of this length scale (as a vortex length scale) has been observed in both numerical and experimental studies of bacterial turbulence, so our methods and results provide a rigorous theoretical foundation for this phenomenon. We also carry out pseudospectral direct numerical simulations of these PDEs in dimension $d=2$ through which we obtain Lyapunov spectra for representative parameter values. We show that our numerical results are consistent with the analytically derived rigorous bounds.

Accurate assessment of human epidermal growth factor receptor 2 (HER2) expression is critical for breast cancer diagnosis, prognosis, and therapy selection; yet, most existing digital HER2 scoring methods rely on bulky and expensive optical systems. Here, we present a compact and cost-effective lensfree holography platform integrated with deep learning for automated HER2 scoring of immunohistochemically stained breast tissue sections. The system captures lensfree diffraction patterns of stained HER2 tissue sections under RGB laser illumination and acquires complex field information over a sample area of ~1,250 mm^2 at an effective throughput of ~84 mm^2 per minute. To enhance diagnostic reliability, we incorporated an uncertainty quantification strategy based on Bayesian Monte Carlo dropout, which provides autonomous uncertainty estimates for each prediction and supports reliable, robust HER2 scoring, with an overall correction rate of 30.4%. Using a blinded test set of 412 unique tissue samples, our approach achieved a testing accuracy of 84.9% for 4-class (0, 1+, 2+, 3+) HER2 classification and 94.8% for binary (0/1+ vs. 2+/3+) HER2 scoring with uncertainty quantification. Overall, this lensfree holography approach provides a practical pathway toward portable, high-throughput, and cost-effective HER2 scoring, particularly suited for resource-limited settings, where traditional digital pathology infrastructure is unavailable.

This paper addresses a critical gap in hydroacoustics through a systematic wind tunnel investigation of wall-pressure fluctuations on the fully appended DARPA SUBOFF model at operationally relevant Reynolds numbers ranging from $5.6 \times 10^{6}$ to $1.4 \times 10^{7}$. The experimental campaign encompasses baseline straight-ahead flow, complex maneuvering (yaw and pitch) conditions, and a first-of-its-kind assessment of a novel vortex control baffle (VCB). To ensure benchmark-quality spectral data, rigorous signal processing techniques were applied, specifically Wiener filtering for background noise suppression and dynamic transfer function correction for pinhole sensors. Key findings indicate that while spectral self-similarity holds across Reynolds numbers, the primary finding is the critical role of appendages in noise amplification. Unstable horseshoe vortex dynamics at the sail-hull junction drive localized pressure fluctuations of up to 300%, establishing this feature as a major coherent noise source. To address this, the study provides the pioneering experimental validation of the VCB. By physically suppressing horseshoe vortex formation at the sail-hull junction, the VCB achieves a global stabilization of the downstream flow, resulting in a significant 35% reduction in root-mean-square wall-pressure fluctuations at the stern and an approximately 14% reduction along the parallel mid-body. Furthermore, maneuvering conditions are shown to fundamentally reshape the pressure field, introducing substantial crossflow effects and non-monotonic spectral behaviors. This comprehensive dataset and the demonstrated efficacy of the VCB provide essential physical insights and a critical validation benchmark for the design of next-generation quiet submarines.

While geostrophy remains the simplest and most practical balance to extract velocity information from sea-surface height anomaly (SSHa), confusions remain within the oceanographic community to what extent this balance can be applied to altimetric observations with the launch of the Surface Water and Ocean Topography (SWOT) satellite. Given the limited temporal resolution of SWOT, many studies have resorted to claiming that the spatially filtered SSHa fields correspond to the geostrophic component. This introduces the ambiguity of which spatial scale to choose. Here, we build upon the recent developments in internal tide (IT) corrections (Yadidya et al., 2025) and apply a dynamic mode decomposition (DMD)-based method introduced by Lapo et al. (2025) to robustly extract the geostrophic component associated with sub-inertial frequencies from the SWOT one-day-repeat orbit; we distribute the global dataset as a public good. We provide the joint probability density function (PDF) of vorticity and strain, and spectra of SSHa at a few cross-over regions.

Soft interfaces formed by lipid membranes are fundamental to living cells, synthetic cells, and membrane-based soft materials. However, a quantitative framework linking molecular organization with nonlinear interfacial mechanics remains elusive. Here, we establish an analytical framework that captures the nonlinear elastic response of lipid-membrane-coated synthetic cells under micropipette aspiration. Incorporating both area stretching and curvature bending enables the model to quantitatively reproduce the complete pressure-displacement response within the small-deformation regime. This approach reduces interfacial mechanics to two parameters: the in-plane area-stretching modulus and an out-of-plane bending-related term. Using this unified framework, we experimentally demonstrate that nonlinear interfacial mechanics can be programmed by altering the molecular geometry and effective dimensionality of adsorbed elements. The lipid molecular shape and curvature-dependent packing regulate in-plane stiffness, while DNA nanostructures, the other adsorbed element, introduce an orthogonal control axis via dimensionality: isolated motifs primarily enhance area stretching, whereas three-dimensional network architectures markedly reinforce bending resistance. Together, these results establish a general molecular design principle for programming interfacial mechanics and provide a quantitative foundation for engineering mechanically tunable synthetic cells and soft interfaces.

Separating turbulent fluctuations from coherent large-scale background flows is a longstanding challenge in the analysis of numerical simulations and astronomical observations. Traditional approaches commonly rely on decomposition-based techniques such as Fourier or wavelet filtering, which assume that a meaningful separation can be achieved through scale selection. In realistic flows, however, coherent motions and turbulence often overlap across a broad range of scales and interact nonlinearly, making a unique separation inherently ambiguous. In this work, we investigate the robustness of an AI-based turbulence-background separation approach using two-dimensional incompressible Navier-Stokes simulations of decaying hydrodynamic turbulence. The simulations are initialized with a coherent background flow and divergence-free turbulent perturbations with a Kolmogorov-like spectrum and evolve without external forcing, providing a controlled physical testbed. A neural network trained exclusively on static synthetic images is applied to simulation snapshots at different evolutionary stages. The model recovers turbulent fluctuations during early and intermediate stages when partial scale separation is present. At later stages, where nonlinear interactions increasingly mix coherent and turbulent structures, the separation becomes less distinct; nevertheless, the recovered fields remain visually and spectrally consistent with the expected turbulent behavior. Quantitative comparisons with a Fourier filtering baseline show that the AI-based approach achieves comparable reconstruction accuracy while not requiring an explicit spectral cutoff scale. These results suggest that AI models trained on static data can provide a flexible diagnostic tool for turbulence-background separation in time-evolving flows, with potential applications to astrophysical datasets.

3D kinetic particle-in-cell (PIC) simulations are performed using the kinetic Alfvén wave (KAW) eigenvector relations from a two-fluid model as initial conditions, in order to study turbulent fluctuations and intermittent structures at sub-ion and electron scales. Simulations with different ion-to-electron mass ratios are set up to investigate the role of electron scales in the formation of intermittent structures. We analyze the current sheet structures that develop in these simulations. Two algorithms, namely Breadth-First Search (BFS) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN), are employed to determine the thickness, length, and width of the current sheets, and both methods are found to yield consistent results. The average current sheet thickness scales inversely with the square root of the ion-to-electron mass ratio, with values close to the electron skin depth ($d_e$), indicating the presence of electron-scale current sheets in the simulations. The widths and lengths of the current sheets show a weaker scaling with the mass ratio. The scale-dependent kurtosis reveals enhanced intermittency at electron scales, consistent with magnetosheath observations. Distributions of scale-dependent properties of the current sheets also align with the electron skin depth of the different simulations and they lie within ranges observed in kinetic scale solar wind turbulence. This study reveals the nature of sub-ion-scale current sheets in KAW turbulence and their role in dissipation.

Large-scale neutrino observatories operate under unavoidable detector deadtime arising from photomultiplier saturation, digitizer limits, and front-end readout constraints. Conventional coincidence-based trigger logic implicitly assumes continuous sensor availability and therefore suffers systematic efficiency loss when channels become temporarily non-live. This work presents the design of a liveness-aware trigger architecture targeting low-latency FPGA deployment in distributed optical arrays. We introduce a recursive Infinite Impulse Response (IIR) update law implemented as a fully synthesizable pipeline that constructs a continuity-preserving effective observable at each sensor node. Rather than collapsing during non-liveness intervals, the observable decays smoothly while retaining phase and amplitude information relevant for network-level coherence estimation. By explicitly separating continuous measurement construction from discrete trigger decision logic, the proposed architecture enables graceful degradation under partial channel non-liveness. Simulation results demonstrate sustained event recovery efficiency in regimes of elevated deadtime probability, where conventional coincidence logic degrades substantially.

A kind of asymmetric transmission (AT) device based on bilayer silicon arrays (BSA) nanostructure is theoretically explored, which achieves high forward transmissivity and suppressed backward transmissivity for broadband by simply adjusting the parameters of the structure. The structure consists of two silicon cylinder arrays, one on the SiO2 substrate and the other embedded in the substrate. Particularly, three AT devices with different configurations are designed, which exhibit broadband AT with high isolation ratios in the wavelength ranges of 685-807 nm, 866-1029 nm, and 1285-1536 nm, respectively. A comprehensive analysis of the BSA structure's performance across different array periods highlights its potential for broadband optical applications, such as optical isolation and multi-channel optical sensors.

Although 1-alkanols are widely used as anesthetics and membrane-active agents, the molecular basis of their chain-length-dependent cutoff behavior remains unclear. Here, we perform extensive atomistic molecular dynamics simulations to investigate the partitioning of 1-alkanols with varying chain lengths in a raft-like lipid bilayer composed of dipalmitoylphosphatidylcholine (DPPC), dioleoylphosphatidylcholine (DOPC), and cholesterol (Chol), which exhibits coexistence of liquid-ordered ($l_o$) and liquid-disordered ($l_d$) domains. We observe pronounced lateral heterogeneity in alkanol distribution, membrane thickness, number density, and lateral pressure profiles across coexisting phases. A distinct cutoff chain length, $n_{cutoff}=12$, is identified: alkanols with $n<n_{cutoff}$ preferentially partition into DOPC-rich $l_d$ domains, whereas alkanols with $n \ge n_{cutoff}$ preferentially localize within DPPC- and cholesterol-rich $l_o$ domains. This chain-length-dependent redistribution is accompanied by systematic reductions in the lateral pressure profile, membrane compressibility, and bending rigidity of the bilayer. The results provide a detailed molecular characterization of how alkanol chain length modulates membrane structure and mechanical response in laterally heterogeneous lipid membranes.

An essential feature of canonical zero-pressure-gradient (ZPG) turbulent boundary layers (TBLs) is that the mean streamwise velocity exhibits a logarithmic dependence on the wall-normal distance, known as the log law. In this study, we demonstrate that this conventional log law is not suitable for turbulent boundary layers subjected to pressure gradients (PGs). Instead, a log–linear law is theoretically derived for TBLs under moderate adverse pressure gradients (APGs), based on the total shear-stress balance and a rescaled eddy-viscosity model. The validity of the proposed log–linear law is assessed using available datasets of incompressible APG TBLs with the Clauser pressure-gradient parameter $\beta$ ranging from 0.73 to 9.0. Compared with the conventional log law, the present log–linear formulation shows significantly improved agreement with the measured mean velocity profiles. In the limiting case of $\beta \to 0$, the proposed law naturally recovers the classical log law.

Time-varying photonic systems open new possibilities for controlling light, enabling photonic time crystals, time reflection and refraction, frequency conversion, synthetic gauge fields, optical nonreciprocity, among others. These effects emerge from the dynamic modulation of optical properties, which can mediate photonic transitions between eigenstates of different frequencies and/or wavevectors. To achieve such transitions, conventional approaches rely on periodic modulation schemes that demand ultrafast modulation rates and continuous energy input, posing significant practical challenges at optical frequencies. Here, we demonstrate that periodic-modulation-driven photonic transitions within the radiation continuum can be effectively mimicked using a single-period ultrafast pulse modulation, eliminating the need for sustained continuous modulation. By leveraging dispersion engineering in metasurfaces to tailor the density of states in the radiation continuum, we achieve controlled frequency transitions and theoretically demonstrate strong nonreciprocity for free-space waves as a key application. Our findings may guide future experimental research on time-varying photonics using materials such as transparent conductive oxides and semiconductors, expanding the possibilities for ultrafast and reconfigurable optical technologies. More broadly, our work may establish a practical and energy-efficient framework for dynamic photonic systems, with potential applications ranging from spatio-temporal wavefront manipulation to photonic computing and ultrafast signal processing.

Drug-drug interactions (DDIs) strongly affect the safety and efficacy of combination therapies. Despite the availability of large DDI databases, selecting optimal multi-drug combinations that balance safety, therapeutic benefit, and regimen size remains a challenging combinatorial optimization problem. Here, we present a quantum-control-based framework for DDI-aware drug combination optimization, in which known harmful and synergistic interactions are encoded into Ising Hamiltonians as penalties and rewards, respectively. The optimization is performed using the feedback-based quantum algorithm FALQON, a gradient-free variational approach. We study two clinically motivated tasks: the Maximum Safe Subset problem and the Synergy-Constrained Optimization problem. Numerical simulations using interaction data from Drugs.com and SYNERGxDB demonstrate efficient convergence and high-quality solutions for clinically relevant drug sets, including COVID-19 case studies.

High-performance room-temperature sensing is often limited by non-stationary $1/f$ fluctuations and non-Gaussian stochasticity. In spintronic devices, thermally activated Néel switching creates heavy-tailed noise that masks weak signals, defeating linear filters optimized for Gaussian statistics. Here, we introduce a physics-integrated inference framework that decouples signal morphology from stochastic transients using a hierarchical 1D CNN-GRU topology. By learning the temporal signatures of Néel relaxation, this architecture reduces the Noise Equivalent Differential Temperature (NEDT) of spintronic Poisson bolometers by a factor of six (233.78 mK to 40.44 mK), effectively elevating room-temperature sensitivity toward cryogenic limits. We demonstrate the framework's universality across the electromagnetic and biological spectrum, achieving a 9-fold error suppression in Radar tracking, a 40% uncertainty reduction in LiDAR, and a 15.56 dB SNR enhancement in ECG. This hardware-inference coupling recovers deterministic signals from fluctuation-dominated regimes, enabling near-ideal detection limits in noisy edge environments.

Contradictory experimental reports on the relationship between efficiency and stimulation frequency have hindered mechanistic understanding in converting neural activity into mechanical work during muscle contraction. To resolve this issue, we develop a biophysical model integrating calcium-mediated excitation with a detailed cross-bridge cycle to enable single-fiber simulations. Our model predictions indicate that the emergent shortening velocity is the primary determinant of cross-bridge efficiency: efficiency peaks at an optimal velocity and declines at higher or lower velocities, while frequency appears to exert secondary influence. Critically, the velocity yielding peak efficiency remains almost consistent across frequencies, with a slight upward shift at higher frequencies in most of our parametric studies. Interestingly, elevated inorganic phosphate ([Pi]) appears to amplify the efficiency disparity between high- and low-frequency regimes in our analysis. Our work suggests that stimulation frequency modulates efficiency predominantly through its regulation of shortening velocity, which primarily governs the kinetics of the myosin power stroke. This work may help clarify neural control of muscle energetics, and provide a quantitative foundation for studying muscle function in physiological and pathological contexts.

Understanding how ancient Mesoamerican societies integrated mathematical ideas into calendrical design and monumental architecture requires approaches that acknowledge their distinct epistemological frameworks. While explicit textual evidence for concepts such as $\pi$ or the golden ratio $\phi$ is absent, numerical patterns embedded in Mesoamerican calendars, iconography, and ritual architecture reveal a coherent system of proportional reasoning grounded in simple integer ratios. Here we show that the numbers 5 and 8, central to Venus and solar calendrical relations and widely represented in Mesoamerican cosmology, generate rational approximations that reproduce, within known construction tolerances, the geometric relations associated with decagonal layouts. Using high-resolution measurements of the Iguana structure at Guachimontones, we demonstrate that its proportions align with integer ratios consistent with those found in the calendrical system and with the practical geometry of the regular decagon, without requiring knowledge of irrational constants. These findings suggest that Mesoamerican builders employed stable proportional modules that harmonized astronomical cycles, cosmological symbolism, and architectural design. This should not be interpreted as a lack of mathematical sophistication; rather, the material record reveals a distinct mathematical tradition in which number, measure, and cosmology were mutually reinforcing elements of cultural knowledge.

Tabletop version Kibble balances are a significant developing trend for mass realizations following the revised International System of Units. A key innovation through the miniaturization of the Kibble balance from a large-scale instrument into a tabletop device is making the quantum-based realization of mass accessible to a wider range of calibration laboratories and industries. This paper presents a tabletop Kibble balance design at Tsinghua University targeting E2-accuracy class mass calibrations from 1 g to 1 kg. For calibrating a mass of 1 kg, for instance, the required relative standard measurement uncertainty must be below 0.27 ppm to meet E2-accuracy class. Major components and features of the proposed system are discussed. A novel method of multi-harmonic excitation is proposed to improve the coil-motion linearity during velocity measurement. We show that injecting odd-order harmonics into the motion-driving current can significantly improve the uniformity of the coil's moving velocity, while the second-order component can address the asymmetry between upward and downward movements. This achieves a flat velocity $\Delta v/v< 5\%$ over 60% of the motion cycle.

We present a method for implementing an optical neural network using only linear optical resources, namely field displacement and interferometry applied to coherent states of light. The nonlinearity required for learning in a neural network is realized via an encoding of the input into phase shifts allowing for far more straightforward experimental implementation compared to previous proposals for, and demonstrations of, $\textit{in situ}$ inference. Beyond $\textit{in situ}$ inference, the method enables $\textit{in situ}$ training by utilizing established techniques like parameter shift methods or physical backpropagation to extract gradients directly from measurements of the linear optical circuit. We also investigate the effect of photon losses and find the model to be very resilient to these.

The paper examines and critiques the expression of entropy as the logarithm of the number of quantum states of a physical system. Boltzmann method of expressing entropy as the logarithm of the number of states of a gas with a given total energy is analyzed. We demonstrate that entropy is the product of subquantum processes and show that entropy is expressed as the ratio of the logarithm of the maximum number of realizations, over the observation period, of a macroscopic system's states with a given total energy, to the number of occurrences of its quantum states over this time.

We consider the problem of a conditional extremum of an action in a class of fields constrained by differential equations. For this setup, we propose an extension of Noether's first theorem to connect the symmetries of the action and the imposed equations to the currents conserved at the conditional extrema. The key ingredient of the extension is the gauge symmetry of the differential equations constraining the admissible class of field configurations. We consider a special type of global symmetries of the action which we call conditional symmetries. Such global symmetries must be special cases of gauge transformations of the constraint equations. We construct conservation laws that follow from the conditional symmetries of action. No Lagrange multipliers or other auxiliary fields are introduced and the conserved currents include only the original fields. We also prove the converse theorem which connects the conserved currents to the conditional symmetries of action. The general method is illustrated by several examples.