Papers

Turning scientific papers into reliable code requires making implicit computational knowledge explicit. We introduce and evaluate a human-supervised workflow in which large language models develop and review technical specifications before implementing quantum many-body algorithms. The specifications encode conventions, procedures, scaling requirements, and validation gates for mathematical consistency, physical accuracy, and production-scale performance. For the density-matrix renormalization group, all 16 tested specification–implementation model pairings succeed, compared with 6 of 13 direct attempts; two further attempts succeed with a prose specification. For Pfaffian conversion of Hartree–Fock–Bogoliubov states to matrix product states, on eleven matched model–harness configurations, four self-paired workflow attempts pass and no direct attempts do; across all phases, 11 of 26 workflow attempts pass the audit and validation criteria. Cross-model transfer is asymmetric: one implementation model succeeds with all four specifications written by other models, whereas all four reverse-direction attempts fail. A consolidated specification informed by these experiments supports successful implementations in three previously unsuccessful model–harness configurations. Technical specifications therefore guide scalable implementation and help distinguish paper-to-code ambiguity from limitations in implementation capability. We synthesize these findings into the Paper-to-Program Many-Body protocol, which turns implementation experience into reusable review criteria, validation gates, and reporting practices. Publicly archived prompts, transcripts, specifications, code, and validation records support independent verification and extension to other many-body algorithms.

The rapid integration of Large Language Models (LLMs) into scientific writing fundamentally challenges traditional definitions of authorship, responsibility, and scientific integrity. As researchers transition from using computers as deterministic tools to managing them as “virtual collaborators,” the nature of human contribution must be re-evaluated. Using the drafting process of a recent computational physics manuscript as a case study, this essay explores the indispensable role of the Human-in-the-Loop (HITL). We demonstrate that while AI excels at structural organization and syntax generation, the human author bears the ultimate responsibility for enforcing rigorous physical logic, maintaining academic diplomacy, and anticipating peer-review critiques. In this paradigm, the human contribution shifts from writing boilerplate text to acting as a Principal Investigator who actively mentors and steers the AI's reasoning. To ensure accountability and preserve the integrity of the scientific record in this new era, I argue that the community must mandate the publication of full, unedited AI interaction transcripts as standard supplementary material.

Density functional approximations (DFAs) suffer from delocalization error, which limits their accuracy in predicting electron affinities (EAs), ionization potentials (IPs), and quasiparticle energies. In this work, we present a theoretical refinement of the quasiparticle energies from density functional theory (QE-DFT) method by improving the perturbative expression for the exchange-correlation potential, leading to a more consistent description of molecular systems. We further develop an open-source Python program, PyGSC, built upon the PySCF library, which implements the modified QE-DFT framework. Benchmark tests on main-group atoms and G2/97 molecules demonstrate that the modified QE-DFT method outperforms the original DFAs, with third-order corrections achieving mean absolute deviations below 0.3 eV for EA and IP predictions. Application to dipole-bound states of DNA/RNA nucleobases further validates the superiority of the QE-DFT approach over original DFAs, offering an efficient and accurate approach for predicting electronic properties in large molecular systems.

Iron particles, with their non-volatile combustion mode, remain in the dispersed phase throughout the combustion process, causing the flow in a typical iron powder combustor to be particle-laden and turbulent. Preferential concentration is a phenomenon prevalent in such turbulent flows that causes particle clustering. To estimate the effects of clustering on the combustion process, direct-numerical-simulations are performed on a cubical domain with forced homogeneous isotropic turbulence. Simulations pertaining to Kolmogorov Stokes number $\mathrm{St}=1,10,50$, turbulent Reynolds number $\mathrm{Re_\lambda}= 5,10,20$, and global equivalence ratio (considering FeO as the oxidation product) $\phi=0.25,0.5,0.75$ are executed. Increasing $\phi$ significantly extends the combustion completion time. A Poisson distribution of particles burns faster with a higher peak mean temperature. The evolution of the mean temperature in the combustion of the clustered distribution is smooth and results in a smaller peak value. However, the total combustion time of a clustered distribution is significantly extended, by up to eight times at $\mathrm{Re_\lambda}=20$ and $\phi=0.75$. Analysis of the Voronoi volumes $V_\mathrm{norm}$ at the start of combustion shows that particles in highly dense regions burn longer, as seen before in the literature. Furthermore, the combustion time exhibits a strong exponential dependence on $V_\mathrm{norm}$ in the “cluster” regions, and an asymptotic behavior in the “void” regions. However, significant spread is observed in the correlation. Time-averaging $V_\mathrm{norm}$ does not minimize this variation considerably. Analysis of the macroscale $\mathrm{O_2}$ depletion zone indicates the importance of the macrostructure – proximity of multiple clusters – on the extension of the combustion time.

Stratospheric turbulence is difficult to observe, yet it strongly affects how momentum, trace gases, and aerosols spread through the atmosphere. Here, we use global high-resolution radiosonde observations from 370 stations during October 2014-December 2025 to estimate stratospheric turbulent diffusivity with a Richardson-number-based method. We find that turbulent diffusivity spans a broad range and is dominated by turbulence occurring in statically stable but strongly sheared conditions. Enhanced values appear over Turkey, India, Malaysia, Japan, and major mountainous regions, with evidence that mountain waves and convective activity both contribute. We also identify a local maximum just above the tropical tropopause, between the Equator and 15 N at 17 km altitude, which may indicate an injection region conducive to the rapid aerosol dispersion for stratospheric aerosol injection (SAI). Stratospheric turbulent diffusivity also shows a significant increase ($3.5 \times 10^{-3}\ \mathrm{m^2\ s^{-1}\ yr^{-1}}$) over 2015-2025. Finally, these results may help constrain and predict the dispersion of stratospheric plumes from rockets, aircraft, and potential SAI.

Exponential growth in global data traffic demands ever-increasing transmission rates–a pursuit fundamentally constrained by the physical limitations of digital-to-analog converters (DACs). Existing strategies to overcome this bottleneck, such as multi-DAC arrays and optical time-division multiplexing, inevitably introduce system complexity and coordination overhead. Here we demonstrate an all-optical spatiotemporal transmitter that generates controllable high-repetition information-carrying femtosecond pulses at the focus of a phase-modulated planar diffractive lens (PDL) through optical-path-induced spatial-to-temporal conversion. Each pulse serves as an information bit, encoding binary data via on-axis focal intensity states corresponding to '0' and '1', achieved by switching between topological and constant phase modulations. High experimental orthogonality between arbitrary bits enables nearly error-free transmission of 15X15-pixel grayscale (8-bit coding) and colour (9-bit coding) images at a record-high single-channel rate of approximately 3 terabits per second (Tbit/s). Free from electronic and coordination bottlenecks, this all-optical transmitter establishes a scalable high-speed single-channel pathway toward ultrahigh-capacity optical communication.

A spherical torus (ST) p B11 plasma model that satisfies multi-magnetofluid force balance is developed, which includes small fractions of suprathermal ions with temperatures around 0.5 MeV and suprathermal electrons in the MeV range. Alongside the primary thermal plasma with ion temperatures exceeding 100 keV and densities above 10E20 m-3, these components enhance fusion reaction rates by leveraging the p B11 double-peak fusion cross section. Suprathermal ions and strong toroidal rotation driven by neutral beam injection have been observed in devices such as START, MAST, NSTX, Globus-M2, and ST40. Central-solenoid-free plasma initiation, ramp-up, and sustainment were tested on EXL-50 and replicated on EXL-50U with partial central induction, demonstrating efficient current drive and consistent with the multi-magnetofluid equilibrium model. Motivated by ENN's aneutronic commercial fusion roadmap, this paper presents a rotating, thermally un-equilibrated ST p B11 plasma with unique properties: fluid components experience separate balance under centripetal, electrostatic, and Lorentz forces with common electric and magnetic fields, leading to large rotation speed differences between thermal boron ions and suprathermal protons; a large outboard region with magnetic well and omnigeneity is created, affecting neoclassical transport and gradient-driven turbulence; suprathermal charged particles can extend beyond the last closed flux surface and be limited by plasma-facing components, influencing recycling and pedestal conditions; and the superposition of these plasma components modifies sources and sinks of free energy, prompting renewed evaluation of stability, turbulence, transport, heating, current drive, and flux diffusion. Challenges and opportunities for sustained burn are discussed for a compact p B11 ST with 1.4-meter major radius, 13-MA current, and 3-T toroidal field.

Spatially Fractionated Radiation Therapy (SFRT) or GRID therapy has been in existence for more than a century. GRID therapy was invented as an approach to address the challenges posed by bulky tumors. To treat patients with GRID therapy, the protocol needs to be implemented within the Treatment Planning System (TPS) software. Also, the dose needed for treatment must be validated. A few TPS software vendors such as Elekta and Varian have protocols for treating patients with GRID therapy. However, we are the first to implement the protocols within RayStation TPS. To achieve these, we used the .decimal GRID block applicator to create conformal treatment plans. For the beam modeling, several dosimetry data, and measurements such as percentage depth dose (PDD), beam profile, output, and GRID factors, were obtained for different field sizes and energies. The dose distribution from the GRID openings covered the Planning Target Volume (PTV), with the QA plan having a 98% agreement with the planned dose. Also, we used the Mapcheck2 results to verify the adjustments made to the script variables such as aperture size (hole and spacing). We developed a robust way to validate the dose needed for patient treatment using the GRID block applicator. Also, we have proposed a novel approach to standardize the clinical implementation of the GRID block in RayStation TPS.

We report time-resolved photoelectron spectroscopy of the $3s3p$ doubly excited states of helium dressed by an intense near-infrared (NIR) laser field. Using synchronized XUV free-electron-laser and 800-nm NIR laser pulses, we observe a pronounced delay-dependent shift of resonance-related spectral minima together with the emergence of additional structures around the NIR sideband energy. Ab initio theoretical calculations support these observations and identify the features as signatures of NIR-induced coupling of the bright ($3s3p {}^{1}P^{o}$) autoionizing state to nearby dark ($^{1}D^{e}$ and $^{1}S^{e}$) resonances below the $N = 3$ threshold. A multichannel Fano resonance analysis of the measured spectra yields delay-dependent line-shape parameters and resonance energies, establishing a quantitative route to characterize and control correlated two-electron resonances in strong laser fields.

I present a quantum-tunnelling oscillator model as a universal dynamical engine for two paradigmatic problems in quantum cognition theory – optical illusion perception and group decision making – where individuals are treated as quantum-mechanical agents whose choices shift through context-dependent transitions rather than simple probabilities. I show that, when networked together, these units form a quantum-cognitive neural system that reproduces familiar collective and perceptual phenomena while naturally accommodating counterintuitive processes that challenge classical models. Bridging ideas from quantum cognition theory and neural networks, this approach offers a compact, physically grounded way to describe how real individuals and groups think, perceive and decide.

A photodetector that can directly distinguish the orbital angular momentum (OAM) of light is highly desirable for integrated on-chip OAM detection and focal plane array devices. The recent development of OAM detectors based on the intrinsic orbital photo galvanic effects (OPGE) of materials provide a new route for direct OAM detection that is on-chip scalable with high resolution and speed. In this paper, we summarize the current progress in direct photodetection of OAM via OPGE. We begin with a short review of the basic operation scheme of the OAM detector and provide a comprehensive symmetry analysis to sort out the favorable characteristics of the materials, incorporating considerations from device schemes based on various device performance characteristics and specific application circumstances. From that, we review the current experimental progress and technical challenges, then oversee the possible solutions to these challenges and provide a perspective on the future opportunities of this OAM detection route.

Natural panspermia is a transport-and-establishment hypothesis, not a theory of abiogenesis. The Earth-specific question is whether any nonterrestrial donor remains competitive with terrestrial origin once launch, planetary-system escape, transit, Earth interception, atmospheric entry, terminal loading, and post-delivery establishment are imposed. We formulate this problem as a donor-class dependent Earth-directed transport-survival kernel, supplemented by a minimum protected-depth envelope d_{min}(t_{flight}) and a survival-weighted buried-volume fraction F_{bur} for the low-shock spall population. The resulting hierarchy is strongly ordered under current transport and survival constraints. Hard panspermia is physically credible only on Solar-System scales: early Mars combines demonstrated lithic exchange with Earth, low escape speed, early aqueous habitability, and a rare 10^2-10^4 yr transfer tail, whereas the more common 10^5-10^7 yr martian transfer regime requires lightly shocked meter-class carriers. Beyond the Solar System, low-v_{inf} capture and long-duration survival fail simultaneously; even the Solar birth-cluster channel yields only ~3 x 10^{-5} f_{seed} expected Earth-seeding events, where f_{seed} is the conditional probability that a viable arrival establishes life on Earth. Indigenous terrestrial origin therefore remains the default inference, early Mars is the only quantitatively serious external hard-panspermia alternative, and extrasolar or intergalactic hard panspermia is not competitive for Earth's actual origin history. Soft panspermia is much more plausible as chemical enrichment of early terrestrial abiogenesis.

Understanding the realization of thermal equilibrium through the thermalization process in a many-body system is a fundamental and complex scientific question, bridging thermodynamics and classical dynamics and connecting to a host of physical phenomena, such as mechanical instabilities in a thermal environment. In this work, based on the harmonic lattice model, we investigate the thermalization process in both velocity and coordinate spaces, by examining microscopic dynamics on the atomic level. We show the distinct relaxation rates of the transverse and longitudinal components of the velocity, reveal the power law governing the nonlinear proliferation of dominant frequencies, and observe the concurrent rapid proliferations of frequencies and topological defects. We also show that the lattice system's persistent out-of-plane deformations exhibit two-stage fluctuation behaviors, characterized by distinct power laws of fractional exponents and associated with the broken up-down symmetry. This work demonstrates the rich dynamics underlying the thermalization process, and advances our understanding on the dynamical adaptations of many-body systems to external disturbances.

Geoscientific systems tend to be characterized by pronounced temporal non-stationarity, arising from seasonal and climatic variability in hydrometeorological drivers, and from natural and anthropogenic changes to land use and cover. As has been pointed out, such variability renders "the assumption of statistical stationarity obsolete in water management", and requires us to "account for, rather than ignore, non-stationary trends" in the data. However, metrics used for model development are typically based on the implicit and unjustifiable assumption that the data generating process is time-stationary. Here, we introduce the JKGE_ss metric (adapted from KGE_ss) that detects and accounts for dynamical non-stationarity in the statistical properties of the data and thereby improves information extraction and model performance. Unlike NSE and KGE_ss, which use the long-term mean as a benchmark against which to evaluate model efficiency, JKGE_ss emphasizes reproduction of temporal variations in system storage. We tested the robustness of the new metric by training physical-conceptual and data-based catchment-scale models of varying complexity across a wide range of hydroclimatic conditions, from recent-precipitation-dominated to snow-dominated to strongly arid. In all cases, the result was improved reproduction of system temporal dynamics at all time scales, across wet to dry years, and over the full range of flow levels (especially recession periods). Since traditional metrics fail to adequately account for temporal shifts in system dynamics, potentially resulting in misleading assessments of model performance under changing conditions, we recommend the adoption of JKGE_ss for geoscientific model development.

Thermodynamic accounts of intelligence have advanced rapidly, but they do not yet resolve a prior definitional problem: what makes internal structure intelligent rather than merely ordered? This paper argues that any thermodynamic intelligence metric should include a usefulness filter. The numerator must track internal structure that is relevant to viability, prediction, and control, not merely raw information or benchmark score. Intelligence is therefore defined as the resource-bounded capacity of an adaptive system to acquire, compress, and deploy useful internal structure so as to improve prediction, control, and robustness across a range of environments. Capability and efficiency are separated. Capability is adaptive reach, A, defined over a reference distribution of environments or task families. Efficiency is acquisition efficiency, I_eff = Delta C_u / W_diss, where C_u denotes useful internal structure and W_diss physically dissipated work. Concretely, C_u is the predictive and control-relevant information that a system's internal state carries about viability-relevant environmental structure, making it a semantic rather than purely syntactic information term. I_eff is a learning or adaptation metric, not a complete scalar measure of deployed intelligence: trained systems at steady state may have Delta C_u = 0 while still exhibiting high adaptive reach. A toy numerical example with exact Landauer-referenced dissipation costs, explicit boundary conditions, temperature-normalised reporting conventions, and falsifiable comparisons against size-based and energy-only baselines are provided.

We study the classification task of structured-light beams after propagation through a random turbulent medium. The received speckle patterns are generated by numerical simulation of a stochastic paraxial propagation model, and the classification task is formulated over a finite alphabet of 15 OAM source classes. We benchmark intensity and autocorrelation inputs using SimpleCNN and ResNet-18 as classifiers. We also quantify the effect of training-set size and receiver-window misalignment. Since additional propagated samples may be costly to obtain, we develop a class-conditioned diffusion model for generative augmentation of turbulence-degraded intensity images. The main contribution is a spectrum-aware diffusion objective: a pixel-domain loss combined with a Fourier-domain Bregman regularizer designed to preserve high-frequency speckle statistics. We prove that this hybrid objective is consistent with the posterior-mean regression target of the diffusion model and show that generated samples substantially improve low-data classification.

The VASCO project has identified over 100,000 sub-second optical transients on photographic plates from the First Palomar Observatory Sky Survey (1949-1957), all predating artificial satellites. Cann (2026a) established that transient detection rates are dose-dependently suppressed during geomagnetic storms (Z = -3.391, p = 0.0007), ruling out emulsion defects and confirming the transients as real, magnetospherically coupled phenomena. Villarroel et al. (2022) constrained the source altitude to ~42,000 km (geosynchronous orbit) through an Earth-shadow deficit. This paper presents two results. First, a pre-registered empirical test reveals the full temporal recovery profile: transient rates remain suppressed at 55% of baseline during days 7-21 post-storm, then rise to 309% of baseline during days 25-45 (p = 0.00066, Wilcoxon rank-sum; all robustness checks significant). Combined with the dose-response staircase, the overall significance reaches 3.6-4.7 sigma (Fisher's method, range reflecting sensitivity to the independence assumption). Second, we propose a candidate physical mechanism: storm-enhanced electromagnetic trapping of charged micrometeoroid dust at L ~ 6.6, followed by aggregation of icy cometary grains under restored cold plasmaspheric conditions. A flux dilution analysis demonstrates that specular reflection from a partially reflective icy aggregate only 1-4 m in diameter suffices to produce the observed plate magnitude at 42,000 km. This mechanism connects the VASCO transients to independently observed magnetospheric dust swarms correlated with geomagnetic activity (Sommer 2024) and explains the extinction of the transient population following the onset of the space age. Multi-site replication is required to confirm these results.

While Physics-Informed Neural Networks offer a promising framework for solving partial differential equations, the standard $L^2$ loss formulation is fundamentally insufficient when applied to the Bhatnagar-Gross-Krook (BGK) model. Specifically, simply minimizing the standard loss does not guarantee accurate predictions of the macroscopic moments, causing the approximate solutions to fail in capturing the true physical solution. To overcome this limitation, we introduce a velocity-weighted $L^2$ loss function designed to effectively penalize errors in the high-velocity regions. By establishing a stability estimate for the proposed approach, we shows that minimizing the proposed weighted loss guarantees the convergence of the approximate solution. Also, numerical experiments demonstrate that employing this weighted PINN loss leads to superior accuracy and robustness across various benchmarks compared to the standard approach.

Atomic-resolution imaging on molten metal/solid interfaces at temperatures above 200 {\deg}C was achieved using a high-temperature, high-speed atomic force microscope (AFM) equipped with a qPlus sensor. A tip-scanning high-speed Quadpod scanner for a large mass load of qPlus sensor (2.3 g) was developed to enhance thermal drift tolerance by high-speed scanning and thermal insulation from the heated specimen. This scanner has dominant resonant frequencies of 7.05 kHz (lateral) / 29.7 kHz (vertical) without a load. In addition, the Hybrid-loop frequency demodulation technique for low-resonant-frequency ($f_0$) sensors with a wider bandwidth than conventional phase-locked loop was also established, providing a demodulation bandwidth of $B_{\Delta f_\mathrm{inst}}\sim 0.26 f_0$ without exceeding the theoretical noise of the input deflection signal. Combining these techniques enabled atomic-resolution imaging on the molten $\mathrm{Ga/PtGa_x}$ interface at $\sim$210 {\deg}C. The topographic images obtained at $\sim$210 {\deg}C showed a relatively low-symmetry surface with an oblique lattice with a superstructure, which differed from the primitive rectangular lattice observed in the non-heated sample left at room temperature for 96 h. This demonstrates that the developed high-temperature, high-speed AFM techniques for qPlus sensors enable visualization of non-aqueous liquid/solid interfaces above 200 {\deg}C at atomic resolution, which has various potential applications, such as injection modeling, soldering, and the fabrication of liquid-metal-based catalysts.

Correction for nearsightedness and farsightedness is an important concern for functional magnetic resonance imaging (fMRI) experiments involving visual stimuli in humans. In the absence of personal contact lenses, spherical refractive errors are typically corrected using interchangeable lenses mounted in goggles or glasses frames worn by the participant, or mounted on the head coil during scanning. The coil-mounted device described here avoids the ergonomic challenges encountered with head-mounted goggles and addresses limitations of prior coil-mounted designs, including ease of lens switching and inter-pupillary distance adjustments. Our device can be 3D printed economically with MRI-compatible plastics, including PLA.

Tensor hypercontraction provides an attractive four-center two-electron repulsion integral format that can lower the scaling of many electronic structure methods while only requiring O(N^2) memory. However, in its grid-based least-squares incarnation, tensor hypercontraction requires the tedious design of compact spatial quadrature grids to achieve efficiency and accuracy, representing a bottleneck for widespread application. To simplify grid generation, we devise a reweighting scheme in which the grid weights are optimized to ensure accurate reproduction of the atomic orbital overlap matrix by numerical integration. By casting this fitting task as a non-negative least-squares problem, we obtain a black-box methodology that not only yields robust grids for tensor hypercontraction as well as numerical integration of other integrals but also prunes the grids by zeroing quadrature weights for insignificant points.

We address the problem of recovering a time-varying 4D distribution from a sparse sequence of 2D projections - analogous to novel-view synthesis from sparse cameras, but applied to the 4D transverse phase space density $\rho(x,p_x,y,p_y)$ of charged particle beams. Direct single shot measurement of this high-dimensional distribution is physically impossible in real particle accelerator systems; only limited 1D or 2D projections are accessible. We propose PhaseFlow4D, a feedback-guided latent diffusion model that reconstructs and tracks the full 4D phase space from incomplete 2D observations alone, with built-in hard physics constraints. Our core technical contribution is a 4D VAE whose decoder generates the full 4D phase space tensor, from which 2D projections are analytically computed and compared against 2D beam measurements. This projection-consistency constraint guarantees physical correctness by construction - not as a soft penalty, but as an architectural prior. An adaptive feedback loop then continuously tunes the conditioning vector of the latent diffusion model to track time-varying distributions online without retraining. We validate on multi-particle simulations of heavy-ion beams at the Facility for Rare Isotope Beams (FRIB), where full physics simulations require $\sim$6 hours on a 100-core HPC system. PhaseFlow4D achieves accurate 4D reconstructions 11000$\times$ faster while faithfully tracking distribution shifts under time-varying source conditions - demonstrating that principled generative reconstruction under incomplete observations transfers robustly beyond visual domains.

Pairwise hydrodynamic interactions of microswimmers form the fundamental building blocks for understanding their more complex collective behaviors. In this work, we revisit the canonical problem of two interacting squirmers swimming along their common line of centers in both Newtonian and shear-thinning fluids. For the Newtonian case, we first derive an exact, closed-form solution for the axisymmetric Stokes flow generated by the interacting pair, thereby complementing prior analyses based on the reciprocal theorem approach by providing direct access to the detailed knowledge of the flow around the swimmers. The analytical solution is then used to cross-validate numerical simulations based on the finite element method. The combined theoretical and numerical investigation reveals co-swimming configurations in which the two squirmers develop identical velocities over a range of separations. We rationalize these behaviors through symmetry arguments and quantify their propulsion performance in terms of the speed and energetic cost of swimming. Furthermore, motivated by the prevalence of shear-thinning biological fluids encountered by microswimmers, we examine how this ubiquitous non-Newtonian rheological behavior modifies the propulsion characteristics of these co-swimming pairs. Taken together, our results establish quantitative benchmarks for interacting squirmers in both Newtonian and shear-thinning fluids, laying the groundwork for future studies of many-body dynamics of microswimmers in complex fluid environments.

We demonstrate that the system of fine-tuning constraints for life is overdetermined: the number of inequalities is too large relative to the number of free parameters in the chemical sector and likely not only there. This entails that life-permitting conditions are secured at the level of physics not only by the fine-tuned fundamental constants, but also by a very special form of the laws. In 1960, Eugene Wigner published his essay "The Unreasonable Effectiveness of Mathematics in the Natural Sciences," summarizing his reflections on the miraculous comprehensibility of the mathematical structure of physical laws. The present paper points to another, no less remarkable, aspect of this structure, which may be called as our title suggests.

Nonlinear optics underpins a broad range of photonic technologies, from classical and quantum light sources to emerging nonlinear photonic neural networks. Yet, conventional nonlinear optical devices exhibit static functionality: their transfer characteristics and emission profiles are dictated by the intrinsic nonlinear process and locked by fabrication, limiting adaptability. Here, we introduce an ultra-thin metasurface platform that enables dynamic reconfiguration of nonlinear functionality in a contact-less fashion. By leveraging all-optical control of the optical torque exerted on liquid crystal molecules infiltrating a resonant metasurface, we achieve tunable polynomial nonlinear transfer functions based on third-harmonic generation process. This mechanism further allows real-time modulation of nonlinear weighting across different diffraction orders, revealing a previously unexplored interplay between mode structure and nonlinear emission. Our approach opens up a pathway toward field-programmable nonlinear photonic systems, offering unprecedented flexibility for reconfigurable nonlinear signal processing and adaptive photonic computing.

We present HYMOR (HYpersonic MOdal/non-modal, and Receptivity), an open-source computational framework for the linear stability analysis of high-enthalpy hypersonic flows. The toolkit includes MATLAB and Julia implementations and is released under the MIT license. HYMOR provides global modal, non-modal, and freestream receptivity analyses capable of capturing interactions among spatially separated physical mechanisms that are inaccessible to traditional local methods. A shock-fitting formulation is employed to treat the bow shock as a sharp discontinuity, ensuring that the interaction of infinitesimal disturbances with the shock reproduces the exact response predicted by linear interaction analysis. The code also solves the nonlinear equations for base-flow computation and automatically linearizes the resulting discrete operators for the stability analyses. Several thermochemical models are available for treatment of real-gas effects in high-enthalpy regimes. The numerical implementation is verified against a collection of benchmark cases that demonstrate the accuracy and capabilities of the toolkit across its modal, non-modal, and receptivity analysis modes.

Biological surfaces with micrometer-scale protrusions, such as microvilli, crustacean hairs, and cilia, often interact with pressure-driven fluid flow, resulting in a two-way elastoviscous problem. Characterizing their response to flow can enable applications in microfluidics, bioinspired engineering, and smart materials. Here, we investigate a biomimetic hair system subjected to pressure-driven flow experimentally and theoretically. We show that the rescaled resistance and rescaled pressure of various hair and chamber conditions collapse into an inverse power law after a critical dimensionless pressure, yielding one characteristic response across conditions. Our model predicts the behavior of angled hairs under Poiseuille flow along and against the grain, with the latter exhibiting significantly higher resistance. Finally, we demonstrate a conceptual application of angled hair beds to prevent backflow during intravenous therapy. This work establishes a unified model and experimental characterization of hair bed behavior in pressure-driven flows, advancing understanding of hair-flow interactions and laying the foundation for innovative applications.

In both rural and urban educational settings, science education is often hindered by limited access to lab resources and intimidating, complex instruments. This paper introduces a low-cost, homemade experimental apparatus built using a mobile charger, nichrome wire, galvanometer, and digital multimeter that enables educators to perform key higher secondary electricity experiments. The Indigenous Metre Bridge (IMB) has proven to be an intuitive, user-friendly tool that not only bridges theoretical and practical learning but also reduces student apprehension toward lab work. Its simplicity and accessibility exemplify how frugal innovation can transform physics education.

Band formation in periodic media is a central topic in undergraduate solid-state physics, typically introduced through Bloch's theorem as an eigenvalue problem in reciprocal space for infinitely periodic systems. While mathematically elegant, this formulation can appear abstract: it assumes an idealized infinite lattice, shifts attention away from real-space wave dynamics, and presents band structures as static results rather than emergent consequences of wave propagation. Consequently, students often struggle to relate band gaps to familiar physical phenomena such as reflection, transmission, and interference, leading to a disconnect between formal band theory and observable wave behavior. We present a computational framework that addresses this gap by reconstructing band formation directly from time-domain wave propagation in finite periodic systems. Using a staggered-grid finite-difference time-domain scheme for elastic waves, a broadband excitation is propagated through a layered medium to obtain its transmission spectrum. From this, students extract the Bloch dispersion relation and observe spatial attenuation in band-gap regions, revealing the roles of multiple scattering and phase coherence. This approach provides a physically transparent pathway to band theory and enables exploration of finite-size effects, disorder, and defect-localized modes within a unified computational framework. Implemented through compact code and guided exercises, the method offers an accessible and versatile pedagogical tool, while also equipping students with transferable skills in numerical modeling of wave phenomena across disciplines.

While crystalline materials with glass-like thermal conductivity are fundamentally intriguing, structurally triggering the transition from propagating to diffusive heat transport within a single framework remains a formidable challenge. Here, using extensive machine learning molecular dynamics, we demonstrate a fundamental thermal transport crossover in metal-organic frameworks. We reveal that grafting flexible side chains onto a pristine MOF backbone acts as a structural switch, strongly reducing the thermal conductivity by $\sim$70% (from $\sim 0.7$ to $\sim 0.2\ \text{W m}^{-1}\text{K}^{-1}$ at 300 K). Crucially, the functionalized derivatives exhibit a drastic transition from a classical Peierls $\sim 1/T$ decay to an anomalous, temperature-independent glass-like plateau. Reciprocal- and real-space analyses reveal the microscopic origins: the side chains act as built-in local resonators that trap acoustic energy via strong low-frequency resonant hybridization, while simultaneously inducing extreme steric crowding. Consequently, the heat-carrying phonon modes become critically damped, with their mean free paths strictly confined to the nanometer scale and their lifetimes collapsing to the Ioffe-Regel limit. This work establishes a highly programmable molecular engineering strategy to dismantle the phonon gas model, forcing crystalline frameworks into an extreme diffusive transport regime.