Papers

We develop a novel approach to ultrafast optical modulation of quantum-mechanical phenomena at the interface of plasmonic metals. Focusing on efficient and versatile nanoparticle-on-mirror plasmonic nanocavities, we discuss indirect control of plasmonic properties through laser-induced ballistic hot electron injection. Overcoming the limitations precluding the observations of laser-driven mesoscopic phenomena in the time domain with state-of-the-art amplified sources, our proposed experimental approach can be readily realized without irreversible optical damage and holds immense potential for the future development of ultrafast electrodynamics in nanogaps, applications in photochemistry and nanoscale control of quantum emitters. Agreeing with the results of numerical simulations, an intuitive microscopic model for the proposed time-dependent mesoscopic electrodynamics facilitates the analysis of the temperature-induced modulation of quantum plasmonic properties in a broad parameter space. Our work expands the realm of quantum nanophotonics onto non-equilibrium electronic systems and facilitates the development of ultrafast methods in active plasmonics.

We present T-DAQ-P, a compact and portable data acquisition and telemetry platform designed to support detector deployments in laboratory and field conditions by integrating event streaming, slow-control telemetry, and operator-oriented commissioning tools in a single unit. The system combines a Raspberry Pi 5 host for multi-stream ingestion, visualization, and storage with an Arduino UNO R4 WiFi microcontroller dedicated to sensor acquisition. The electronics integrate commercial off-the-shelf (COTS) modules through an engineered interface layer including protected power distribution, logic-level adaptation, and a DB-37 expansion connector exposing I2C-SPI-ADC lines for external instrumentation. On the telemetry side, the microcontroller firmware performs automatic I2C discovery, conditional sensor handling, and outputs framed NMEA-like messages with XOR checksum and explicit block delimiters, enabling robust parsing and synchronization on the host. A finite-state control (STOP-RUN-SIM-INIT) and watchdog-style recovery actions implement resilience under partial hardware availability and field failures. The host software adopts a multi-process architecture with independent readers for event, telemetry, and actuator streams, message queues for isolation, and coherent time-stamped logging across streams. A simulation mode and integrated command channel allow end-to-end pipeline verification and rapid commissioning without full sensor availability. The paper details architecture, electronics integration, firmware protocol, host software design, and functional/engineering validation procedures, providing a reproducible blueprint for portable instrumentation readout and contextual telemetry acquisition.

We present a fully reproducible demonstration of an AI-assisted scientific workflow designed for a broad physics, mathematics, and computer-science readership. The initial project artifact stack was generated from one single user prompt and then reviewed and curated for submission by the human author. Rather than claiming a new scientific discovery, the manuscript uses canonical benchmark problems with exact, manufactured, or independently checkable answers. The analytical component starts from the one-dimensional quantum harmonic oscillator, derives its dimensionless form, and validates finite-difference eigenpairs against exact Hermite-function benchmarks. The numerical partial-differential-equation component solves a heat equation with a known modal solution and a Poisson problem verified by a manufactured solution, with explicit convergence studies. The inverse-modeling component fits synthetic damped-oscillation data by nonlinear least squares and quantifies parametric uncertainty by bootstrap resampling. The computational-science component compares dense and sparse eigensolvers and contrasts direct and iterative sparse linear solvers, with careful interpretation of machine-dependent timing data. Taken together, the results show that contemporary AI can already serve as a useful scientific copilot for derivation, implementation, validation, visualization, and manuscript preparation, provided that each stage is constrained by benchmark theory, explicit verification, and transparent artifacts. The demonstration is therefore relevant not because the underlying science is novel, but because it offers a concrete template for trustworthy AI use in technical research practice.

Dipolar molecules in optical traps are a versatile platform for studying many-body phases of quantum matter in the presence of strong and long-range interactions. The dipolar interactions in such setups can be enabled by microwave driving opposite parity rotational levels of the molecules. We find that the regime where the $N=0,J=1/2,F=1$ state is coupled to the $N=1,J=3/2,F=2$ manifold with circularly polarized microwaves, in the presence of a small magnetic field, can lead to spin-1 quantum magnetic Hamiltonians, due to the decoupling between electron spin and orbit, that is unique to the $^2\Sigma$ ground state molecules. We demonstrate that in one dimension, the phase diagram associated with this Hamiltonian, computed via tensor network methods, hosts the celebrated Haldane phase. We find that the Haldane phase persists even in the presence of SU(3) correction terms that break the SU(2) algebra of the Hamiltonian. We discuss the feasibility of the proposed scheme for $^2\Sigma$ molecules with large rotational constants such as the directly laser cooled molecule MgF for future experiments.

Sparse-view computed tomography (CT) is an effective method to reduce the radiation exposure in medical imaging. To reduce the severe streaking artifacts that occur in reconstructed images due to violation of the Nyquist/Shannon sampling criterion, regularization is widely used to minimize the cost function. However, the iterative methods may lead to the accumulation and propagation of errors, which adversely affects the restoration of image details and textures. In this paper, we propose a hybrid model that integrates statistical sampling with iterative regularization to simultaneously shorten the sampling time and enhance the reconstruction quality. The proposed method is validated using three datasets: the Shepp-Logan phantom, the actual walnut X-ray projections provided by the Finnish Inverse Problems Society, and the clinical lung CT images.

Optical thermodynamics offers a distinctive framework for understanding complex phenomena in multimode systems, yet standard ideal-gas-like formulation neglects the effect of nonlinear interaction on thermodynamic quantities, significantly restricting its range of validity. Here, we overcome this limitation by developing a mean-field thermodynamic theory that incorporates the nonlinear renormalization of the mode spectrum. The resulting nonlinear equation of state, analogous to that of the van der Waals for gases, enables the prediction of power-dependent mode localization and the description of optical cooling and heating in photonic Joule-Thomson expansion. Our work establishes a unified thermodynamic perspective on the nonlinear control and transport of optical waves.

The current definition of the SI second is based on the 133Cs ground-state hyperfine transition in the microwave domain, with the most accurate realizations achieving fractional frequency uncertainties of about (1-2)E16. In contrast, state-of-the-art optical clocks now demonstrate estimated uncertainties two to three orders of magnitude lower, prompting discussion on the redefinition of the SI second. Several options for the new definition have been proposed, one of which introduces a constant N defined as the weighted geometric mean of multiple clock transition frequencies. In this work, we investigate how N can be practically realized when not all defining transitions are available and when multiple optical clocks operate with different performance levels and non-overlapping uptimes. We consider two complementary realization and reconstruction routes. One route is based on geometric-mean combinations, and the other is based on arithmetic-mean combinations. We derive consistent uncertainty expressions that incorporate both measurement uncertainties and, where required, uncertainties of recommended frequencies or frequency ratios. Using analytic three-transition case studies, we identify the parameter regimes in which each route yields a lower total uncertainty and provide explicit conditions for the crossover between them. We further address the dominant role of dead time when a hydrogen maser serves as a flywheel reference by introducing a time-segmented, time-weighted combination based on coefficient and covariance matrices, which accounts for overlapping operation and correlations across measurement intervals. Our findings offer practical guidance for minimizing total uncertainty in multi-clock realizations and contribute to ongoing efforts toward redefining the SI second.

Cross-field electron transport in Hall thrusters is widely attributed to high-frequency $E\times B$ instabilities, yet its net spatial pathway remains poorly resolved. Here we perform instability-resolving three-dimensional particle-in-cell simulations of a Hall thruster using a boundary-faithful and highly integrated framework. The model incorporates a realistic magnetic-field configuration, self-consistent dielectric wall charging, secondary electron emission, Monte Carlo ionization collisions, a self-consistent continuum neutral-gas evolution model, and an open near-plume outflow treatment. From the strongly oscillatory three-dimensional fields, we extract the net instability-driven transport by time and azimuthal averaging of the correlation term $\langle n_e E_y\rangle$ and the corresponding effective perpendicular mobility. The simulations reveal that anomalous electron transport is not distributed uniformly across the channel cross section. Instead, it self-organizes into persistent near-wall pathways connected to the near-exit region. By comparing conducting-wall, ceramic-wall-with-secondary-emission, and open-outflow closures, we show that the near-wall transport topology is robust, while the boundary treatment mainly redistributes the detailed strength of the pathway and its coupling to the exit and near-plume region. These results demonstrate a previously unresolved spatial organization of instability-driven anomalous transport in Hall thrusters and highlight the unique role of 3D PIC simulations in revealing it.

The ocean remains one of the least instrumented parts of Earth, and many geophysical, biological, and anthropogenic signals go undetected for lack of instrumentation. Distributed acoustic sensing (DAS) can transform submarine fiber-optic cables into dense seafloor sensor arrays, but extracting diverse signals from massive DAS recordings remains challenging. Here we present DASNet, a deep learning framework that detects, classifies, and picks arrival times of diverse marine signals in continuous DAS data. Applied to nearly four years of Seafloor Fiber-Optic Array in Monterey Bay recordings, DASNet identifies more than 620,000 events. These detections reveal local earthquakes; distant earthquake- and volcanic-eruption-generated T-waves from the southwestern Pacific and mid-ocean ridge systems; more than 510,000 blue and fin whale calls with seasonal and interannual variability consistent with hydrophone records; and vessel traffic near the cable. Together, these results show that submarine fiber-optic cables combined with deep learning enable scalable, high-resolution ocean monitoring.

First-principles calculations can accurately describe electron-phonon (e-ph) interactions and electronic transport in a wide range of materials, but are currently limited to unit cells with up to $\sim$100 atoms due to computational cost. Here, we develop an atomistic electronic potential with Holstein- and Peierls-like terms for modeling e-ph interactions and phonon-limited electronic transport that enables the study of moiré systems with thousands of atoms per unit cell. This method can accurately reproduce first-principles e-ph coupling and resistivity in graphene and large-angle twisted bilayer graphene (TBG). Using this approach, we study TBG over a range of twist angles down to 1.6$^\circ$ (5044-atom unit cell), and report the evolution of e-ph interactions and phonon-limited resistivity with twist angle. The predicted resistivity increases by two orders of magnitude between 13.2$^\circ$ and 1.6$^\circ$, driven by the progressive reduction of the electronic energy scale. Our calculations can predict key experimental trends in 2.0$^\circ$ and 1.6$^\circ$ TBG, including the resistivity and its dependence on temperature and band filling. Our work establishes a scalable approach for quantitative studies of e-ph interactions and transport in moiré materials and other systems with previously inaccessible length scales.

Atom interferometric inertial sensors offer exceptional sensitivity but are fundamentally constrained by the periodic phase response of matter-wave interference, which imposes an intrinsic half-fringe dynamic-range limit and prevents continuous inertial tracking. In multi-axis configurations, additional cross coupling between acceleration and rotation further complicates closed-loop operation. Here we demonstrate the first dual-channel closed-loop operation of an atomic beam interferometer, realizing decoupled feedback control of acceleration- and rotation-induced phases and overcoming the half-fringe limitation. Using continuous, transversely cooled $^{87}$Rb atomic beams, the interferometric phases associated with rotation and acceleration are independently extracted, tracked across multiple fringes, and actively compensated through Raman frequency modulation. This closed-loop scheme enables unambiguous measurements up to $\pm1\,\mathrm{^{\circ}/s}$ in rotation and $\pm0.17\,\mathrm{g}$ in acceleration while maintaining high fringe contrast, corresponding to nearly two orders-of-magnitude extension beyond the conventional half-fringe limit. The sensor achieves a long-term stability of $4\times10^{-4}\,\mathrm{^{\circ}/h}$ for rotation and $4\,\mathrm{\mu g}$ for acceleration at an averaging time of $1000\,\mathrm{s}$. By converting the intrinsically periodic interferometric response into stabilized phase-encoded inertial channels, this work establishes a new operating regime for atomic beam interferometry and advances matter-wave sensors toward practical quantum inertial navigation under dynamic conditions.

Accurately simulating extended periodic systems is a central challenge in condensed matter physics. Neural quantum states (NQS) offer expressive wavefunctions for this task but face issues with scalability. In this work, we successfully extend the neural network backflow (NNBF) approach to ab-initio solid-state materials. Building on our scalable optimization framework for molecules [Liu et al., PRB 112, 155162 (2025)], we introduce a two-stage pruning strategy to manage the massive configuration space expansions: by utilizing a computationally cheap, physics-informed importance proxy, we devote exact NNBF amplitude evaluations solely to the most relevant determinants, significantly improving optimization efficiency, energy estimation, and convergence. Our framework achieves state-of-the-art accuracy across diverse solid-state benchmarks. For 1D hydrogen chains, NNBF matches or surpasses DMRG and AFQMC, remains robust in strongly correlated bond-breaking regimes where coupled-cluster methods fail, and smoothly extrapolates to the thermodynamic limit. We further demonstrate its scalability by computing ground-state potential energy curves for 2D graphene and 3D silicon. Finally, ablation studies validate the computational savings of our pruning strategy and highlight the dependence of the NNBF energies on basis sets.

We propose that a linear relation between the energy of stress-bearing interactions and the surface of contact within the fragment-asperity model for earthquakes. It reveals as the only one that leads to a closed elementary form for a well-defined total entropy as a function of non-extensivity parameter, $q$. By writing the total Tsallis entropy as a function of $q$, a critical range of values is identified: $1.4\lesssim q\lesssim 1.8$. Such interval of $q$-values corresponds to the strong variation of entropy and contains the most of reported results for this parameter determined for main-shocks around the world in recent decades, indicating the role of $q$ as a criticality indicator, more than just a fitting parameter.

Spontaneous parametric downconversion (SPDC) has been considered as a reliable source of high- dimensional entangled states in orbital angular momentum (OAM) basis. In real-world experiments, the spatial walk-off of the pump often degrades the fidelity of the generated quantum state. Since the walk-off effect breaks the rotational symmetry of the system, the conservation of total OAM is violated. Although the compensation of walk-off effects has become a well-established experimental technique, a systematic modal analysis of the spatial walk-off effect is still incomplete for SPDC. Here, we quantitatively analyze the violation of OAM conservation due to the pump walk-off effect in SPDC processes. We have derived a scaling law of the total OAM distribution with respect to the pump walk-off angle. We have also explored the feasibility of using the spatial walk-off as a mechanism to engineer the generated quantum state. Our study has provided guidelines for the generation of OAM-entangled state under realistic experimental conditions.

The nudged elastic band (NEB) method is the standard approach for finding minimum energy paths and transition states on potential energy surfaces. Practical NEB calculations require several pre-processing steps: endpoint minimization, structural alignment, and initial path generation. These steps are typically handled by ad-hoc scripts or manual intervention, introducing errors and hindering reproducibility. We present a fully automated, open-source Snakemake workflow for small gas phase molecules that couples modern machine learning potentials (PET-MAD) to the eOn saddle point search software. Each step of the calculation lifecycle is encoded as an explicit dependency graph, from model retrieval and endpoint preparation through path initialization and band optimization. The workflow resolves all software dependencies from conda-forge, ensuring identical execution across platforms. Validation on the HCN to HNC isomerization demonstrates that the automated pipeline recovers the known single-barrier energy profile and product energy without manual intervention.

As large language models (LLMs) are increasingly considered for automated assessment and feedback, understanding when LLM marking is valid is essential. We evaluate LLM-as-a-judge marking across four settings spanning three assessment formats - structured questions, written essays, and scientific plots - comparing GPT-5.2, Grok 4.1, Claude Opus 4.5, DeepSeek-V3.2, Gemini 3 Pro, and committee aggregations against human markers under blind, solution-provided, false-solution, and anchored conditions. We distinguish absolute accuracy from rank-order agreement, since a marking system can match the distribution of human marks while failing to order responses by quality. Across task types, performance is sharply task-dependent. For blind university exam questions ($n=771$) and secondary and university structured questions ($n=1151$), models show robust rank-order agreement with human markers (Spearman $\rho > 0.6$), with official solutions reducing error and strengthening agreement. False solutions degrade absolute accuracy, showing that models defer to provided references, but leave rank-ordering intact. Essay marking behaves fundamentally differently. Across $n=55$ scripts, each containing five essays ($n=275$ essays total), blind AI marking is harsher and more variable than human marking and adding marking guidance does not improve rank-order agreement. Anchored exemplars shift the AI mean close to the human mean and compress variance below the human standard deviation, but rank-order agreement remains near-zero. For code-based plot elements ($n=1400$), models achieve high rank-order agreement ($\rho > 0.83$) with near-linear calibration. LLM marking validity depended strongly on the assessment task, and this held for every contemporary model tested. The results also show that the reliability of the human benchmark constrains the claims that can be made about AI-human agreement.

Nitrogen-vacancy (NV) centers in diamond are a versatile quantum sensing platform for high sensitivity measurements of magnetic fields, temperature and strain with nanoscale spatial resolution. A common bottleneck is the analysis of optically detected magnetic resonance (ODMR) spectra, where target quantities are encoded in resonance features. Conventional nonlinear fitting is often computationally expensive, sensitive to initialization, and prone to failure at low signal-to-noise ratio (SNR). Here we introduce a robust, efficient machine learning (ML) framework for real-time ODMR analysis based on a one-dimensional convolutional neural network (1D-CNN). The model performs direct parameter inference without initial guesses or iterative optimization, and is naturally parallelizable on graphics processing units (GPU) for high-throughput processing. We validate the approach on both synthetic and experimental datasets, showing improved throughput, accuracy and robustness than standard nonlinear fitting, with the largest gains in the low-SNR regime. We further validate our methods in two representative sensing applications: diagnosing intracellular temperature changes using nanodiamond probes and widefield magnetic imaging of superconducting vortices in a high-temperature superconductor. This deep-learning inference framework enables fast and reliable extraction of physical parameters from complex ODMR data and provides a scalable route to real-time quantum sensing and imaging.

A minimal effective dark-energy framework - Quantum-Kinetic Dark Energy (QKDE) - is developed in which the scalar kinetic normalization carries a slow background time dependence through a covariantly completed clock field \chi such that K = K(\chi) > 0, while the Einstein-Hilbert metric sector remains unmodified. The resulting effective action admits a diffeomorphism-invariant completion, and working in unitary gauge \chi = t reproduces the background equations used in the numerical analysis. Within the effective field theory of dark energy (EFT-DE) description the model corresponds to \alpha_K > 0 with \alpha_B = \alpha_M = \alpha_T = \alpha_H = 0, implying luminal tensor propagation and a constant Planck mass. Scalar perturbations propagate with c_s^2 = 1, satisfy \Phi = \Psi, and source linear growth through the unmodified Einstein equations. Observable signatures therefore arise solely through the expansion history H(a) and the induced growth D(a). Two realizations of the kinetic normalization are studied: (i) a curvature-motivated form K = 1 + \alpha R/M^2, and (ii) a phenomenological running K = 1 + K_0 (1 + z)^p. A reproducible numerical pipeline and Fisher analysis for H(z), distances, and f\sigma_8(z) are presented. The framework predicts \mu(a,k) = \Sigma(a,k) = 1, \eta(a,k) = 0, and c_T^2 = 1 on linear scales, providing clear falsifiable null tests. Earlier drafts circulated under the working title Scalar-Cost Dark Energy (SCDE).

We introduce an updated, time-dependent treatment to the split-tail ("croissant-like") heliosphere model with data-driven solar wind conditions at 1 au, to study the evolution of the heliosphere with solar-cycle variations in plasma speed, plasma density, and magnetic field intensity. The model produces a sub-Alfvénic and low beta region, not observed by the Voyagers, ~15 au ahead of the heliopause. The simulated magnetic field and radial flow depart from Voyager observations in this region, indicating that time-dependent effects alone are not sufficient to understand this regime of the heliosheath. We decompose fast and slow magnetosonic wave modes from time-dependent plasma pulse structures in the heliosheath, using a linear Riemann variable analysis, for the first time. Fast mode waves can both reflect at the heliopause and transmit into the interstellar medium, and their speeds are unaffected by the low beta plasma regime in front of the heliopause. The model reproduces the pf2 jump in the magnetic field at ~2020 in the interstellar medium and we attribute the source of pressure fronts observed by Voyager 1 in the interstellar medium, and pressure pulses observed by Voyager 2 in the heliosheath, to fast mode waves that are commonly recurring solar-cycle features. The presence of fast mode waves drive a highly variable termination shock, with average radial speeds of 6.05 au yr$^{-1}$ $\pm$ 5.37 au yr$^{-1}$ in the New Horizons direction. We find that the termination shock has a sinusoidal-like oscillatory motion in the rising phase of the solar cycle, and broad inward motions during the declining phase.

During the COVID-19 crisis, policymakers have implemented social bubble merging strategies, which allowed people from different households to meet and interact. Although these measures can mitigate the negative effects of extreme isolation, they also introduce additional contacts that may facilitate disease spread. As a result, several modeling studies have explored the epidemiological impact of different household-merging strategies, in which the selection of households to be merged is guided by specific demographic criteria, such as household size or the age composition of their members. Here, we investigate an alternative pairing strategy in which households are merged according to the number of economically active (working) members. We develop a mathematical model of household networks using real demographic data from multiple regions around the world, and simulate a lockdown scenario in which only economically active individuals can leave their households, while the remaining non-working members stay indoors. By using numerical simulations and the generating function technique, we then estimate the epidemic risk for different household merging strategies. We find that merging strategies based on the number of working members can keep epidemic risk at similar levels as those based on household size. Moreover, the worker-based approach allows significantly more people to form larger social bubbles, exceeding 40% of the population in some countries. We find that merging households with at most one worker provides the best balance between controlling epidemic risk and addressing people's need for social contact.

Solid-state nanopore DNA sequencers present mechanical and chemical stability, reusability, and large-scale integrability. However, their development is hindered by the absence of a protein-free mechanism for controlling DNA translocation, which is accomplished by motor proteins in their biological counterparts. Here, we propose and theoretically analyze a protein-independent ratchet mechanism based on the unzipping of double-stranded DNA at the nanopore rim. When the transmembrane bias exceeds a certain threshold, the base pairs mechanically dissociate, allowing one strand to translocate while the other remains upstream. This unzipping process is known to slow DNA motion, suggesting that voltage pulses can trigger individual unzipping events at externally defined times, a concept referred to as digital unzipping. However, the intrinsic unzipping barrier is insufficient to provide the dwell times required for a reliable ionic-current readout; therefore, an additional mechanism is needed to hold the DNA in place between voltage pulses. To overcome this limitation, we introduce a reversible hold mechanism implemented via electrostatic attraction between DNA and a charged nanopore wall, which temporarily immobilizes the strand once the unzipping fork catches on the nanopore rim. Using a statistical-mechanical model, we track the evolution of the mean and variance of DNA position through each ratchet cycle. Analytical expressions for the corresponding error probabilities show that submicrosecond switching of the hold mechanism enables base-by-base stepping with an error rate <5%. These results theoretically demonstrate that digital unzipping combined with a reversible hold mechanism can yield deterministic single-base motion, thus opening a viable route toward all-solid-state nanopore sequencing.

Machine learning interatomic potentials (MLIPs) have become widely used tools in atomistic simulations. For much of the history of this field, the most commonly employed architectures were based on short-ranged atomic energy contributions, and the assumption of locality still persists in many modern foundation models. While this approach has enabled efficient and accurate modelling for many use cases, it poses intrinsic limitations for systems where long-range electrostatics, charge transfer, or induced polarization play a central role. A growing body of work has proposed extensions that incorporate electrostatic effects, ranging from locally predicted atomic charges to self-consistent models. While these models have demonstrated success for specific examples, their underlying assumptions, and fundamental limitations are not yet well understood. In this work, we present a framework for treating electrostatics in MLIPs by viewing existing models as coarse-grained approximations to density functional theory (DFT). This perspective makes explicit the approximations involved, clarifies the physical meaning of the learned quantities, and reveals connections and equivalences between several previously proposed models. Using this formalism, we identify key design choices that define a broader design space of self-consistent electrostatic MLIPs. We implement salient points in this space using the MACE architecture and a shared representation of the charge density, enabling controlled comparisons between different approaches. Finally, we evaluate these models on two instructive test cases: metal-water interfaces, which probe the contrasting electrostatic response of conducting and insulating systems, and charged vacancies in silicon dioxide. Our results highlight the limitations of existing approaches and demonstrate how more expressive self-consistent models are needed to resolve failures.

The branching geometry of biological transport networks is characterized by a diameter scaling exponent $\alpha$. Two structural attractors compete: impedance matching ($\alpha \sim 2$) for pulsatile flow and viscous-metabolic minimization ($\alpha = 3$) for steady flow. Neither predicts the empirically observed $\alpha_{\mathrm{exp}} = 2.70 \pm 0.20$ in mammalian arterial trees. Incorporating sub-linear vessel-wall scaling $h(r) \propto r^p$ ($p = 0.77$) into a three-term metabolic cost rigorously breaks Murray's cubic law – via Cauchy's functional equation – bounding the static optimum to $\alpha_t \in [2.90, 2.94]$. We formulate a unified network-level Lagrangian balancing wave-reflection penalties against transport-metabolic costs. Because the operational duty cycle $\eta$ is uncertain over developmental timescales, we cast the optimization as a zero-sum game between network architecture and environment. Von Neumann's minimax theorem – proved via strict monotonicity of the cost curves – yields a unique saddle point $(\alpha^, \eta^)$ satisfying an exact equal-cost condition. We further prove $N = 2$ uniquely maximizes the network stiffness ratio $\kappa_{\mathrm{eff}}(N)$, deriving binary branching as a structural consequence of the framework. For the porcine coronary tree ($G = 11$ generations), $\alpha^* = 2.72$, within $0.1\sigma$ of morphometric data. Sensitivity analysis confirms $|\Delta\alpha^*| < 0.01$ across physiological metabolic ranges; the prediction depends critically only on the histological exponent $p$ – a zero-parameter derivation from fundamental scaling principles that simultaneously recovers a cumulative wave dissipation of 6.3%, consistent with independent clinical estimates.

How stochastic, microscopic events generate deterministic, macroscopic properties is a fundamental question in physics. We address this question by developing a quantum master equation model for concentrated radical solutions, where random molecular collisions govern the magnetic properties of the system. Our theory reveals a simple mechanism: the first-order exchange contribution averages to zero over collisions, while the second-order term survives as an effective ferromagnetic coupling that enhances magnetization. The model captures the experimentally observed trends in magnetic behavior that deviate from conventional theories. Because the mechanism arises from statistical averaging, it may apply to a broader class of soft matter phenomena, including liquid crystals.

Acrylamide, with PubChem identifier CID=6579 is broadcasted to have four stable conformers contrasting with several journal publications characterizing only two or three. In this revision summary the discrepancy is clarified. Through very high precision density functional theory (DFT) calculations, three stable conformers and the three transition state barriers existing between them are verified to exist and validated with our own DFT calculations The most stable conformer is a planar molecular structure termed "sys" or "trans" in the literature. Meanwhile, a less stable structure termed "skew" pertains to two 3-dimensional structures that are energy-degenerate, but differ in their structure for being mirrored images of each other. Vibrational spectra, partial atomic charges, Cartesian coordinates, and Intrinsic Reaction Coordinate paths are summarized and recalculated with DFT at the $\omega$B97XD/Def2TZVPP level for the three stable acrylamide isomers: the sys/trans lowest in energy structure, and the two skew mirrored structures.

This paper investigates propagation of SH-waves in a layered composite structure consisting of a pre-stressed functionally graded magnetoelastic orthotropic layer overlying a pre-stressed functionally graded orthotropic half-space under the influence of gravity. The study introduces a physics-informed neural network (PINN) framework for the dispersion analysis of SH-waves in the considered composite medium. As a benchmark, an analytical solution to the dispersion relation is derived and used to validate accuracy and reliability of the proposed PINN formulation. In the developed PINN model, the phase velocity corresponding to a prescribed wave number is treated as a trainable parameter, enabling the determination of the dispersion relation associated with the nonlinear eigenvalue problem. The Adam optimizer is employed to minimize the loss function during the training process. In addition, the effects of different activation functions and network architectures, including variations in number of hidden layers and neurons, are systematically investigated to study the performance of the proposed framework. Error analysis is carried out using several norms, namely $L_1$, $L_2$, RMSE, relative absolute error, and $L_\infty$, to assess the accuracy of the predictions. Furthermore, the variation of phase velocity with wave number under different material parameters is investigated. The comparison between the analytical and PINN-based results demonstrates excellent agreement, confirming the effectiveness of the proposed deep learning approach for analysing dispersion relations in complex layered composite structures.

Chemicals are embedded in nearly every aspect of modern society, yet their production poses substantial sustainability concerns. Achieving a sustainable chemical industry requires detailed Life Cycle Assessment (LCA); however, current assessments face many unknowns due to limited, partly inconsistent, and untransparent data coverage since existing Life Cycle Inventory (LCI) databases account for only a tiny fraction of traded chemicals. Here, we introduce the Chemical RetrosYnthesiS for Transparent Assessment of Life-cycles (CRYSTAL) framework, which automatically generates consistent and transparent LCI data for organic chemicals based on their molecular structure using retrosynthesis and machine-learned gate-to-gate inventories. Using the predictive power of CRYSTAL, we create a consistent database for more than 70000 organic chemicals, comprising over 110000 transparent LCI datasets that quantify both feedstock and energy demands, together with associated auxiliary materials, biosphere flows, and waste flows. From this comprehensive database, we identify 50 key environmental hotspots driving high impacts of organic chemical production across multiple environmental categories and pivotal hub chemicals that are most critical for downstream chemical production. In providing this comprehensive data foundation, the CRYSTAL framework offers systematic guidance for targeted engineering and policy interventions. Its transparent, modular nature is designed to shift chemical LCA from a reliance on "unknown unknowns" to a collaboratively improvable mapping of "known unknowns".

In this work we study in detail all phases of the time evolution of a delta-like excitation in Erdös-Renyi (ER) random networks by means of the survival probability (SP): The initial decay of the SP (both, the fast decay followed by the power-law decay), the correlation hole regime (the regime between the minimum value of the SP and its saturation value), and the saturation of the SP. Specifically, we found that (i) the power-law decay of the SP and the time-averaged SP are proportional to $t^{-D_{2}}$ and $t^{-\widetilde{D}_{2}}$, respectively (where $D_2$ and $\widetilde{D}_2$ are the correlation dimension of the eigenstates of the randomly weighted adjacency matrices of the ER random networks and the correlation dimension associated with the initial state, respectively) and (ii) the relative depth of the correlation hole of the SP scales with the average degree $\langle k\rangle\approx np$ (here, $n$ and $p$ are the size and the connection probability of the ER random networks). In addition, we show that the eigenstates of the randomly weighted adjacency matrices of ER networks display clear multifractal properties.

This research uses 10 years of institutional data at a large public university in the USA to investigate trends in the undergraduate majors students declare, drop, and earn degrees, especially comparing physics to other disciplines. We find that physics has the lowest number of students of all science, technology, engineering, and math (STEM) disciplines and it also has the highest rates of attrition of students who declare a major. While many STEM disciplines have students migrating both in and out of those majors, physics primarily has a uni-directional migration of students out of the major. Furthermore, physics has the lowest percentage of women undergraduate majors. Using an equity framework, we view these findings as signatures of inequitable and non-inclusive culture. We suggest that important roles may be played by stereotypes such as the incorrect belief that physics is accessible only to brilliant men, the issue of first-year college physics courses failing to energize students, and apathy in large physics departments toward improving intentional recruitment and retention of physics majors.

The phenomenon of energy cascade is addressed in the case of free-shear flows, modeled with the equations for incompressible Newtonian fluids with mixed periodic and free-slip boundary conditions driven by an imposed mean shear profile. The rigorous results are proved for ensemble averages with respect to stationary statistical solutions in the sense of Foias and Prodi. The flow is decomposed into high and low horizontal wavenumber components, corresponding to scales perpendicular to the mean shear gradient, and the corresponding energy budget balances viscous dissipation, production by the mean shear, and transfer between scales. Exploiting the orthogonality of horizontal Fourier modes to control the shear-production term, we look for a cascade in which energy flows from the large scales, where it is fed by the mean shear, down to the small scales, where viscosity dissipates it. For any given wavenumber, we define an associated horizontal Taylor wavenumber of the high-wavenumber band above the given wavenumber. We prove two energy cascade results, for the energy flux and for a restricted energy flux that discounts possible energy loss to flow singularities, both valid for wavenumbers whose associated horizontal Taylor wavenumber lies above the viscous shear wavenumber and whose low-wavenumber energy dissipation rate is negligible compared to the total energy dissipation rate. On heuristic grounds, these rigorous conditions correspond precisely to an energy cascade in the range from the classical Corrsin scale down to the Kolmogorov dissipation scale, as expected in the conventional theory of turbulent shear flows.