Papers

Graphdiyne (GDY) is recognized as a compelling candidate for the fabrication of next-generation high-speed low-energy electronic devices due to its inherent p-type semiconductor characteristics. However, the development of GDY for applications in field-effect transistors (FETs), complementary metal-oxide-semiconductor (CMOS), and logic devices remains constrained by the relatively low carrier mobility reported in current experimental studies. Herein, the synthesis of layer-controlled hydrogen-substituted graphdiyne (HsGDY) films directly on silicon substrates under a supercritical CO2 atmosphere is reported, along with the fabrication of these films into HsGDY-based FETs. The transfer-free growth strategy eliminates performance degradation caused by post-synthesis transfer processes. The resulting HsGDY FETs exhibit a remarkable hole mobility of up to 3800 cm2 V-1 s-1 at room-temperature, which is an order of magnitude higher than that of most p-type semiconductors. The synthesis of transfer-free HsGDY wafers provides a new strategy for resolving the carrier mobility mismatch between p-channel and n-channel two-dimensional metal-oxide-semiconductor devices.

This study examines recent enrollment trends and their socioeconomic drivers within New Mexico's public school districts, with a specific focus on those serving low-income communities. Utilizing a mixed-methods geospatial framework, the research integrates longitudinal enrollment data, district poverty metrics from the Small Area Income and Poverty Estimates (SAIPE), and neighborhood context from the American Community Survey (ACS) from 2019 to 2023. The analysis reveals that while statewide K-12 enrollment has contracted, the decline is not uniformly distributed. Districts with high concentrations of low-income students have experienced more pronounced losses, though the relationship between poverty and enrollment change is complex and weak at the statewide level (r=0.011 in 2023). Spatial analysis shows a checkerboard pattern of growth and decline, even among adjacent high-poverty districts, indicating that local factors such as housing instability, labor market conditions, and programmatic changes are significant drivers. The study identifies 18 high-risk districts characterized by both high poverty and recent enrollment declines, which warrant targeted stabilization efforts. The findings underscore that while concentrated poverty is a critical challenge, it does not deterministically predict enrollment loss. Consequently, the authors propose a dual-track policy approach: immediate, flexible support for high-risk districts to ensure stabilization, coupled with long-term structural investments in all high-need districts to address underlying resource gaps and enable sustained educational offerings.

Tumor Obliteration by Resonant Amplification (TOR) was evaluated purely in simulation. Forward models in COMSOL, ANSYS, and ABAQUS used the same small-strain rheology, nonthermal/noncavitational limits, and an emulated closed loop (phase-locked actuation plus contrast/safety gating). Over >= 200 Monte Carlo runs per setup, TOR produced per-focus extinction in 2.6-3.2 s at approx. 0.85-0.90 J/cm^3, with selectivity Q = 39 +/- 5, peak temperature rise dT_max <= 0.2 deg C, and CEM43 << 1, consistent with strictly nonthermal operation. Primary indices indicated high spectral fidelity and intrinsic safety margin: MMI = 0.92 +/- 0.03, SSR = 14.6 +/- 3.1 (circumscribed) / 11.2 +/- 2.4 (infiltrative), and ATI <= 0.8 of the matrix-failure threshold. Simulated foci matched FE predictions within +/- 150 um. A unitless reality-adherence score comparing four observables to consolidated literature ranges yielded A = 95% +/- 2% (BCa 95%). Organ-specific estimates were 95.1% (pancreatic ductal adenocarcinoma), 96.0% (prostatic acinar adenocarcinoma), and 94.2% (invasive ductal carcinoma of the breast). The delivery stack - tungsten micro-needle (300-500 um) with 5-25 um tip excursions, phase lock, and amplitude gating - operated in a small-strain, noncavitational regime, keeping off-target strain below safety limits by design. Mechanistically, mode-selective collapse implies suppression of core vesicle biogenesis and nociceptor drive; rim scanning is constrained by healthy-referenced bounds, motivating compact neuroimmune readouts in future tests. Overall, the calibrated multiphysics results support a reproducible, spectrum-locked path from modeling to benchtop: deterministic extinction at low energy, high selectivity, and strict thermal neutrality, with pre-registered experiments planned to confirm the predicted safety and efficacy envelopes.

The highly non-convex optimization landscape of modern lens design necessitates extensive human expertise, resulting in inefficiency and constrained design diversity. While automated methods are desirable, existing approaches remain limited to simple tasks or produce complex lenses with suboptimal image quality. Drawing inspiration from the synaptic pruning mechanism in mammalian neural development, this study proposes OptiNeuro–a novel automated lens design framework that first generates diverse initial structures and then progressively eliminates low-performance lenses while refining remaining candidates through gradient-based optimization. By fully automating the design of complex aspheric imaging lenses, OptiNeuro demonstrates quasi-human-level performance, identifying multiple viable candidates with minimal human intervention. This advancement not only enhances the automation level and efficiency of lens design but also facilitates the exploration of previously uncharted lens architectures.

Magnetic reconnection, often accompanied by turbulence interaction, is a ubiquitous phenomenon in astrophysical environments. However, the current understanding of the nature of turbulent magnetic reconnection remains insufficient. We investigate the statistical properties of reconnection turbulence in the framework of the self-driven reconnection. Using the open-source software package AMUN, we first perform numerical simulations of turbulent magnetic reconnection. We then obtain the statistical results of reconnection turbulence by traditional statistical methods such as the power spectrum and structure function. Our numerical results demonstrate: (1) the velocity spectrum of reconnection turbulence follows the classical Kolmogorov type of $E\propto k^{-5/3}$, while the magnetic field spectrum is steeper than the Kolmogorov spectrum, which are independent of limited resistivity, guide field, and isothermal or adiabatic fluid states; (2) most of the simulations show the anisotropy cascade, except that the presence of a guide field leads to an isotropic cascade; (3) reconnection turbulence is incompressible in the adiabatic state, with energy distribution dominated by the velocity solenoidal component; (4) different from pure magnetohydrodynamic (MHD) turbulence, the intermittency of the velocity field is stronger than that of the magnetic field in reconnection turbulence. The steep magnetic field spectrum, together with the velocity spectrum of Kolmogorov type, can characterize the feature of the reconnection turbulence. In the case of the presence of the guide field, the isotropy of the reconnection turbulence cascade is also different from the cascade mode of pure MHD turbulence. Our experimental results provide new insights into the properties of reconnection turbulence, which will contribute to advancing the self-driven reconnection theory.

Liquid nitrogen (LN2) is a long-time favorite for physics demonstrations, with a large repertoire of crowd-pleasing experiments that are cornerstones in outreach efforts. While R1 universities usually have a ready LN2 supply for their Physics, Chemistry, and Biology departments, K-12 and two-year college teachers often have to go to specialty suppliers to obtain LN2, and also need access to expensive storage equipment. Dry ice (solid CO2) is available at many super markets, and as such its suitability as a substitute for LN2 was explored, with the results discussed below. At 77K (-196{\deg}C) LN2 is considerably colder than dry ice at 195K (-78.5{\deg}C), however some demonstrations are still viable.

Modern magnetic memory technology requires unconventional transverse spin current to achieve deterministic switching of perpendicular magnetization. Spin current in antiferromagnets (AFMs) has been long thought to be trivial as nonmagnets. Recently, a class of spin-splitting AFMs has been shown to be able to generate unconventional spin current for spin-orbit torque (SOT) applications. However, such AFMs requires specific symmetry breaking, and have been largely restricted by the limited material candidates. Here, we reveal universal spin fluctuation-induced unconventional transverse spin current in conventional spin degenerate AFMs at finite temperature by taking collinear (AFM) L10-MnPt as an example. The field-free switching of perpendicular magnetization was achieved via out-of-plane anti-damping-like SOT, leveraging the spin current with both y- and z-spin polarizations in heterostructures with MnPt functioning as spin current source. Based on symmetry analyses and experimental characterizations of current-induced spin torques, we find that the spin current generated by L10-MnPt exhibits anisotropy that varies with the current direction, enforced by the low symmetry magnetic point group when its Néel vector is along [110]. From a fundamental perspective, it would be intriguing to uncover a mechanism underlying the emergence of unconventional spin currents in spin degenerate antiferromagnets, which is highly desirable for spintronic applications.

Feynman path integrals (PIs) have found many uses in approximate quantum dynamics methods for efficiently calculating real-time quantum correlation functions. The PIs typically take the form of discrete imaginary-time slices along a closed path, where the slices form the “beads” of a ring polymer (RP) necklace. Some methods, such as centroid molecular dynamics (CMD), use the RP to generate an effective potential for the dynamics, while others, like RP molecular dynamics (RPMD), directly utilize the RP in real-time dynamics in order to incorporate approximate quantum effects. The standard, discretized bead forms of CMD and RPMD may require a large number of RP beads to obtain accurate results for systems with strong quantum effects and at low temperatures. In Paper I (J. Chem. Phys. 2025, 163, 144123), we introduced the bead-Fourier (BF)-CMD method, where we utilized the inclusion of a Fourier sine series to reduce the number of beads needed to converge the CMD effective potential up to eightfold. In this work, we extend RPMD to incorporate BF-PIs in the form of BF-RPMD. We study several implementations of the BF-RPMD method through the calculation of correlation functions for both linear and nonlinear operators. We found that the effectiveness of this method is sensitive to both the type of system and the form of the operators being studied. Our results show that this method is able to produce results on par with standard RPMD, with at worst a twofold and up to an eightfold reduction in the number of beads by including two to three Fourier components.

Recent advances in machine learning force fields (MLFFs) are revolutionizing molecular simulations by bridging the gap between quantum-mechanical (QM) accuracy and the computational efficiency of mechanistic potentials. However, the development of reliable MLFFs for biomolecular systems remains constrained by the scarcity of high-quality, chemically diverse QM datasets that span all of the major classes of biomolecules expressed in living cells. Crucially, such a comprehensive dataset must be computed using non-empirical or minimally empirical approximations to solving the Schrödinger equation. To address these limitations, we introduce the QCell dataset – a curated collection of 525k new QM calculations for biomolecular fragments encompassing carbohydrates, nucleic acids, lipids, dimers, and ion clusters. QCell complements existing datasets, bringing the total number of available data points to 41 million molecular systems, all calculated using hybrid density functional theory with nonlocal many-body dispersion interactions, as captured by the PBE0+MBD(-NL) level of quantum mechanics. The QCell dataset therefore provides a valuable resource for training next-generation MLFFs capable of modeling the intricate interactions that govern biomolecular dynamics beyond small molecules and proteins.

McMullen et al. [1] comment that the numerical simulations that explicitly include random velocity fluctuations “should exhibit a thermal-fluctuation-dominated range” consistent with the literature, so that our results (J. Fluid Mech. 1008, R2, 2025) [2] “contradict other results in the literature”. First of all, we would give an opposite example against this viewpoint: DNS results (that are badly polluted by numerical noises quickly, as mention in Section 2) implicitly include random numerical noises, but they also DO NOT exhibit a thermal-fluctuation-dominated range. In other words, DNS results in the literature qualitatively agree with ours at this point. In addition, we highly suggest that influences of numerical noises on statistics of turbulent flows given by ALL numerical approaches should be carefully checked, since numerical noises might have huge influences on statistics of chaotic systems (including turbulence), as pointed by Lorenz [3] in 2006. Detailed replies are given below.

This paper investigates how the spinor space of the electroweak gauge group $\text{SU}_{I}(2) \times \text{U}_{Y}(1)$ can be derived using recent geometric techniques within the real Clifford Algebra $\mathbb{R}_4 = \text{Cl}_4(\mathbb{R})$. Central to this approach is a novel procedure for constructing the spinor space of $\mathbb{R}_4$ directly, without complexification or matrix representation. In fact, the defining projector of the spinor space corresponds to the zero matrix in the defining representation of $\text{SU}(2)$, and hence this construction has no $2 \times 2$ complex matrix counterpart. We subsequently show that the spinor space of $\mathbb{R}_4$ naturally accommodates irreducible representations (irreps) for a single generation of chiral Standard Model leptons, including a sterile right-chiral neutrino. Left- and right-chiral particles arise due to the grade-parity of the irreps, explaining geometrically why weak isospin acts exclusively on left-chiral states. This manuscript shows how the real Clifford algebra $\mathbb{R}_4$ is the smallest algebra to house irreps for both the $\text{SU}_{I}(2) \times \text{U}_{Y}(1)$ gauge bosons and the Standard Model leptons. Simultaneously the geometric approach presents a way to compute interactions that does not depend on irreps, but which instead allows computations on the higher level of bosons and leptons. The emergence of the correct interactions directly from first principles highlights the promise of this framework not only for the geometric foundations of Electroweak Theory, but also for the Standard Model and Grand Unified Theories more broadly. This paper is the first panel of the Lepton Triptych, which will ultimately present the full Yang-Mills theory of the electroweak model based on these principles.

Electromagnetism is at the heart of the Standard Model, but despite all the successes of modern theory, our basic description of light traveling in free space remains unsatisfactory. The four bosons that compose light are introduced in a rather trivial way, simply by quantizing the (scalar and vector) potential amplitudes. This leads to quite a few conceptual problems linked to the two virtual photons, the longitudinal and scalar ones. Moreover, the spin of the photon is rather poorly handled by the conventional Quantum Electro-Dynamics theory. Therefore: what if the field's Lagrangian density, to some extent, would not be properly chosen? Here we look at these questions from a completely different point of view, bypassing the problems encountered in conventional theories. We choose a pragmatic approach that relies only on basic Condensed Matter like Quantum Mechanics, and a specific gauge fixing procedure for the potential field: we propose the concept of gauge duality, which leads to an original quantization scheme. Building on the Poincaré symmetries, all constants of motion are identified. Four bosons are introduced, responsible for a proper spin 1 pseudo-vector and parity and charge related operators. They emerge from scalar fields that can be viewed as generalized fluxes (in the sense of M. Devoret), with quantum conjugate virtual charges responsible for the "confinement" of light in space, within "virtual electrodes", somehow reproducing the holographic principle originally proposed for gravity. All observable properties of light in free space then arise from a specific choice of eigenstates (a procedure replacing here the Ward identity of Quantum Field Theory). Real photons are thus the "helicity" bosons, while virtual ones correspond to a "parity charge". Photon and anti-photon are (as expected) the same particle, linked through an internal gauge transformation.

Multi-reflection time-of-flight mass separators and spectrometers (MR-ToF MSs) are indispensable tools at radioactive ion beam (RIB) facilities. These electrostatic ion beam traps act as highly selective mass separators and high-precision mass spectrometers for rare and exotic nuclei. When well-tuned and designed to minimize higher-order flight-time aberrations, state-of-the-art MR-ToF MSs approach, and slightly exceed, mass resolving powers of \( m / \Delta m = 10^6 \). Achieving \( m / \Delta m > 3 \cdot 10^6 \) would provide the ability to resolve \( >90\% \) of all known isomeric states with half-lives above 10~\text{ms}. However, the ability to mass separate in all practical setups is limited by non-ideal conditions which place such resolving powers out of reach. To this end, we present a simulated analysis of these conditions in the newly proposed high-voltage MR-ToF MS for the Facility for Rare Isotope Beams (FRIB). It is expected to store ions at 30~\text{keV} beam energy and increase ion throughput by two orders of magnitude compared to current devices. Existing efforts to mitigate the effects of non-ideal conditions employed for current MR-ToF devices storing ions at \( <3~\text{keV} \) beam energy will already enable mass resolving powers approaching \( 10^6 \) for FRIB's high-voltage MR-ToF device. Simulations of newly proposed mitigation strategies show that even mass resolving powers approaching \( 10^7 \) might become feasible.

Motivated by the century-old problem of modeling the electron as a pointlike particle with finite self energy, we develop a new class of nonlinear perturbations of Maxwell's electrodynamics inspired by, but distinct from, the Born–Infeld theory. A hallmark of our construction is that the effective radius of an electric point charge can be reduced arbitrarily by tuning a coupling parameter, thereby achieving scales far below the Born–Infeld bound and consistent with the experimentally undetected size of the electron. The models preserve finite self energy for point charges while energetically excluding monopoles and dyons, a robustness that appears intrinsic to this class of nonlinear theories. Two complementary behaviors are uncovered: In the non-polynomial perturbations, the Maxwell limit is not recovered as the coupling vanishes, whereas in polynomial models the self energy diverges correctly, meaning that the Maxwellian ultraviolet structure is reinstated. A further subtlety emerges in the distinction between the prescribed source charge, imposed through the displacement field, and the measurable free charge arising from the induced electric field. In particular, the free charge and the self energy contained within any ball around the point charge tend to zero in the strong-nonlinearity or zero effective-radius limit, rendering a pointlike structure locally undetectable, both electrically and energetically. These findings highlight how nonlinear field equations reconcile theoretical prescription with experimental measurement and suggest a classical rationale for the effective invisibility of the electron substructure.

An electron scattering experiment to search for the trineutron state $^3n$ by reaction ${\rm ^4He}(e,~e'p\pi^{+})^{3}n$ is designed for the A1 facility at Mainzer Microtron. The detailed principles, setup, and simulation of this experiment are presented. With the momenta of the scattered electron, the produced proton and $\pi^+$ from the reaction measured by three spectrometers with their triple coincidence, the missing mass spectrum of $^3n$ can be obtained. The production rate of $^3n$ based on the cross section of the reaction and a MC simulation is estimated to be about 1.5 per day, which can provide a confidence level of the signal greater than 5$\sigma$ with a beam time longer than 16 days. According to a MC simulation that evaluates the energy losses of particles in materials and the performance of three spectrometers, the estimated resolution and the predicted shape of the missing mass spectrum are presented. This work provides a new experimental concept for the search for multineutron states in future experiments with an electron beam.

The terahertz (THz) band is a promising candidate for sixth-generation wireless networks, but its deploymen in outdoor environments is challenged by meteorological phenomena, particularly fog, which imposes variable and difficult-to-predict channel degradation. This article introduces dynamic channel model for the THz band explicitly driven by the time-evolving droplet size distribution (DSD) of fog, integrating real-time microphysical sensing to capture variations in the fog microstructure. Experimental measurements were conducted at 220 GHz and 320 GHz in a controlled fog chamber to achieve quasi-stationary states, and a larger room-scale setup to characterize dynamic, non-stationary fog evolution. The results confirm that channel power loss is overwhelmingly dominated by absorption rather than scattering, validating the use of the computationally efficient Rayleigh approximation below 1 THz. Statistical analysis revealed exceptionally high Rician K-factors, demonstrating that THz channels maintain strong line-of-sight stability even in dense fog. System-level performance analysis shows that degradation in bit error rate is driven by the slow, gradual evolution of the DSD, rather than fast multipath fading. This finding enables the reliable simplification of the THz fog channel into a near-Gaussian channel model with time-varying signal-to-noise ratio. This microphysics-aware approach established here provides the necessary foundation for developing adaptive system designs centered on SNR tracking for robust future THz networks.

Earth's energy imbalance at the top of the atmosphere is a key climate system metric, but its natural variability is poorly constrained by the short observational record and large uncertainty in coupled climate models. While existing ocean heat content reconstructions offer a longer perspective, they cannot separate the contributions of shortwave and longwave radiation, obscuring the underlying processes. We extend the energy budget record into the pre-industrial period by reconstructing the top-of-atmosphere radiation and related surface variables over the last millennium (850-2000 CE) by using data assimilation to combine proxy data and dynamics from a coupled climate emulator. Validation reveals skill in the reconstructed radiation fields, especially in the tropics. Results show a familiar last-millennium cooling trend, which coincides with persistent heat loss and a reduction in upper-ocean heat content. The cooling trend differs by season and latitude, and is associated with radiative anomalies suggestive of an eastward shift in Indo-Pacific convection. Following large volcanic eruptions, ocean heat content anomalies persist for 10-20 years on average, supporting previous evidence that the cooling trend was forced by decadally-paced eruptions. The reconstruction also reveals that the current rate of energy gain is unprecedented relative to the period before 1850.

Seasonal forecast of Arctic sea ice concentration is key to mitigate the negative impact and assess potential opportunities posed by the rapid decline of sea ice coverage. Seasonal prediction systems based on climate models often show systematic biases and complex spatio-temporal errors that grow with the forecasts. Consequently, operational predictions are routinely bias corrected and calibrated using retrospective forecasts. For predictions of Arctic sea ice concentration, error corrections are mainly based on one-to-one post-processing methods including climatological mean or linear regression correction and, more recently, machine learning. Such deterministic adjustments are confined at best to the limited number of costly-to-run ensemble members of the raw forecast. However, decision-making requires proper quantification of uncertainty and likelihood of events, particularly of extremes. We introduce a probabilistic error correction framework based on a conditional Variational Autoencoder model to map the conditional distribution of observations given the biased model prediction. This method naturally allows for generating large ensembles of adjusted forecasts. We evaluate our model using deterministic and probabilistic metrics and show that the adjusted forecasts are better calibrated, closer to the observational distribution, and have smaller errors than climatological mean adjusted forecasts.

The ultracold electron source is a unique approach to the generation of high-brightness electron beams. We give an overview of its development over the past 20 years, including the underlying physical principles, technical details and recent experiments, and give a flavor of the exciting prospects that the future may hold.

Over the past decade, there has been a growing trend toward integrating human factors (HF) into traffic flow models to better understand the complexities of human behavior and its impact on traffic dynamics. This research seeks to advance this trend by bridging the gap between traditional car-following models and the inherent variability of human driving behavior. By incorporating these elements, our models provide valuable insights into how driver behavior adaptation and risk-taking influence traffic flow dynamics. Specifically, the study proposes a model called Intelligent Driver Model Task Saturation that integrates human behavioral adaptations and risk-taking strategies into a modified version of the established Intelligent Driver Model, enriched by a cognitive layer based on Fuller's Task Capability Interface model. This amalgamation offers a perspective on the interplay between driver behavior adaptation when the driving task saturates and the risk-taking strategy of drivers. When total task demand exceeds task capacity, drivers may adapt their behaviors in accordance with Fuller's risk allostasis theory, aiming to mitigate risk to acceptable levels. The model was calibrated utilizing data from driving simulator scenarios involving both normal and distracted driving and naturalistic data, ensuring its sensitivity to diverse driving behaviors and adaptive behavioral changes. We investigated the model in terms of behavioral soundness and model fitting. Our results demonstrate that the model effectively incorporates endogenous mechanisms to explain both inter- and intra-driver heterogeneities in driving behavior. Additionally, it generates two plausible HF: risk-taking and behavior adaptation. The findings of this study suggest a step forward in achieving a more realistic representation of driving behavior adaptation and risk-taking strategies.

Understanding ionic transport in halide solid electrolytes is essential for advancing next-generation solid-state batteries. This work demonstrates the effectiveness of fine-tuning the Crystal Hamiltonian Graph Network (CHGNet) universal machine learning interatomic potential to accurately predict total energies, relaxed geometries, and lithium-ion dynamics in the ternary halide family Li$_{3}$YCl$_{6-x}$Br$_{x}$ (LYCB). Starting from experimentally refined disordered structures of Li$_{3}$YCl$_{6}$ and Li$_{3}$YBr$_{6}$, we present a strategy for generating ordered structural models through systematic enumeration and energy ranking, providing realistic structural models. These serve as initial configurations for an iterative fine-tuning workflow that integrates molecular dynamics simulations and static density functional theory calculations to achieve near-ab initio accuracy at four orders of magnitude lower computational cost. We further reveal the influence of composition (varied x) on the predicted phase stability and ionic conductivity in LYCB, demonstrating the robustness of our approach for modeling transport properties in complex solid electrolytes.

Ammonia is a promising zero-carbon fuel for industrial and transport applications, but its combustion is hindered by flame instabilities, incomplete oxidation, and the formation of nitrogen oxides. Accurate and detailed kinetic models are critical for designing optimal burners and engines. Despite numerous mechanisms published in recent years, large discrepancies remain between model predictions and experimental data, particularly for NOx species. In this work, we have reviewed the literature to obtain the most up-to-date and reliable thermochemical and kinetic parameters for most reactions present in ammonia combustion, and for reactions for which these parameters are not available, we performed high-level calculations to determine them. The purpose of this was to minimize the number of estimated parameters used in model development. A new, detailed kinetic mechanism was then generated with the Reaction Mechanism Generator (RMG). To ensure physical consistency, geometry optimizations were carried out for all hypothesized 'edge' species, and any non-convergent or non-physical structures were excluded. The resulting mechanism was tested against experimental laminar burning velocities, ignition delay time, flow reactor species profiles, and jet-stirred reactor data, and compared with five recent representative mechanisms. Recently developed bath-gas-mixture rules were applied to a number of key reactions in the mechanism, and we found this to result in better agreement with experiment for a number of modeling targets. While the mechanism does not reproduce all experimental results, it demonstrates improved robustness without parameter tuning, thereby reducing the risk of over-fitting and enhancing predictive reliability under conditions relevant to practical applications.

Maritime transportation systems (MTS) play a crucial role in ensuring the uninterrupted supply of essential goods and services, impacting the economy, border security and general welfare. However, MTS operations face disruptions from natural disasters, man-made disturbances, or cascading combinations of these events. These threats can disrupt trade routes, pose serious risks to personnel, and damage critical port infrastructure. In today's advanced technological world, MTS also face growing threats from cyberattack and terrorist attack. Given these evolving risks, efforts to ensure resilience in port operations must be more inclusive, reflecting the full spectrum of potential threats. This article focuses on two key aspects of maritime transportation supply chain resilience (MTSCR): operational excellence and system technology. It presents a structured review framework of relevant literature including research papers and government documents, to explore strategies for strengthening MTSCR. The article classifies the review findings and highlights vulnerable areas requiring future research.

Permanent magnet electron optics offer many advantages over electromagnets, and are being increasingly used in high-energy (GeV) electron accelerator designs. Here, we identify the advantages of permanent magnet electron optics for low-energy (keV) electron accelerators. We explore the applications of a class of designs based on axially magnetized permanent magnets, which offer a variety of advantages such as short focal lengths (few mm), while also preventing apparent emittance growth resulting from starting particles in a magnetic field. The proposed design philosophy is applied to an accelerator based on the ultracold electron source. The design is shown to be `emittance preserving' even for very short focal lengths (~5 mm) at an emittance level better than 1 nm-rad, while the short beamline (12 cm) limits space-charge effects. Two remedies for the mitigation of typical manufacturing and alignment challenges are considered. The performance of the design (related to parasitic aberrations) is enhanced by the proposed techniques. Applications of this design philosophy can improve the performance of ultrafast electron diffraction setups with minimal manufacturing effort.

Recent experiments in neuroscience reveal that task-relevant variables are often encoded in approximately orthogonal subspaces of neural population activity. These disentangled, or abstract, representations have been observed in multiple brain areas and across different species. These representations have been shown to support out of distribution generalization and rapid learning of novel tasks. The mechanisms by which these representations emerge remain poorly understood, especially in the case of supervised task behavior. Here, we show mathematically that abstract representations of latent variables are guaranteed to appear in the hidden layer of feedforward nonlinear networks when they are trained on tasks that depend directly on these latent variables. These learned abstract representations reflect the semantics of the input stimuli. To show this, we reformulate the usual optimization over the network weights into a mean field optimization problem over the distribution of neural preactivations. We then apply this framework to finite-width ReLU networks and show that the hidden layer of these networks will exhibit an abstract representation at all global minima of the task objective. Finally, we extend our findings to two broad families of activation functions as well as deep feedforward architectures. Together, our results provide an explanation for the widely observed abstract representations in both the brain and artificial neural networks. In addition, the general framework that we develop here provides a mathematically tractable toolkit for understanding the emergence of different kinds of representations in task-optimized, feature-learning network models.

We introduce CuPyMag, an open-source, Python-based framework for large-scale micromagnetic simulations with magnetostriction. CuPyMag solves micromagnetics with finite elements in a GPU-resident workflow in which key operations, such as right-hand-side assembly, spatial derivatives, and volume averages, are tensorized using CuPy's BLAS-accelerated backend. Benchmark tests show that the GPU solvers in CuPyMag achieve a speedup of up to two orders of magnitude compared to the CPU codes. Its runtime grows linearly/sublinearly with problem size, demonstrating high efficiency. Additionally, CuPyMag uses the Gauss-Seidel projection method for time integration, which not only allows stable time steps (up to 11 ps) but also solves each governing equation with only 1-3 conjugate-gradient iterations without preconditioning. CuPyMag accounts for magnetoelastic coupling and far-field effects arising from the boundary of the magnetic body, both of which play an important role in magnetization reversal in the presence of local defects. CuPyMag solves these computationally intensive multiphysics simulations with a high-resolution mesh (up to 3M nodes) in under three hours on an NVIDIA H200 GPU. This acceleration enables micromagnetic simulations with non-trivial defect geometries and resolves nanoscale magnetic structures. It expands the scope of micromagnetic simulations towards realistic, large-scale problems that can guide experiments. More broadly, CuPyMag is developed using widely adopted Python libraries, which provide cross-platform compatibility, ease of installation, and accessibility for adaptations to diverse applications.

Tungsten (W) is widely valued for its exceptional thermal stability, mechanical strength, and corrosion resistance, making it an ideal candidate for high-performance military and aerospace applications. However, its high melting point and limited room-temperature plasticity pose significant challenges for processing W using additive manufacturing (AM). Cold spray (CS), a solid-state AM process that relies on high-velocity particle impact and plastic deformation, offers a promising route for additive manufacturing of W, yet conventional CS fails to induce sufficient plastic deformation for effective bonding. In this study, we employ atomistic simulations to investigate the effect of ultrasonic perturbation in enhancing plastic deformation during CS of W, with a focus on acoustoplasticity-driven deformation mechanism. We show that ultrasonic perturbation leads to pronounced acoustic softening and promotes transient temperature elevation at the particle-substrate and particle-particle interfaces, thereby enhancing plastic deformation compared to non-ultrasound-assisted CS. Additionally, our results show that the coupled effects of acoustic softening and enhanced transient thermal activation lead to substantial improvements in interfacial bonding across a wide range of impact velocities, particle sizes, and ultrasonic parameters. Finally, we analyze the feasibility of ultrasound-assisted CS for manufacturing heterogeneous interfaces consisting of an equimolar Vanadium (V)-Tungsten (W) coating on a W substrate. Simulations reveal distinct mechanical behavior and dislocation densities compared to the homogeneous W on W CS configurations. Overall, this work highlights the potential of ultrasound-assisted cold spray as an effective strategy for manufacturing uniform coatings and engineered alloys, thereby addressing critical limitations in the additive manufacturing of refractory metals.

Aromaticity is a common chemical functionalities in bioactive molecules. In interstellar and circumstellar environments benzene and other small aromatics are considered the precursor for more complex prebiotic molecules and they have shown to potentially have rich ice-phase photochemistry. The availability of small organic molecules in prebiotic networks depends on their photostability in astrophysical environments preceding planet formation, particularly during the protoplanetary disk stage, as the disk composition is linked to the chemical make-up of planets and planetesimals. We study the ultraviolet (UV) photodestruction (120-160 nm) of five aromatic molecules in undiluted ices and, for selected cases, in astrophysically relevant ice matrices (H2O, CO, CO2). For each ice, we measure the destruction cross sections as a function of photon exposure. In undiluted ices, aromatic molecules exhibit substantially lower photodestruction cross sections (sigma < 10-19 cm2) than aliphatic hydrocarbons, including cyclohexane, (sigma = 2.8-4x10-18 cm2). Furthermore, neither substituent nature nor size affects the aromatic stability in pure ices, suggesting that the strong intermolecular interactions among aromatic molecules provide protection against VUV exposure, even with small to mid-sized ring substituents. In mixed ices, the photodestruction and reactivity of aromatic molecules (sigma = 2.5-6.1x10-18 cm2) increases by more than an order of magnitude, but are still lower than in the gas-phase. We attribute this to a weaker cage effect and matrix-specific interactions. We use the experimental photodestruction cross sections to estimate the lifetime of aromatic molecules in protoplanetary disks, denileating the disks regions in which aromatic photochemistry is expected to be the most active.

Strain-mediated magnetoelectric (ME) heterostructures enable electric-field control of magnetism and are promising for ultra-low-power spintronic logic. Yet achieving spatially selective, low-voltage control in thin films and quantifying ME coupling across the full ferroelastic loop remains challenging. Here, we investigate sub-micrometer Ni/BPZT thin-film devices with laterally patterned gates that localize in-plane strain beneath the Ni stripe and modulate its magnetization. We use anisotropic magnetoresistance to measure magnetization changes across the ferroelastic loop under different magnetic bias fields. Combined with Multiphysics strain simulations and micromagnetic modeling, this provides a quantitative framework that captures the convolution of ferroelastic and magnetoelastic nonlinearities and provides critical insight for device design, while enabling multi-state, bias-field-free magnetization control for non-conventional computing. The extracted coupling coefficient in linear range is 1.3 mT/V across a 700 nm gap, with a clear pathway to improving voltage efficiency through device scaling, establishing a scalable CMOS-compatible platform for energy-efficient spintronic devices.

We present a time-dependent framework that combines a hybrid Gaussian-FEDVR basis with a multicenter grid to simulate strong-field and attosecond dynamics in atoms and molecules. The method incorporates the construction of the orthonormal hybrid basis, the evaluation of electronic integrals, a unitary time-propagation scheme, and the extraction of optical and photoelectron observables. Its accuracy and robustness are benchmarked on one-electron systems such as atomic hydrogen and the dihydrogen cation ($\text{H}_{2}^{+}$) through comparisons with essentially-exact reference resutls for bound-state energies, high-harmonic generation spcetra, photoionization cross sections, and photoelectron momentum distributions. This work establishes the groundwork for its integration with quantum-chemistry methods, which are already operational but will be detailed in future work, thereby enabling ab initio simulations of correlated polyatomic systems in intense ultrafast laser fields.