Papers

Reliable assessment of tropical cyclone risk is limited by the short and spatially uneven historical record, especially for rare, high-intensity landfalls that dominate insured loss. We present WHITS, the Wind-focused Hurricane Interactive Track Simulator, a non-parametric semi-Markov model that generates a 10,000-year global synthetic catalog of tropical cyclone tracks. WHITS builds new storms by resampling variable-length segments of historical tracks, with segment choices conditioned on local wind speed, location, storm age, and forward motion. This structure preserves both the statistical grounding of the historical record and a form of system memory along realized storm pathways. The resulting catalog reproduces observed track density and the annual probability that hurricane- or typhoon-force winds pass a given location across the major tropical cyclone basins. WHITS is designed to support climate risk analysis, insurance applications, and extreme event research over operational planning time scales by providing a large, low-bias sample of physically plausible storms. This low-bias design is important for applications in which introduced bias cannot simply be corrected after the fact and small errors in storm frequency, track, or intensity can substantially affect loss estimates. Because loss accumulates along the storm path, track geometry matters: storms that loop, stall, or turn sharply can produce damage footprints that differ fundamentally from straight-moving events. By preserving these irregular shapes while greatly expanding the sample of plausible storms, WHITS provides risk analysts, insurers, and coastal planners with information unavailable from observations alone.

We extend the tensor-product direct solver from the Laplacian to the Schrödinger operator $-\Delta + V$. When the potential $V_1$ is separable, the operator $-\Delta + V_1$ is inverted or exponentiated at cost $O(N^{1+1/d})$ in $d$ dimensions via per-axis eigendecomposition. On a single NVIDIA A100 GPU, this costs less than one second for $10^9$ degrees of freedom in 3D. For non-separable potentials $V = V_1 + V_2$, the same solver provides a preconditioner $(-\Delta + V_1)^{-1}$ for the preconditioned conjugate gradient (PCG) method and a propagator for operator-splitting time integrators. For bounded $V_2$, we prove that the preconditioned operator has a bounded condition number and a clustered spectrum with at most finitely many outlier eigenvalues, independently of the mesh size, and also independently of the domain size when $V_1$ is a confining potential. This explains the mesh- and domain-independent PCG iteration counts observed in practice. We apply this method to ground state computation via inverse iteration for linear problems and via the $a_u$ gradient flow for Gross–Pitaevskii energy in 3D, and also Hamiltonian simulation via the approximated qHOP and Magnus-2 splitting methods from 3D to 9D on a single NVIDIA GH200 GPU.

A ball rolled over a shallow step will experience an increase in velocity along the direction perpendicular to the step. This causes a deflection in the ball's trajectory. In this paper we derive the equations that describe the motion of a ball rolled over a shallow step and present the results of our experimental test. This simple demonstration can be used in any classroom where the physics teacher has access to a ball and a stack of papers. Prior work has shown that a ball rolled over an edge can maintain its speed, as is commonly assumed, but it can also experience an increase or even decrease in speed. The ball can either roll without slipping while it is in contact with the edge, or else begin to slip before it leaves the edge. In this paper we will consider the case where the ball rolls without slipping the entire time it is in contact with the step edge, then contacts a lower platform. We work with shallow step heights relative to the radius of the ball so that the motion of the ball is easy to observe at all times, and so that the ball does not bounce when it encounters the lower platform. These shallow step heights mean that we can assume the ball does not slip as it moves over the edge.

We present calculations of the millielectronvolt-scale x-ray scattering spectra of multilayered dynamic-compression targets comprising an unstructured ablator layer and a crystalline, textured sample layer. Our model builds on the classic formulation of x-ray thermal diffuse scattering by Warren [B. E. Warren, Acta Crystallogr. 6, 803 (1953)] and includes both elastic and first-order (single-phonon) inelastic scattering contributions to the dynamic structure factor $S(\mathbf{q},\omega)$. We focus on the umklapp scattering regime (i.e., at momentum transfers outside the first Brillouin zone) where the ablator scattering that threatens to overwhelm the inelastic scattering from the crystalline layer of interest is suppressed. We show that, despite the considerably more complex structure of the inelastic scattering spectra in this intermediate-$q$ regime, it is still possible to reliably deduce the temperature of the crystal using Dornheim's Laplace-transform–based formalism [Dornheim et al., Phys. Plasmas 30, 042707 (2023)], regardless of the details of the sample's texture.

Entropy stabilization of the compressible Euler system is achieved by adapting the averages that are applied to the density and internal energy variables. The approach achieves non-linear robustness despite the use of simplified symmetric means (e.g., arithmetic, geometric, or harmonic evaluations), including their related expansions for asymptotic entropy conservation. The proposed formulation works via centralized convective terms and can naturally adhere to additional structures of the flow equations such as kinetic-energy- and pressure-equilibrium-preservation.

Cone-beam CT (CBCT) is routinely acquired during radiotherapy for patient setup, but its quantitative reliability is degraded by scatter, noise, and reconstruction artifacts, limiting Hounsfield Unit (HU) accuracy. We propose EPC-3D-Diff, a novel conditional 3D latent diffusion framework for volumetric CBCT to CT synthesis that introduces a projection domain equivariance loss derived from acquisition physics. Unlike common image domain equivariance, we exploit the fact that an in plane rotation of the volume corresponds to an angular shift in its projections. During training, we enforce this relationship by forward projecting rotated synthesized CT volumes and matching them to appropriately angle shifted projections of the paired target CT, yielding a physics consistent equivariance constraint integrated into the diffusion objective. To capture full 3D context efficiently, conditional diffusion is performed in a compact latent space learnt by a lightweight 3D autoencoder, preserving axial depth while downsampling in plane resolution for stable training. We validate on a paired head CBCT/CT phantom dataset, including repeat scans, and paired clinical data using patient wise splits, and perform single and mixed domain training, ablations, and comparisons with diffusion and CycleGAN. EPC-3D-Diff generalizes well and achieved substantial improvements, +7.4 dB (phantom) and +1.8 dB (clinical data) in PSNR compared to state of the art methods, alongside improved SSIM and HU accuracy, within tissue boundaries. Overall, EPC-3D-Diff improves robustness and physics consistency, supporting HU aware synthesis for downstream radiotherapy workflows. The open source code for EPC-3D-Diff is available at https://github.com/ALZAHRAALTALIB/EPC-3D-Diff.

The jellyfish Clytia hemisphaerica possesses astounding regenerative capacities and is able to close even large wounds within a few hours. This rapid pace of wound closure raises the question whether tissue mechanics, rather than tissue restructuring or cell proliferation, might be underlying the process. We tested this possibility by asking if simple pre-strains within the jellyfish umbrella would be capable of initiating wound closure in a jellyfish body geometry. To this end, we employed an in silico spring lattice model, a coarse-grained model of elastic materials which has previously been established to study tissue mechanics problems. We found that, using radially contractile (but not radially extensile) strains, wound closure can indeed be initiated across a wide range of conditions. This is even true for large cut sizes and, hence, small pieces of remaining tissue material, in good agreement with the experimental findings. Finally, we derived an analytical expression for the expected amount of achievable closure as a function of the residual material angle. These results establish important foundations for further investigations of the biophysics underpinning jellyfish regeneration.

Weak shock theory based on cylindrical blast waves has been used to interpret meteor infrasound, but it has not been systematically benchmarked against a non-ablating hypersonic source with independently known parameters. The objective of this study is not to propose a new theoretical framework, but to evaluate the operational validity of the existing suite of blast radius formulations against a high-fidelity ground truth dataset. The OSIRIS-REx Sample Return Capsule reentry on 24 September 2023 provides such a benchmark because the capsule geometry, trajectory, and infrasound emission points are constrained from mission data and ray tracing, reducing source-side uncertainty associated with ablation. Using observations from 39 infrasound stations, this benchmarking study evaluates six published blast radius (R_0) formulations and three weak-shock transition coefficients (C) within a stratified atmospheric propagation model to predict signal period and peak overpressure. The benchmarking identifies the Sakurai formulation as the best-performing formulation for non-ablating bodies, with the Jones/Plooster formulation performing comparably when a physically appropriate C is adopted. Sakurai and Jones/Plooster yield linear-period median absolute percentage residuals of 9% and 11%, respectively. The period predictions show only weak sensitivity to C at these propagation distances. The Mach-diameter approximation commonly used in meteor studies overestimates R_0 by more than a factor of 3 in the absence of ablation. These results establish a performance baseline for applying cylindrical blast wave theory to non-ablating hypersonic bodies and demonstrate that the signal period is a robust observable for constraining R_0.

Many complex systems combine dense background structure with sparse contextual information. We introduce a diffusion-based framework for analyzing how sparse condition-specific layers reshape diffusion geometry in multilayer hypergraphs. Each layer is represented as a weighted hypergraph, layers are coupled through shared entities, and random walks on the coupled system induce multiscale diffusion distances between nodes. We apply the framework to disease-conditioned gene networks by coupling a dense MSigDB functional gene-set layer to sparse disease-specific DGIdb drug-gene hypergraphs, with disease-associated drugs selected from DDDB and HumanNet-GSP used to define external gene weights. Across Bipolar Disorder, Schizophrenia, Leukemia, and Breast Cancer, the disease-specific layer contains less than 2 percent of genes in the coupled system, yet substantially changes diffusion distances and community structure. Centrality analysis suggests that this disproportionate effect arises because DGIdb-associated genes occupy influential positions in the MSigDB-derived functional network. The resulting diffusion-derived communities are stable under subsampling and show coherent post hoc functional enrichment, including signaling and neurotransmission categories in neuropsychiatric diseases and immune, translational, and metabolic categories in cancer-associated diseases. Community-level comparisons further reveal disease similarities not reducible to direct DGIdb gene overlap, including a Breast Cancer-Schizophrenia relationship consistent with recent biomedical evidence. These results show that sparse contextual layers can induce interpretable nonlocal changes in higher-order network geometry.

We present the Neutron Scattered Spectra Tool (NeSST), an open-source Python package for rapidly constructing primary and singly scattered neutron spectra from inertial confinement fusion (ICF) implosions. NeSST evaluates primary spectra for deuterium-tritium (DT), deuterium-deuterium (DD) and tritium-tritium (TT) reactions. Differential and total nuclear cross sections are read directly from Evaluated Nuclear Data File (ENDF) libraries. This enables elastic ($n$D, $n$T) and inelastic [D$(n,2n)$p, T$(n,2n)$D] scattering from DT fuel, as well as scattering from additional ablator materials such as $^{12}$C, to be treated within a common framework. Relativistic corrections to elastic scattering kinematics are included. Areal density asymmetries are incorporated through a Legendre mode expansion of the neutron-averaged projected areal density, allowing the spectral signatures of implosion non-uniformities to be computed and fitted. The effect of scattering ion velocities on the neutron backscatter edge shape is handled through pre-computed ion-velocity-dependent scattering kernels. A synthetic neutron time-of-flight (nToF) module converts energy spectra into detector signals with a full forward model that includes configurable instrument response functions (IRFs), energy-dependent scintillator sensitivity, and beamline attenuation. The code is publicly available at https://github.com/aidancrilly/NeSST

Sensitive detection of terahertz (THz) radiation is fundamental to progress in spectroscopy, advanced wireless communication, and the realization of emerging quantum technologies. However, the intrinsically low photon energies in the THz range combined with thermal background radiation tend to constrain detector performance when operating at ambient temperatures. Here, we demonstrate efficient room-temperature THz detection based on nonlinear upconversion in the organic crystal N-benzyl-2-methyl-4-nitroaniline (BNA) to resolve frequencies from 1 to 7.5 THz. The system encompassing spectral filters and a single-photon counter achieves an overall detection efficiency of 2% for sum-frequency generated photons. This enables the detection of a train of 50 000 terahertz pulses carrying, on average, fewer than 0.04 photons per pulse, with a signal-to-noise ratio of unity. At a higher flux, when ~60 photons per pulse impinge on the BNA crystal, the per-pulse detection probability reaches 50%. After accounting for loss mechanisms in the setup, the nonlinear THz-to-near-infrared conversion efficiency in BNA exceeds 75%. These results demonstrate the feasibility of quantum experiments relying on single-photon-level THz detection via upconversion in nonlinear crystals in ambient conditions.

Nanomaterials stacked on-demand, such as rotationally assembled two-dimensional (2D) van der Waals (vdW) layered compounds, provides a versatile platform for quantum simulation and the exploration of exotic electronic phases. Currently, however, such nanoassemblies remain largely confined to inefficiency, manually operated process, limiting their potential for probing emergent physical phenomena. There is a pressing need in the field for high-precision, automated assembling techniques, especially for the scalable fabrication of 2D twistronic heterostructures. Here, we present an intelligent automation system dedicated to the fabrication of van der Waals stacks, following the state-of-the-art protocol for dry transfer of exfoliated 2D materials. The system further employs metadata generated from each automated stacking procedure to perform reinforcement learning, thereby continuously bettering its performances. As a concrete demonstration, we fabricate twisted bilayer graphene (TBLG) – known for its challenging preparation – and exhibit its unconventional superconductivity near the magic angle. Our work may pave the way for high-throughput fabrication of low-dimensional nanomaterials including twistronic heterostructures, where integrating data mining and artificial intelligence can accelerate the discovery of novel physical phenomena.

Models of opinion dynamics aim to capture how individuals' opinions change when they interact with each other. One well-known model of opinion dynamics is the Deffuant–Weisbuch (DW) model, which is a type of bounded-confidence model (BCM). In the DW model, agents have pairwise interactions, and they are receptive to other agents' opinions when their opinions are sufficiently close to each other. In this paper, we extend the DW model by studying it on networks with heterogeneous and adaptive edge weights between pairs of agents. These edge weights govern the interaction probabilities between the agents and thereby encode the idea that people are more likely to communicate with individuals with whom they have previously compromised or had other positive interactions. We prove theoretical guarantees of our adaptive edge-weighted DW model's convergence properties, the long-time dynamics of its edge weights, and the model's associated “effective graph", which is a time-dependent subgraph that includes edges only between agents that are receptive to each other's opinions. We support our theoretical results with numerical simulations of our adaptive edge-weighted DW model on a variety of networks and find that including adaptive edge weights yields different qualitative dynamics for different types of networks. In particular, for small confidence bounds, we observe that incorporating adaptive edge weights decreases the convergence time for dense networks but increases the convergence time for sparse networks.

We study whether multimodal large language models (MLLMs) can leverage crystallographic plane indices (Miller indices) as a structured latent representation for reasoning about fracture geometry. We formulate Miller indices $z = (h,k,l)$ as a latent variable governing idealized planar fracture and evaluate two complementary capabilities: (i) latent inference, where the model maps visual observations to plane hypotheses under physically valid conditions, and (ii) latent applicability assessment, where the model determines whether such a representation is meaningful for a given fracture image. Through extensive experiments spanning synthetic data, controlled 2D–3D geometric pairs, and real-world fracture images across multiple material classes – including ceramics, glass, metals, and concrete – we show that MLLMs can reliably perform latent inference in idealized settings and, critically, can reject the latent representation when the underlying physics does not support it. As an exploratory extension, we further examine AI-generated fracture sequences and observe qualitatively plausible brittle-fracture progression behaviors, suggesting that multimodal generative models may encode partial implicit physical priors related to material failure dynamics. These results suggest that MLLMs can act as physics-aware reasoning systems conditioned on structured latent priors, provided that the domain of validity is explicitly modeled.

TiO2 ferroelectric field effect transistors (FeFETs) with HfZrO2 (HZO) ferroelectric dielectric layers and bottom gate topology are fabricated for applications in neuromorphic systems. Two sets of devices are fabricated with different gate topologies by varying the thickness of the ferroelectric gate stack. Different device architectures are studied by varying the source drain length (LSD) and gate length (LG). The devices have high on/off ratios up to 10^7 with low leakage off currents <10^-12 A. Repeated cycle testing shows high reliability and a stable memory window. The devices have large memory windows ranging from 3 to 8 V.

Class imbalance is a fundamental challenge in medical image segmentation, where frequent classes typically dominate training at the expense of rare classes. Loss-based approaches mitigate imbalance by reweighting the per-pixel loss within the batch, while sampling strategies control which images enter the batch. Yet neither explicitly controls which classes appear within the batch, leaving rare-class exposure only partially rebalanced. In this work, we adopt episodic sampling from few-shot learning to promote class-balanced batch construction in a fully supervised setting. We decouple episodic sampling from its conventional metric-learning context and evaluate it in body composition segmentation in CT. We compare episodic sampling against random and weighted sampling on nine muscle and adipose tissues, derived from 210 scans of the public SAROS dataset. Training is performed under full- and low-data regimes, with additional comparisons under matched training iteration budgets. Under full-data training, all three strategies performed comparably (mean Dice 0.882 for episodic, 0.878 for random and weighted). Under low-data training, episodic sampling outperformed random and weighted (0.787 vs. 0.758 and 0.762), driven by a 12-fold difference in training iterations. Under matched training budgets, random and weighted overfit earlier, while episodic improved for approximately three times more iterations before plateauing. Our findings identify the training iteration budget as under-recognized confound in sampling strategies, motivating iteration-aware evaluation protocols for small datasets. Furthermore, the residual advantage of episodic sampling is consistent with an implicit regularization effect of class-balanced batches, offering a low-cost, model-agnostic strategy for class-imbalanced medical image segmentation. Code is available at https://github.com/iasonsky/episodic-sampling.

We show that optical activity in atomic media can generate multidimensional correlations in frequency, polarization and spatial degrees of freedom, allowing us to observe frequency shifts as image rotations. Specifically, we demonstrate a pump-probe scheme, where optical pumping with circularly polarized light generates macroscopic magnetization in an atomic vapor, associated with frequency-dependent circular dichroism and birefringence. A vortex probe beam, characterized by spatially varying polarization, maps this optical activity onto the spatial structure of the transmitted light. Measuring the intensity profile in a suitable polarization component then allows us to perform spatially resolved polarization spectroscopy. On resonance we observe a rotation with a slope in the order of 98 mrad per MHz. These findings may find applications in image-based laser-locking, high-precision spectroscopy, magnetometry, and the generation of hybrid entanglement.

Driven by the growing demand for miniaturized spectrometers for in-situ analysis, and point-of-care diagnostics, conventional spectrometers are often constrained by bulky architectures and pathlength-limited spectral resolution. Achieving high-resolution, single-pixel computational spectrometers is therefore critical for the realization of compact, on-chip systems. Here, we report a single-pixel spectrometer enabled by a single 2D material; few-layer GaSe-based photodetector, in which the Schottky barrier height modulation, governed jointly by applied bias and optical excitation, provides an efficient mechanism for spectral encoding without the need for bulky dispersive elements. The device exhibits a high peak-wavelength accuracy of ~0.78 nm across a broad operational bandwidth (300-700 nm) within a compact footprint of ~100 um^2 and resolves closely spaced spectral features with separations down to ~5 nm. The device operates at low bias (+/- 4V) with an ultralow dark current density ~0.3 pA/um^2 at 4V bias. These results establish a simple, scalable route toward compact, cost-effective spectroscopic systems for on-chip spectral sensing and portable hyperspectral imaging applications.

In a tidal disruption event (TDE), a star is destroyed by the tidal field of a supermassive black hole. The stellar debris is initially placed on highly elliptical orbits, and a longstanding question in TDE theory is: How does the stellar debris circularize into a disc and accrete? The originally proposed answer to this question is self-intersection shocks, where relativistic apsidal precession results in a strong collision between the incoming and outgoing material. However, global simulations of TDEs tend to find enhanced hydrodynamical dissipation prior to any intersections of the debris orbits, with the material “fanning out” into a wide-angle and partially-unbound outflow upon passing through pericenter. We show that this dissipation is numerical in origin and arises from a combination of 1) the change in the kinematics of the debris as it passes through pericenter, with its velocity profile along the stream transitioning from strongly diverging pre-pericenter to strongly converging post-pericenter, and 2) the dependence of numerical algorithms (viscosity switches for particle-based methods and Riemann solvers for Godunov-based schemes) on the diverging vs. converging nature of the fluid. We support this conclusion with analytical and numerical modeling. We discuss possible resolutions to these issues as well as the implications of our findings in the context of observations.

We investigate magnetic-energy dissipation, current-sheet dynamics, and nonthermal particle acceleration in three-dimensional relativistic reconnection in an electron–ion plasma with a realistic mass ratio. Using particle-in-cell simulations of a double Harris current sheet, we explore a range of ion magnetisations and guide-field strengths to determine how guide fields regulate the overall magnetic energy dissipation. At low magnetisation, $\sigma_i=0.1$, increasing the guide field suppresses reconnection: magnetic-energy dissipation decreases, the growth of tearing modes is weakened, and nonthermal particle acceleration remains inefficient. At higher magnetisations, $\sigma_i=1$ and $\sigma_i=5$, the behaviour changes qualitatively. In the zero-guide-field case, strong drift-kink activity corrugates and broadens the current sheet, inhibiting efficient tearing-mediated reconnection. A weak guide field suppresses this drift-kink-driven disruption, allowing the current sheet to remain laminar and more coherent and thereby enhancing magnetic-energy dissipation. However, once the guide field becomes too strong, reconnection is again suppressed: the onset is delayed, tearing activity weakens, current-sheet compression is reduced, and the system retains a larger fraction of its initial magnetic energy. This non-monotonic behaviour is reflected consistently in magnetic-energy evolution, Fourier analysis of the tearing and kink modes, current-sheet thickness, and nonthermal particle acceleration. The most dissipative cases are not necessarily the zero-guide-field runs, but rather those in which the guide field balances drift-kink suppression without strongly impeding the tearing modes. Our results show that the overall system evolution is controlled not only by the available magnetic energy, but also by the guide-field-regulated morphology and stability of the reconnecting current sheet.

International scientific collaboration is organized primarily by researcher-level logic and network dynamics in which scientists seek partners at the frontier of their field with little consideration of national affiliation. Research security policies that raise the friction costs of international collaboration are often assumed to operate against this logic, aiming to produce selective withdrawal from sensitive fields to deny knowledge transfer to specific countries. This paper tests that assumption against eight years of bibliometric evidence from China's scientific output from 2018 through 2025 across 27 Scopus subject categories. China's internationally co-authored publication share declined universally across all fields after 2018, consistent with China's push for domestic capacity maturation, but the pattern of decline is inconsistent with the deterrence prediction. Fields with high dual-use proximity, including computer science, materials science, physics and astronomy, chemical engineering, and engineering, showed the least contraction (3-6 percentage points), while fields with minimal dual-use salience (psychology, nursing, dentistry, and health professions) showed the steepest declines (8-24 percentage points). The findings suggest that governments raised the friction level but did not redirect the network nor suppress collaboration in sensitive areas. Researchers practiced adaptive participation, maintaining frontier collaborations while adjusting the form and language of their engagement. This pattern matches what the invisible college theory and the Ostrom knowledge commons framework predict.

With the rapid development of information and communication technologies in recent years, the transmission capacity of single-core optical fibers has nearly reached its physical limit. Space-division multiplexing based on multi-core fibers offers an effective solution to this bottleneck. Multi-core fibers feature high integration and large transmission capacity, and their unique structural characteristics also give them special value in fiber-optic sensing applications. Among various types of multi-core fibers, coaxial dual-core fibers (CDCFs) have shown promising performance in particle trapping, signal emission, and spectral analysis. To enable reliable interconnection between different types of multi-core fibers and single-core fiber arrays, this paper presents the design and fabrication of a fan-in device for coaxial dual-core fibers with different core diameters. The proposed method relies solely on cold-processing techniques and does not require any fusion splicing or thermal processing. The device is implemented on a V-groove substrate. Through structural design, fabrication, and experimental characterization, the average insertion loss of the ring core and the central core at a wavelength of 980 nm is measured to be 2.15 dB and 1.25 dB, respectively, demonstrating the successful fabrication of a coaxial dual-core fan-in device.

The advent of Extremely Large Telescopes ELTs, ground-based optical or infrared observatories with primary mirrors exceeding 20 m heralds a transformative epoch in observational astronomy. This article examines the dawn of this new era and the three upcoming facilities in the optical infrared band the Giant Magellan Telescope GMT, the Thirty Meter Telescope TMT, and the European Extremely Large Telescope ELT. This article will focus on the ELT, while a sequel will cover GMT and TMT. We describe the key technological breakthroughs enabling its construction, most notably the segmented mirror design, advanced adaptive optics AO, and laser guide star systems. These innovations will deliver more than an order of magnitude leap in light-gathering area and spatial resolution, providing image sharpness exceeding that of spacebased telescopes for widefield observations. The scientific impact of the ELT is profound and multifaceted. We discuss its inception and construction milestones and explore its potential to directly image and characterize the atmospheres of Earth like exoplanets, searching for biosignatures, and trace the formation of the first stars, galaxies, and supermassive black holes. This paper concludes that ELTs are not mere incremental improvements but foundational instruments that will redefine the frontiers of astrophysics, address some of science's most enduring questions, and inevitably lead to discoveries beyond current prediction.

Population aging has made the daily mobility of older adults an increasingly important issue for urban planning and transport research. While previous studies have examined elderly mobility in relation to accessibility, active aging, and transport inclusion, less attention has been paid to whether older adults living in different residential contexts experience equal spatial activity opportunities. This study addresses that gap by comparing elderly residents in three wards of Nagoya, namely Naka, Showa, and Moriyama, which represent different rent levels and urban opportunity contexts. Using stay events derived from GPS-based mobility data, we construct a 500 m x 500 m grid-based analytical framework and examine spatial activity opportunity fairness through four dimensions: activity space, opportunity context, opportunity exposure, and semantic structure. The results show clear inter-group differences. Naka residents display the strongest concentration in the urban core, Showa residents occupy an intermediate position, and Moriyama residents exhibit a more dispersed and corridor-oriented pattern. Citywide semantic opportunities are unevenly distributed and concentrated around the urban core. Event-level exposure also shows a clear gradient, with Naka residents encountering the highest opportunity density, followed by Showa and Moriyama. Category-specific analysis further reveals that Naka residents are more strongly embedded in Retail/Service and Food/Drink opportunity structures, whereas Showa and Moriyama show relatively stronger Transit-related components. These findings suggest that spatial activity opportunity inequality among elderly residents is not one-dimensional, but multi-dimensional and category-specific. The paper contributes an event-based perspective for examining experienced urban opportunity inequality under differentiated residential contexts.

One central theme in quantum photonics is tailoring the interactions between atoms/spins and their electromagnetic (EM) environments. Considerable effort has focused on engineering spontaneous emission by shaping EM environments, known as the Purcell effect. However, photonic environment control of pure dephasing, which is a complementary paradigm of non-unitary atom/spin couplings with EM environments, remains largely unexplored. Here, we introduce a nanophotonic approach to modify qubit pure dephasing dynamics. Unlike Purcell engineering that tailors photonic environments at qubit resonance frequencies (typically optical/near-infrared), we develop ultra-subwavelength spin noise metasurfaces for efficient broadband control of low-frequency (e.g., $\sim$MHz) photonic environments far off-resonant with atoms/spins for dephasing engineering. We experimentally demonstrate our approach using lithographically defined CoFeB metasurfaces and shallow nitrogen-vacancy (NV) centers in diamond. Instead of modified spontaneous emission, we observe modified NV pure dephasing dynamics near different spin noise metasurfaces. We further isolate metasurface-controlled dephasing from other dephasing mechanisms (e.g., spin bath) by measuring the NV ensemble dephasing noise spectrum with dynamical decoupling spectral decomposition techniques. Our results establish a new frontier in engineering quantum light-matter interactions with nanophotonic structures.

Understanding the mechanical behavior of quasi-parallel fiber networks is essential for improving the manufacturing processes of fiber-reinforced composites. Mesoscale models of dry yarns and reinforcements require constitutive laws that accurately reflect the heterogeneous microstructure of fiber bundles. This study aims to develop a numerical generator of random fiber bundles for microscopic parametric studies of compaction behavior. A real fiber bundle was first reconstructed from X-ray microtomography data, and the numerical strategy was validated by tracking fiber cross-sections along the bundle length, with a fiber-position error of 5.2%. Based on this validated framework, an experiment-independent generator was established to create parameterized fiber bundles. The generated bundles reproduced the experimental compaction response with good agreement. Parametric results showed that increasing fiber waviness enhances inter-fiber interactions, increases transverse stiffness, and requires a higher load to reach the same fiber volume fraction. This framework provides a useful microscopic basis for studying fiber-bundle deformation mechanisms and for developing future mesoscopic constitutive laws.

Accurate timekeeping relies on feedback that continually steers a local clock toward a higher-grade reference. We evaluate first-order sliding-mode control (SMC) for steering an atomic clock and benchmark it against two standards: linear-quadratic-Gaussian (LQG) control and the bang-bang (BB). All three are tested in a common numerical framework using the standard two-state clock model driven by white and random-walk-frequency noise. To ensure the conclusions are not tied to a single noise realization and a single time period, we repeat the accuracy analysis over 100 independent random seeds for four different time periods, reusing the same seed across controllers within each trial. The time periods considered are one week, one month, one year, and ten years to cover short-, mid-, and long-term analyses of accuracy. Our results show that SMC remains competitive with LQG across the tested timescales and reference-clock qualities, and local-clock noise variations. Both SMC and LQG substantially outperform BB over the same time periods. Over the full averaging-time range studied, SMC's stability is almost identical to LQG's, whereas BB shows the characteristic short-term instability. Together, our results indicate that SMC is a promising clock-steering policy that can remain close to LQG in accuracy while avoiding the short-term instability seen in BB.

Online, comprehensive molecular profiling of exhaled breath provides a non-invasive window into human metabolism, yet current optical platforms are restricted by narrow instantaneous spectral coverage. Here, we present a novel ultra-broadband mid-infrared spectroscopic platform that enables simultaneous, high-sensitivity detection of a comprehensive profile of breath biomarkers. By integrating an intrapulse difference-frequency generation (IDFG) supercontinuum source spanning 2.9-11.5 $\mu$m (2580 cm$^{-1}$) with a custom-built Fourier transform spectrometer, we achieve a spectral resolution of 0.1 cm$^{-1}$ - surpassing current laser-based approaches. Combined with a standardized online sampling system, the platform achieves sensitivities in the tens of parts per billion over three minutes, resolving dynamic metabolic changes of ammonia, methane, isoprene, acetone, carbon monoxide, and nitrous oxide. We demonstrate the system's utility through proof-of-concept case studies tracking responses to fasting, protein intake, and smoking. This calibration-free platform establishes a powerful and versatile tool for online breath analysis, with broad potential in clinical diagnostics and exposure monitoring.

Distributed acoustic sensing (DAS) systems generate continuous, ultra-high-channel-count data streams at rates that exceed the capabilities of conventional batch-oriented analysis frameworks. As a result, essential tasks such as interactive exploration of long-duration recordings, scalable event annotation, and real-time algorithm-in-the-loop monitoring remain inadequately supported by workflows built around manually selected data segments and offline processing. This paper presents FiLark (Fiber Lark), a Python framework that applies a streaming-first principle uniformly across data access, signal processing, visualization and monitoring for DAS. Instead of operating on manually selected data segments, FiLark presents any DAS sources-including continuous multi-file recordings-as a unified stream and builds all system components around that abstraction. An OpenGL-based ring-buffer renderer enables interactive browsing and visualization of arbitrarily long recordings with constant memory usage. An integrated annotation interface supports event labeling directly within continuous data streams, facilitating the creation of reproducible machine-learning-ready labeled datasets without offline preprocessing. The signal processing library includes temporal, spatial, spectral, and decomposition-based operators, with both CPU implementations and GPU-accelerated variants via PyTorch, alongside stateful chunked execution that preserves processing continuity and application semantics across segment boundaries. A standardized monitor interface further integrates streaming detectors and learning-based models into the visualization workflow. By sharing a common streaming abstraction across all layers, FiLark allows processing configurations and workflows developed interactively to transfer directly to scalable production pipelines without modification.

High-energy (mJ) and high-peak-power (MW) multicycle terahertz (THz) pulses are essential for nonlinear THz spectroscopy and compact accelerator technologies, yet their generation by nonlinear optical frequency conversion remains inefficient and imposes severe demands on femtosecond driving lasers. Amplifying existing THz pulses offers an appealing alternative, but no power-scalable amplifier has been realized in the sub-THz regime. Here, we demonstrate an all-optical THz amplifier operating at 0.35 THz based on the modulation of nanosecond laser pulses by a weak THz field in periodically poled lithium niobate (PPLN). The THz-induced phase modulation is converted into an amplitude modulation using controlled group delay dispersion, forming a tailored pulse train that can efficiently drive high-energy THz generation in a second crystal, thereby amplifying the THz seed. By analogy to electronic klystrons, we term this device the Phlystron, in which the electron beam carrying the power is replaced by a photon beam. In this proof-of-concept experiment, a 3.3-fold increase in THz energy is achieved with commercial crystals. Scaling analysis indicates the potential for higher gain when using large-aperture PPLN devices and multi-stage amplification. The Phlystron thus provides a scalable route to powerful multicycle THz sources driven by readily available narrowband lasers.