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Large deployable mesh reflectors play a critical role in satellite communications, Earth observation, and deep-space exploration, offering high-gain antenna performance through precisely shaped reflective surfaces. Traditional dynamic modeling approaches—such as wave-based and finite element methods—often struggle to accurately capture the complex behavior of three-dimensional reflectors due to oversimplifications of cable members. To address these challenges, this paper proposes a novel spatial discretization framework that systematically decomposes cable member displacements into boundary-induced and internal components in a global Cartesian coordinate system. The framework derives a system of ordinary differential equations for each cable member by enforcing the Lagrange’s equations, capturing both longitudinal and transverse internal displacement of the cable member. Numerical simulations of a two-dimensional cable-network structure and a center-feed parabolic deployable mesh reflector with 101 nodes illustrate the improved accuracy of the proposed method in predicting vibration characteristics across a broad frequency range. Compared to standard finite element analysis, the proposed method more effectively identifies both low- and high-frequency modes and offers robust convergence and accurate prediction for both frequency and transient responses of the structure. This enhanced predictive capability underscores the significance of incorporating internal cable member displacements for reliable dynamic modeling of large deployable mesh reflectors, ultimately informing better design, control, and on-orbit performance of future space-based reflector systems. 

Efficient real-time solvers for forward and inverse problems are essential in engineering and science applications. Machine learning surrogate models have emerged as promising alter- natives to traditional methods, offering substantially reduced computational time. Never- theless, these models typically demand extensive training datasets to achieve robust gen- eralization across diverse scenarios. While physics-based approaches can partially mitigate this data dependency and ensure physics-interpretable solutions, addressing scarce data regimes remains a challenge. Both purely data-driven and physics-based machine learning approaches demonstrate severe overfitting issues when trained with insufficient data. We propose a novel model-constrained Tikhonov autoencoder neural network framework, called TAEN, capable of learning both forward and inverse surrogate models using a single arbitrary observational sample. We develop comprehensive theoretical foundations including forward and inverse inference error bounds for the proposed approach for linear cases. For compara- tive analysis, we derive equivalent formulations for pure data-driven and model-constrained approach counterparts. At the heart of our approach is a data randomization strategy with theoretical justification, which functions as a generative mechanism for exploring the train- ing data space, enabling effective training of both forward and inverse surrogate models even with a single observation, while regularizing the learning process. We validate our approach through extensive numerical experiments on two challenging inverse problems: 2D heat conductivity inversion and initial condition reconstruction for time-dependent 2D Navier–Stokes equations. Results demonstrate that TAEN achieves accuracy comparable to traditional Tikhonov solvers and numerical forward solvers for both inverse and forward problems, respectively, while delivering orders of magnitude computational speedups. 

Abstract Human mobility is becoming increasingly complex in urban environments. However, our fundamental understanding of urban population dynamics, particularly the pulsating fluctuations occurring across different locations and timescales, remains limited. Here, we use mobile device data from large cities and regions worldwide combined with a detrended fractal analysis to uncover a universal spatiotemporal scaling law that governs urban population fluctuations. This law reveals the scale invariance of these fluctuations, spanning from city centers to peripheries over both time and space. Moreover, we show that at any given location, fluctuations obey a time-based scaling law characterized by a spatially decaying exponent, which quantifies their relationship with urban structure. These interconnected discoveries culminate in a robust allometric equation that links population dynamics with urban densities, providing a powerful framework for predicting and managing the complexities of urban human activities. Collectively, this study paves the way for more effective urban planning, transportation strategies, and policies grounded in population dynamics, thereby fostering the development of resilient and sustainable cities. 

Standardized protocols for absolute quantification of potato virus Y (PVY) from potato tissue is critical for host-virus dynamic studies. Here, we developed a standardized protocol using a cloned viral sequence as standards to detect and quantify PVY. Starting with total RNA, concentrated via column-based kit, this protocol is able to detect approximately 50 viral copies/reaction from multiple PVY strains. Validation of this protocol confirmed linearity across 8 orders of magnitude with high repeatability, reproducibility and statistical robustness across three independent runs. This protocol offers reliable PVY quantification to manage potato crop health and enables comparative studies with other viral systems. 

As conventional electronic materials approach their physical limits, the application of ultrafast optical fields to access transient states of matter cap- tures imagination. The inversion symmetry governs the optical parity selection rule, differentiating between accessible and inaccessible states of matter. To circumvent parity-forbidden transitions, the common practice is to break the inversion symmetry by material design or external fields. Here we report how the application of femtosecond ultraviolet pulses can energize a parity-forbidden dark exciton state in black phosphorus while maintaining its intrinsic material symmetry. Unlike its conventional bandgap absorption in visible-to-infrared, femtosecond ultraviolet excitation turns on efficient Coulomb scattering, promoting carrier multiplication and electronic heating to ~3000 K, and consequently populating its parity-forbidden states. Interfero- metric time- and angle-resolved two-photon photoemission spectroscopy reveals dark exciton dynamics of black phosphorus on ~100 fs time scale and its anisotropic wavefunctions in energy-momentum space, illuminating its potential applications in optoelectronics and photochemistry under ultraviolet optical excitation. 

Abstract The lysosome-related organelles (“gut granules”) in the intestinal cells of many nematodes, including Caenorhabditis elegans, play an important role in metabolic and signaling processes, but they have not been fully characterized. We report here a previously undescribed phenomenon in which the autofluorescence of these granules displays a “flash” phenomenon in which fluorescence decreases are preceded by sharp increases in fluorescence intensity that expand into the surrounding area when the granules are stimulated with blue light. Autofluorescent granules are present in the intestinal cells of all six nematode species examined, with differences in morphology and distribution pattern. Five species exhibit the flash phenomenon: Panagrellus redivivus (Clade IV), Steinernema hermaphroditum (Clade IV), C. elegans (Clade V), Oscheius tipulae (Clade V), and Pristionchus pacificus (Clade V). The reaction of the granules to blue light stimulation greatly differs among different developmental stages and may also be dependent on physiological conditions. In addition, even within the same animal, the sensitivity of individual granules differs, with some of the variation associated with other characteristics of the granules, such as their overall location within the intestine. We hypothesize that the differences in response to blue light indicate the existence of different sub-populations of gut granules in nematode intestines, and the visually spectacular dynamic fluorescence phenomenon we describe might provide a handle on their eventual characterization.