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ABSTRACT Grassland ecosystems play essential roles in global carbon cycling and biodiversity conservation, yet it remains unclear whether belowground productivity is less sensitive to environmental change than aboveground productivity. Previous studies have predominantly focused on aboveground net primary productivity (ANPP) stability, potentially overestimating ecosystem vulnerability by neglecting critical belowground processes. By synthesizing 1513 observations from 113 studies across 85 grassland ecosystems worldwide, we quantified the responses of productivity, temporal stability, and carbon allocation to nine global change drivers, including nutrient enrichment, altered precipitation, elevated CO₂, warming, mowing, and grazing. Our results reveal that belowground net primary productivity (BNPP) stability shows generally weaker responses to global change drivers than ANPP stability. In addition, variation in ANPP stability was most closely associated with broad‐scale climatic indices of water supply (precipitation and aridity, used here as proxies for plant‐available soil water), whereas BNPP stability was more closely associated with edaphic context (soil moisture‐related and fertility‐related properties). These distinct patterns suggest that broad‐scale climatic variability is more strongly reflected in aboveground stability, whereas belowground stability is better captured by edaphic predictors related to water retention and nutrient availability. Moreover, variation in belowground carbon allocation was consistently associated with stronger coordination between above‐ and belowground responses and with the maintenance of BNPP stability under global‐change perturbations, suggesting a potential allocation‐related pathway linked to ecosystem resistance. Our findings challenge traditional ecological theories emphasizing unified above‐belowground responses and suggest that previous research focusing solely on aboveground processes may have overestimated grassland vulnerability. This synthesis provides critical insights for predicting grassland ecosystem stability and functioning under ongoing global environmental changes. 

Robots designed to interact with people in collaborative or social scenarios must move in ways that are consistent with the robot's task and communication goals. However, combining these goals in a naïve manner can result in mutually exclusive solutions, or infeasible or problematic states and actions. In this paper, we present Lively, a framework which supports configurable, real-time, task-based and communicative or socially-expressive motion for collaborative and social robotics across multiple levels of programmatic accessibility. Lively supports a wide range of control methods (i.e. position, orientation, and joint-space goals), and balances them with complex procedural behaviors for natural, lifelike motion that are effective in collaborative and social contexts. We discuss the design of three levels of programmatic accessibility of Lively, including a graphical user interface for visual design called LivelyStudio, the core library Lively for full access to its capabilities for developers, and an extensible architecture for greater customizability and capability. 

The eukaryotic cytoskeleton plays essential roles in cell signaling and trafficking, broadly associated with immunity and diseases in humans and plants. To date, most studies describing cytoskeleton dynamics and function rely on qualitative/quantitative analyses of cytoskeletal images. While state-of-the-art, these approaches face general challenges: the diversity among filaments causes considerable inaccuracy, and the widely adopted image projection leads to bias and information loss. To solve these issues, we developed the Implicit Laplacian of Enhanced Edge (ILEE), an unguided, high-performance approach for 2D/3D-based quantification of cytoskeletal status and organization. Using ILEE, we constructed a Python library to enable automated cytoskeletal image analysis, providing biologically interpretable indices measuring the density, bundling, segmentation, branching, and directionality of the cytoskeleton. Our data demonstrated that ILEE resolves the defects of traditional approaches, enables the detection of novel cytoskeletal features, and yields data with superior accuracy, stability, and robustness. The ILEE toolbox is available for public use through PyPI and Google Colab. 

Probe is the core component of an optical scanning probe microscope such as scattering-type scanning near-field optical microscopy (s-SNOM). Its ability of concentrating and localizing light determines the detection sensitivity of nanoscale spectroscopy. In this paper, a novel plasmonic probe made of a gradient permittivity material (GPM) is proposed and its nanofocusing performance is studied theoretically and numerically. Compared with conventional plasmonic probes, this probe has at least two outstanding advantages: First, it doesn't need extra structures for surface plasmon polaritons (SPPs) excitation or localized surface plasmon resonance (LSPR), simplifying the probe system; Second, the inherent nanofocusing effects of the conical probe structure can be further reinforced dramatically by designing the distribution of the probe permittivity. As a result, the strong near-field enhancement and localization at the tip apex improve both spectral sensitivity and spatial resolution of a s-SNOM. We also numerically demonstrate that a GPM probe as well as its enhanced nanofocusing effects can be realized by conventional semiconductor materials with designed doping distributions. The proposed novel plasmonic probe promises to facilitate subsequent nanoscale spectroscopy applications. 

Autonomous vehicles (AVs) are on the verge of changing the transportation industry. Despite the fast development of autonomous driving systems (ADSs), they still face safety and security challenges. Current defensive approaches usually focus on a narrow objective and are bound to specific platforms, making them difficult to generalize. To solve these limitations, we propose AVMaestro, an efficient and effective policy enforcement framework for full-stack ADSs. AVMaestro includes a code instrumentation module to systematically collect required information across the entire ADS, which will then be feed into a centralized data examination module, where users can utilize the global information to deploy defensive methods to protect AVs from various threats. AVMaestro is evaluated on top of Apollo-6.0 and experimental results confirm that it can be easily incorporated into the original ADS with almost negligible run-time delay. We further demonstrate that utilizing the global information can not only improve the accuracy of existing intrusion detection methods, but also potentially inspire new security applications. 

A second-generation Ni-based superalloy has been directionally solidified by using a Bridgman method, and the key processing steps have been investigated with a focus on their effects on microstructure evolution and mechanical properties. The as-grown microstructure is of a typical dendrite structure with microscopic elemental segregation during solidification. Based on the microstructural evidence and the measured phase transformation temperatures, a step-wise solution treatment procedure is designed to effectively eliminate the compositional and microstructural inhomogeneities. Consequently, the homogenized microstructure consisting of γ/γ′ phases (size of γ′ cube is ~400 nm) have been successfully produced after a two-step (solid solution and aging) treatment. The mechanical properties of the resulting alloys with desirable microstructures at room and elevated temperatures are measured by tensile tests. The strength of the alloy is comparable to commercial monocrystalline superalloys, such as DD6 and CMSX-4. The fracture modes of the alloy at various temperatures have also been studied and the corresponding deformation mechanisms are discussed.