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This article presents a cooperative framework for multi-robot wildfire monitoring that integrates dynamic Voronoi partitioning with large language model (LLM)-enhanced nonlinear model predictive control (NMPC) to address challenges in dynamic unknown environments. Conventional methods, particularly fixed-weight NMPC, lack adaptability in scenarios with suddenly changing obstacles, such as spreading fire fronts. Our approach employs a hierarchical architecture. At the task allocation level, an enhanced dynamic Voronoi algorithm ensures robust and collision-free area partitioning. At the motion control level, we innovatively leverage the semantic reasoning capability of LLMs to dynamically adjust the cost function weights of the NMPC in real time based on environmental features, overcoming the parameter rigidity of traditional controllers. Extensive simulations in benchmark environments demonstrate the framework’s superior performance over deep deterministic policy gradient (DDPG) and fixed-weight NMPC baselines, showing significant improvements in exploration efficiency and obstacle avoidance success rate. This work provides a viable solution that bridges high-level semantic cognition with low-level optimal control for robust autonomous surveillance.

We present a potentially eco-friendly, cost-efficient strategy for synthesizing high-purity Li 2 S, a key precursor for sulfide-based solid electrolytes. While these electrolytes surpass conventional organic counterparts in both safety and performance, their widespread application is hindered by the high cost of Li 2 S. Here, a solvent-free metathesis route is developed, in which thiourea serves as an S 2 ⁻ donor to sulfurize LiOH, enabling scalable Li 2 S production (∼100 g per batch) with significantly reduced projected costs. During the process, intermediates (H 2 NCN, H 2 O) are transformed into benign gases (CO 2 , NH 3 ) that spontaneously leave the system, thereby driving Li 2 S formation without Δ G mix limitations. The as-synthesized Li 2 S is successfully applied to prepare sulfide-based solid electrolytes such as Li 10 GeP 2 S 12 and argyrodite-Li 5.5 PS 4.5 Cl 1.5 , achieving laboratory-scale (1 kg) production costs reduction of up to 27.5% and 92.9%, respectively. Furthermore, all-solid-state batteries employing Li 5.5 PS 4.5 Cl 1.5 demonstrate electrochemical performance comparable to those fabricate with commercial Li 2 S. This scalable methodology thus may provide a proming pathway to bridge low-cost Li 2 S synthesis with the practical deployment of sulfide-based solid electrolytes, which may accelerate the commercialization of high-performance all-solid-state batteries. Sulfide solid electrolytes show potential for safer, higher-performance batteries, but costly Li 2 S precursors hinder commercial adoption. Here, authors develop a scalable, potentially eco-friendly Li 2 S synthesis method that reduces its production costs, which could facilitate wider deployment of sulfide solid electrolytes.

This study proposes an improved YOLOv8 model for vehicle and pedestrian detection in urban traffic monitoring systems. In order to improve the detection performance of the model, we introduced a multi-scale feature fusion module and an improved non-maximum suppression (NMS) algorithm based on the YOLOv8 model. The multi-scale feature fusion module enhances the model’s detection ability for targets of different sizes by combining feature maps of different scales; the improved non-maximum suppression algorithm effectively reduces repeated detection and missed detection by optimizing the screening process of candidate boxes. Experimental results show that the improved YOLOv8 model exhibits excellent detection performance on the VisDrone2019 dataset, and outperforms other classic target detection models and the baseline YOLOv8 model in key indicators such as precision, recall, F1 score, and mean average precision (mAP). In addition, through visual analysis, our method demonstrates strong target detection capabilities in complex urban traffic environments, and can accurately identify and label targets of multiple categories. Finally, these results prove the effectiveness and superiority of the improved YOLOv8 model, providing reliable technical support for urban traffic monitoring systems.

The sensitivity of soil organic carbon (SOC) decomposition in seasonally frozen soils, such as alpine ecosystems, to climate warming is a major uncertainty in global carbon cycling. Here we measure soil CO 2 emission during four years (2018–2021) from the whole-soil warming experiment (4 °C for the top 1 m) in an alpine grassland ecosystem. We find that whole-soil warming stimulates total and SOC-derived CO 2 efflux by 26% and 37%, respectively, but has a minor effect on root-derived CO 2 efflux. Moreover, experimental warming only promotes total soil CO 2 efflux by 7-8% on average in the meta-analysis across all grasslands or alpine grasslands globally (none of these experiments were whole-soil warming). We show that whole-soil warming has a much stronger effect on soil carbon emission in the alpine grassland ecosystem than what was reported in previous warming experiments, most of which only heat surface soils. This study demonstrates that future whole-soil warming has a much stronger effect on soil carbon emission in the alpine grassland ecosystem than what is estimated by previous warming experiments which only warm surface soils mostly.

Dozens of observational studies and two meta-analyses have investigated the association of migraine with the risk of stroke, but their results are inconsistent. We aimed to quantitatively evaluate the relationship between migraine and stroke risk by performing a meta-analysis of prospective cohort studies. PubMed and Embase were searched through July 2016 to identify studies that met pre-stated inclusion criterion and reference lists of retrieved articles were also reviewed. Information on the characteristics of the included study, risk estimates, and control for possible confounding factors were extracted independently by two authors. The random-effects model was used to calculate the pooled risk estimates. Eleven prospective cohort studies involving 3371 patients with stroke and 2,221,888 participants were included in this systematic review. Compared with non-migraineurs, the pooled relative risks of total stroke, hemorrhagic stroke, and ischemic stroke for migraineurs were 1.55 [95% confidence interval (CI) 1.38–1.75], 1.15 (95% CI 0.85–1.56), and 1.64 (95% CI 1.22–2.20), respectively. Exception of any single study did not materially alter the combined risk estimate. Integrated epidemiological evidence supports that migraine should be associated with the increased risk of total stroke and ischemic stroke, but the relationship between migraine and the risk of hemorrhagic stroke is not of certainty.

The structural stability, electronic structure, and elastic properties of MgZn2, Mg3Zn8Ce, and Mg4Zn7Ce have been investigated by adopting first-principles calculations methods based on density functional theory. The calculated lattice parameters agree well with experimental values and previous calculations. Formation enthalpy and binding energy calculations show that Mg3Zn8Ce has the highest alloying ability and structural stability. Electronic structure analysis suggests that Ce doping forms strong covalent bonds with Mg and Zn atoms, enhancing the stability of the system. Mechanical property calculations show that Mg4Zn7Ce exhibits the highest toughness, while Mg3Zn8Ce demonstrates the best shear resistance. Thus, Ce doping increases the stability and bonding strength of MgZn2, reduces material brittleness, and enhances material ductility. This computational analysis provides theoretical support for predicting the properties of Mg-Zn-Ce alloys.

Long-term observations have shown that many plants and aboveground animals have changed their phenology patterns due to warmer temperatures over the past decades. However, empirical evidence for phenological shifts in alpine organisms, particularly belowground organisms, is scarce. Here, we investigate how the activities and phenology of plants, soil microbes, and soil fauna will respond to warming in an alpine meadow on the Tibetan Plateau, and whether their potential phenological changes will be synchronized. We experimentally simulate an increase in soil temperature by 2–4 °C according to future projections for this region. We find that warming promotes plant growth, soil microbial respiration, and soil fauna feeding by 8%, 57%, and 20%, respectively, but causes dissimilar changes in their phenology during the growing season. Specifically, warming advances soil faunal feeding activity in spring and delays it in autumn, while their peak activity does not change; whereas warming increases the peak activity of plant growth and soil microbial respiration but with only minor shifts in their phenology. Such phenological asynchrony in alpine organisms may alter ecosystem functioning and stability. Phenological shifts driven by climate change are well-studied in plants and aboveground animals, but scarcely in belowground biota. Here, the authors show that soil warming causes phenological mismatches between plants, soil microbes and soil microarthropods in an alpine meadow.

We investigated the effects of warming on litter decomposition and the contribution of soil organisms (microbes vs. fauna) to it across the cold and warm seasons in an alpine meadow of the Qinghai-Tibetan Plateau. Our results showed that (1) warming profoundly increased litter decomposition by ~ 35%, but this warming effect only occurred in coarse-meshed bags (i.e., in the presence of soil fauna) and in warm season; (2) litter decomposition significantly increased by ~ 2.3-fold from fine- to coarse-meshed bags. However, such a mesh effect was only detected in warm (but not cold) season; (3) litter decomposed ~ 6.7 times faster in warm season than in cold season, and this seasonal effect was consistent across ambient and warming climates. Collectively, warm season may greatly promote the role of both fauna and microbes in litter decomposition and determine the amount of annual decomposition. Nevertheless, climate warming may only profoundly stimulate faunal (but not microbial) decomposition, especially during warm season of the alpine meadow.

Background and aimsGrasslands hold one of the most important soil carbon stocks in the world, which is vulnerable to climate change (i.e. precipitation) and human disturbance (i.e. land-use). This study aimed to investigate responses and mechanisms of soil organic carbon (SOC) decomposition and accumulation to precipitation and land-use in an Inner Mongolian grassland.MethodsUsing a randomized complete block design with a split plot, an experiment with land-use regimes (fencing, grazing, and mowing, since 2011) and altered precipitation amount (wet, + 50% precipitation; CT, ambient precipitation; dry, −50% precipitation; since 2016) was conducted to explore their impacts on SOC decomposition (represented by soil heterotrophic respiration and extracellular enzyme activities) and accumulation (represented by SOC and its physical fractions) from samples collected in 2019.ResultsSOC decomposition significantly increased under wet treatment, but decreased under dry treatment. Wet treatment increased SOC accumulation via the increment of mineral-associated organic carbon (MAOC), and vice versa for dry treatment. Precipitation amount may affect soil microbial biomass and activities via alterations of water supply, plant-derived carbon input, and other soil properties, leading to changes of SOC dynamics. Nevertheless, land-use regimes had little influences on SOC dynamics.ConclusionsCompared to land-use regimes, precipitation treatments can significantly change SOC dynamics. Overall, SOC increased under higher precipitation amount, but decreased with less precipitation. We emphasize that the SOC stock in Inner Mongolia temperate grassland may have an unexpectable fast response to precipitation alteration, but more investigation is still needed in longer terms.

The maintenance of cellular homeostasis involves the participation of multiple organelles. These organelles are associated in space and time, and either cooperate or antagonize each other with regards to cell function. Crosstalk between organelles has become a significant topic in research over recent decades. We believe that signal transduction between organelles, especially the endoplasmic reticulum (ER) and mitochondria, is a factor that can influence the cell fate. As the cellular center for protein folding and modification, the endoplasmic reticulum can influence a range of physiological processes by regulating the quantity and quality of proteins. Mitochondria, as the cellular “energy factory,” are also involved in cell death processes. Some researchers regard the ER as the sensor of cellular stress and the mitochondria as an important actuator of the stress response. The scientific community now believe that bidirectional communication between the ER and the mitochondria can influence cell death. Recent studies revealed that the death signals can shuttle between the two organelles. Mitochondria-associated membranes (MAMs) play a vital role in the complex crosstalk between the ER and mitochondria. MAMs are known to play an important role in lipid synthesis, the regulation of Ca2+ homeostasis, the coordination of ER-mitochondrial function, and the transduction of death signals between the ER and the mitochondria. Clarifying the structure and function of MAMs will provide new concepts for studying the pathological mechanisms associated with neurodegenerative diseases, aging, and cancers. Here, we review the recent studies of the structure and function of MAMs and its roles involved in cell death, especially in apoptosis.