Search Results (561)

Snow cover, as a critical component of the global climate system, strongly influences ecological processes in cold and temperate regions. However, how different ecosystem components—plants, soils, and microbes—respond to snow cover change remains poorly understood, especially in terms of their coordination. Here, we conducted a global meta-analysis of 1986 single and 1047 paired observations from snow manipulation experiments across diverse terrestrial ecosystems. Our results showed that snow removal generally reduced SWC and microbial diversity, whereas snow addition exerted smaller or more variable influences across ecosystem components. Among all variables, the effect of snow cover change on soil water content was most pronounced, whereas its impacts on other factors were generally weak. Notably, the direction and magnitude of responses often differed among ecosystem components exposed to the same treatments. Pairwise comparisons revealed frequent mismatches between plant and soil organism responses, indicating substantial ecosystem-level decoupling across biomes. These findings support the ecosystem asynchrony hypothesis and highlight the need for integrated approaches that link aboveground and belowground processes. Our study improves the understanding of ecosystem stability under changing snow regimes and provides insights for predicting future terrestrial responses to global climate change. Full article

Thrombosis remains a leading preventable cause of global morbidity and mortality, with conditions like venous thromboembolism and atrial fibrillation affecting millions worldwide. Traditional anticoagulants (heparins, vitamin K antagonists) require careful monitoring due to narrow therapeutic windows. Direct oral anticoagulants (DOACs) greatly improved convenience and reduced certain hemorrhagic complications (notably intracranial hemorrhage) compared to warfarin, but bleeding, drug–drug interactions, and unmet needs in special populations persist. This review highlights emerging strategies to decouple antithrombotic efficacy from bleeding risk. Novel agents targeting factor XI or XII (small molecules, antibodies, antisense oligonucleotides) have shown in early trials robust thromboembolism prevention with low bleeding. Advances in pharmacogenomics, biomarker-guided dosing, artificial intelligence risk prediction, and digital monitoring promise to personalize therapy. We discuss optimized approaches for high-risk subgroups (cancer-associated thrombosis, extremes of body weight, renal/hepatic dysfunction, pregnancy, perioperative care, and COVID-19) with citations to current evidence. Finally, we outline critical systems-level considerations, including drug accessibility, cost-effectiveness, and educational strategies, that are necessary to realize precision anticoagulation. Our synthesis is grounded in recent peer-reviewed literature and emphasizes innovations likely to improve safety and efficacy of thromboprophylaxis. Full article

Land-use carbon emissions denote the release or sequestration of greenhouse gases (e.g., CO₂, N₂O) resulting from human land-use activities, with land-use changes exerting a major influence on land-use carbon emissions. Revealing the coupling mechanism between land-use changes and carbon emissions is of crucial theoretical significance for achieving “dual carbon” goals and mitigating global climate change. Based on the land-use change data of Jiangxi Province, this study explored the Spatial–temporal relationship between land-use carbon emissions and land-use changes in Jiangxi Province from 2000 to 2020 using a model of land-use dynamic degrees, a model of land-use transfer matrices, and the IPCC carbon emission accounting model. In this study, the factors influencing changes in land-use carbon emissions were comprehensively analyzed using an LMDI model and the Tapio decoupling model. The results indicated that: (1) Jiangxi Province’s land-use changes show a “two-increase, four-decrease” trend, with construction land and unused land experiencing the most significant shifts, while water, grassland, cropland, and forestland changes stayed near 1%. (2) Net land-use carbon emissions exhibit a rapid then gradual increase, with higher emissions in the north/south and lower levels in central regions. While overall land-use carbon emission intensity is declining, per capita emissions continue to rise. (3) Land-use carbon emission changes are primarily driven by emission intensity, land-use structure, efficiency, and economic level. In Jiangxi, economic growth mainly increases land-use carbon emissions, while land-use efficiency enhancement counters this trend. Jiangxi Province shows weak land-use carbon emission–economic growth decoupling, with land-use carbon emissions rising more slowly than economic growth. This study not only provides a typical case analysis and methodological framework for understanding the carbon emission effects of human–land relationships in rapidly urbanizing regions but also offers a specific scientific basis and policy insights for Jiangxi Province and other similar regions to formulate differentiated territorial spatial planning, promote ecological protection and restoration, and achieve green and low-carbon development pathways under the “dual carbon” goals. Full article

Under the framework of the “dual carbon” goals, promoting the coordinated development of carbon emission efficiency, carbon sink capacity, and high-quality growth has become a critical issue for regional sustainability. Using panel data from 2006 to 2021, this study systematically investigates the three-dimensional coupling coordination among carbon emission efficiency, carbon sink capacity, and high-quality development in the Greater Chang-Zhu-Tan urban agglomeration. The spatiotemporal evolution, spatial correlation characteristics, and influencing factors of the coupling coordination were also explored. The results indicate that the coupling coordination system exhibits an evolutionary trend of overall stability with localized differentiation. The overall coupling degree remains in the “running-in” stage, while the coordination level is still in a marginally coordinated state. Spatially, the pattern has shifted from “northern leadership” to “multi-polar support,” with Yueyang achieving intermediate coordination, four cities including Changde reaching primary coordination, and three cities including Loudi remaining imbalanced. Spatial correlation has weakened from significant to insignificant, with Xiangtan showing a “low–low” cluster and Hengyang displaying a “high–low” cluster. The evolution of hot and cold spots has moved from marked differentiation to a more balanced distribution, as reflected by the disappearance of cold spots. The empirical analysis confirms a three-dimensional coupling mechanism: ecologically rich regions attain high coordination through carbon sink synergies; economically advanced areas achieve decoupling through innovation-driven development; while traditional industrial cities, despite facing the “green paradox,” demonstrate potential for leapfrog progress through transformation. Among the influencing factors, industrial structure upgrading emerged as the primary driver of spatial differentiation, though with a negative impact. Government support also exhibited a negative effect, whereas the interaction between environmental regulation and both government support and economic development was found to be significant. Full article

In recent years, frequent wildfires have posed significant threats to both the ecological environment and socioeconomic development. Investigating the mechanisms underlying the influencing factors of forest fires and accurately predicting the likelihood of such events are crucial for effective prevention strategies. However, the field currently faces challenges, including the unclear characterization of influencing factors, limited accuracy in forest fire predictions, and the absence of models for mountain fire scenarios. To address these issues, this study proposes a research framework of “decoupling analysis-model prediction-scenario validation” and employs Principal Component Analysis (PCA) and Shapley Additive Explanations (SHAP) value quantification to elucidate the significant roles of meteorological as well as combustible state indicators through multifactor coupling. Furthermore, the Attention-based Long Short-Term Memory (ALSTM) network trained on PCA-decoupled data achieved mean accuracy, recall, and area under the precision-recall curve (PR-AUC) values of 97.82%, 94.61%, and 99.45%, respectively, through 10-time cross-validation, significantly outperforming traditional LSTM neural networks and logistic regression (LR) methods. Based on digital twin technology, a three-dimensional mountain fire scenario evolution model is constructed to validate the accuracy of the ALSTM network’s predictions and to quantify the impact of key factors on fire evolution. This approach offers an interpretable solution for predicting forest fires in complex environments and provides theoretical and technical support for the digital transformation of forest fire prevention and management. Full article

China’s coastal fisheries face challenges to their sustainability due to climate and human-induced pressures on key habitat drivers. This study provides an 18-year (2003–2020) assessment of six key ecological and data-available environmental factors (sea-surface temperature (SST), salinity, transparency, currents (eastward velocity, EV; northward velocity, NV), and net primary productivity (NPP), selected for their ecological relevance and data availability, across the Bohai, Yellow, and East China Seas at a spatial resolution of 0.083°. Non-parametric trend tests and seasonal climatologies were applied using MODIS-Aqua and CMEMS data with a refined quasi-analytical algorithm (QAA-v6). The results show distinct gradients: SST ranging from 9 to 13 °C (Bohai Sea) to >20 °C (East China Sea); transparency ranging from <5 m (turbid coasts) to 29.20 m (offshore). Seasonal peaks occurred for SST (summer: 18.92 °C), transparency (summer: 12.54 m), and primary productivity (spring: 1289 mg/m²). Long-term trends reveal regional SST warming in the northern Yellow Sea (9.78% of the area), but cooling in the central East China Sea. Widespread increases in transparency were observed (65.14% of the area), though productivity declined significantly (27.3%). The drivers showed spatial coupling (e.g., SST–salinity r = 0.95), but the long-term trends were decoupled. This study provides a comprehensive and long-term assessment of multiple key habitat drivers across China’s coastal seas. The results provide an unprecedented empirical baseline and dynamic management tools for China’s changing coastal ecosystems. Full article

Intrinsically stretchable polymer ionic conductors (PICs) hold significant application prospects in fields such as flexible sensors, energy storage devices, and wearable electronic devices, serving as promising solutions to prevent mechanical failure in flexible electronics. However, the development of PICs is hindered by an inherent trade-off between mechanical robust and electrical properties. Cellulose, renowned for its high mechanical strength, tunable chemical groups, abundant resources, excellent biocompatibility, and remarkable recyclability and biodegradability, offers a powerful strategy to decouple and enhance mechanical and electrical properties. This review presents recent advances in cellulose-based polymer ionic conductors (CPICs), which exhibit exceptional design versatility for flexible electrodes and strain sensors. We systematically discuss optimization strategies to improve their mechanical properties, electrical conductivity, and environmental stability while analyzing the key factors such as sensitivity, gauge factor, strain range, response time, and cyclic stability, where strain sensing refers to a technique that converts tiny deformations (i.e., strain) of materials or structures under external forces into measurable physical signals (e.g., electrical signals) for real-time monitoring of their deformation degree or stress state. Full article

Generational approaches to climate engagement among older adults remain limited. This study examines the evolution of eco-anxiety, environmental activism, and pro-environmental behavior from a life course perspective, focusing on older adults in Spain. A nationwide CATI survey of 3000 residents aged 18 and older was conducted, employing validated multidimensional scales for eco-anxiety, environmental activism, and pro-environmental behavior, each rescaled to a 0–10 range. Data were analyzed using descriptive statistics, analyses of variance, and hierarchical regression models to estimate linear and quadratic age effects beyond sex, education, and subjective social class. Results show that (1) eco-anxiety follows an inverted-U pattern, peaking at ages 45–49 and declining significantly after 60; (2) environmental activism remains high until the late sixties, while everyday pro-environmental behaviors sharply decline after retirement; and (3) eco-anxiety and environmental action in older adults are partially decoupled, reflecting the role of supportive personal and contextual factors beyond emotional concern. The findings challenge prevailing stereotypes of passive older adults by demonstrating that older age can constitute a significant period of climate engagement. Despite a slight decline in climate concern following retirement, the willingness to take action remains notably resilient. Older adults maintain consistent involvement in environmental volunteering and activism, often motivated by a desire to leave a lasting legacy and shaped by personal experiences of past crises and collective struggles. However, pro-environmental behaviors show a marked decrease in older adults, not due to diminished interest but likely as a result of structural constraints such as declining health, limited income, and inadequate housing conditions. This study suggests that, in the context of liquid modernity marked by rapid change and uncertainty, older adults may serve as societal anchors—preserving narratives, emotional bonds, and civic networks. Through policies that address structural barriers, this anchor role can be supported, empowering older adults to improve their well-being and strengthening community resilience in the face of climate change. Full article

Trajectory planning for logistics UAVs in complex environments faces a key challenge: balancing global search breadth with fine constraint accuracy. Traditional algorithms struggle to simultaneously manage large-scale exploration and complex constraints, and lack sufficient modeling capabilities for multi-UAV systems, limiting cluster logistics efficiency. To address these issues, we propose a GWOP algorithm based on dual-layer fusion of GWO and GRPO and incorporate a graph attention network (GAT). First, CEC2017 benchmark functions evaluate GWOP convergence accuracy and balanced exploration in multi-peak, high-dimensional environments. A hierarchical collaborative architecture, “GWO global coarse-grained search + GRPO local fine-tuning”, is used to overcome the limitations of single-algorithm frameworks. The GAT model constructs a dynamic “environment–UAV–task” association network, enabling environmental feature quantification and multi-constraint adaptation. A multi-factor objective function and constraints are integrated with multi-task cascading decoupling optimization to form a closed-loop collaborative optimization framework. Experimental results show that in single UAV scenarios, GWOP reduces flight cost (FV) by over 15.85% on average. In multi-UAV collaborative scenarios, average path length (APL), optimal path length (OPL), and FV are reduced by 4.08%, 14.08%, and 24.73%, respectively. In conclusion, the proposed method outperforms traditional approaches in path length, obstacle avoidance, and trajectory smoothness, offering a more efficient planning solution for smart logistics. Full article

This study presents the influence of the type of weave and relative fabric density on surface roughness and the coefficient of friction in raw cotton woven fabrics. Relative fabric density, which represents how full a fabric is compared to the maximum packing density allowed by its weave, provides a more accurate basis for comparison than absolute fabric density. Analysis revealed that both the type of weave and relative fabric density have a statistically significant effect on surface roughness, while neither factor significantly impacts the coefficient of friction. Notably, increasing relative fabric density consistently reduces surface roughness in plain, 2/2 twill, and, to some extent, 5-end satin fabrics, with plain fabrics showing the highest roughness overall. At high densities, 2/2 twill fabrics exhibit greater structural stability, yielding smoother surfaces than 5-end satin fabrics, reversing trends detected at lower densities. Furthermore, the relationship between surface roughness and friction was decoupled in plain and 2/2 twill fabrics—specifically, increased density leads to smoother surfaces and higher friction. 5-end satin fabrics were unique in showing a simultaneous reduction in both surface-mechanical properties as fabric density increased. These findings highlight that relative fabric density is a critical parameter for engineering fabrics with tailored performance properties. Full article

Electromagnetic micro-electro-mechanical system (MEMS) micromirrors are widely used in optical scanning systems but often encounter mechanical crosstalk due to the use of shared drive coils. This phenomenon leads to parasitic motion along the slow axis during fast-axis operation, resulting in undesirable elliptical scanning patterns that degrade image quality. To tackle this issue, a hybrid actuation scheme is proposed in which a piezoelectric actuator drives the fast axis through an S-shaped spring structure, achieving a resonance frequency of 792 Hz, while the slow axis is independently driven by an electromagnetic actuator operating in quasi-static mode. Finite element simulations and experimental measurements validate that the proposed decoupled design significantly suppresses mechanical crosstalk. When the fast axis is driven to a 40° optical scan angle, the hybrid system reduces the parasitic slow-axis deflection (typically around 1.43°) to a negligible level, thereby producing a clean single-line scan. The piezoelectric fast axis exhibits a quality factor of Q = 110, while the electromagnetic slow axis achieves a linear 20° deflection at 20 Hz. This hybrid design facilitates a distortion-free field of view measuring 40° × 20° with uniform line spacing, presenting a straightforward and effective solution for high-precision scanning applications such as LiDAR (Light Detection and Ranging) and structured light projection. Full article

Electrification introduces additional NVH (noise, vibration and harshness) challenges during the development of powertrain mounting systems due to high-frequency excitations from the powertrain and the absence of masking effects from the combustion engine. In these frequency ranges, engine mounts can stiffen up to a factor of five due to continuum resonances, reducing their structure-borne sound isolation properties and negatively impacting the customer’s NVH perception. Common hardening factors used during elastomer mount development are therefore limited in terms of their applicable validation frequency range. This study presents a methodology for determining decoupled permissible stiffness ranges for a double-isolated mounting system up to 1500 Hz, based on solution space engineering. Instead of optimizing for a single best design, we seek to maximize solution boxes, resulting in robust stiffness ranges that ensure the fulfillment of the formulated system requirements. These ranges serve as NVH requirements at the component level, derived from the sound pressure level at the seat location. They provide tailored guidelines for mount development, such as geometric design or optimal resonance placement, while simultaneously offering maximum flexibility by spanning the solution space. The integration of machine learning approaches enables the application of large-scale finite-element models within the framework of solution space analysis by reducing the computational time by a factor of $7.19·{10}^3$. From a design process standpoint, this facilitates frontloading by accelerating the evaluation phase as suppliers can directly benchmark their mounting concepts against the permissible ranges and immediately verify compliance with the defined targets. Full article

Monitoring and tracking the long-term dynamics of vineyard coverage and fresh grape production can support sustainable agricultural planning under evolving climate, market, and land-use pressures. This study presents a comprehensive, data-driven analysis of global viticulture trends from 1961 to 2023, integrating the official statistical database of the Food and Agriculture Organization of the United Nations (FAOSTAT) for grape-producing countries. We applied statistical trend analysis (Mann–Kendall test), Random Forest regression modeling, cross-correlation functions, and dissimilarity analysis to examine patterns and drivers of change in vineyard area, production volume, yield efficiency, and land-use intensity. Our results reveal a significant global decoupling of production from vineyard areas, driven by increasing yields and technological intensification, particularly in rapidly expanding table grape markets in Asia. While traditional European wine regions are reducing vineyard coverage, emerging producers such as China and India are achieving high production with improved land efficiency. Production volume emerged as the dominant predictor of vineyard-harvested areas, while climatic factors, urbanization, and socio-economic dynamics also exerted significant influence. Our findings point to growing polarization in production amounts, alongside convergence in yield and management efficiency across countries. These findings contribute to the understanding of global viticulture transformation and provide insights into optimizing land-use strategies for sustainable grape production under climate change and market evolution. Full article

The rhizome of Atractylodes macrocephala, a perennial herb in the Asteraceae family, is valued for its bioactive atractylenolides, but achieving consistent quality in cultivation is challenging. This study aimed to decipher how environmental factors differentially regulate its biomass and atractylenolide content. We sampled from 22 Korean cultivation sites and performed correlation analyses, rigorously controlled by a False Discovery Rate (FDR) correction. Our analysis revealed that the environmental networks governing quantitative growth and qualitative composition are largely independent. While growth was weakly correlated with environmental factors, likely due to suboptimal temperatures at our sites, atractylenolide content was robustly associated with soil properties and climate. Specifically, soil texture was a dominant factor, with sand content showing a strong negative correlation (−0.717 ***) with total atractylenolides, whereas silt (0.675 ***) and clay (0.622 ***) had strong positive correlations. Additionally, cation exchange capacity (0.517 *) and temperature were positively correlated, while relative humidity showed a negative correlation (−0.553 **). This decoupling suggests that optimizing yield and phytochemical quality requires distinct cultivation strategies, providing a foundational framework for developing site-specific practices and quality control for this high-value medicinal herb. Full article

This study addresses the issues of unbalanced capacity allocation and rigid train formations in urban metro systems under tidal passenger flow conditions. By integrating temporal–spatial passenger demand with real-time dynamic train formation, we propose a virtual formation optimization method driven by carriage load factors. This method enhances the flexibility of train formation strategies by coordinating virtual coupling and decoupling operations between trains traveling in opposite directions. A mixed-integer linear programming (MILP) model is developed, with train unit allocation and turnover scheduling as the main decision variables. The model aims to minimize total passenger waiting time and system operating costs, while incorporating constraints related to unit allocation, turnover, and passenger assignment. The model can be efficiently solved using commercial solvers such as CPLEX. To evaluate the proposed method, a case study is conducted on a metro line in a major city. Numerical experiments demonstrate that, compared with a fixed 6-car formation scheme, the proposed method reduces total passenger waiting time by approximately 4.2% and operating costs by 11.6%. When compared to a fixed 8-car formation scheme, it achieves a 48.8% reduction in operating costs with only a 4.3% increase in passenger waiting time. These results highlight the potential of the proposed virtual formation strategy to enhance operational efficiency and resource utilization in urban metro systems, offering both practical value and implementation feasibility. Full article