Search Results (2,911)

To explore whether the causal chain of “Industry–University–Government Integrated Innovation Ecosystem → Novel Energy Storage Technology Innovation → Dual-Control Energy Targets” can be achieved, this study analyzes panel data from 30 provinces, municipalities, and autonomous regions in China (excluding Tibet, Hong Kong, Macao, and Taiwan) from 2010 to 2022. By employing a complex mediation effect model combining dynamic Qualitative Comparative Analysis (QCA) and the dynamic panel system Generalized Method of Moments (GMM) model, this study identifies five configuration pathways for driving innovation in novel energy storage technologies within an integrated innovation ecosystem. These include two industry digitalization–university innovation resource-dominant pathways: a government-light and digitally driven “university–industry” resource-sharing and knowledge-conversion synergy, and an industry leadership pathway embedded with university collaborative innovation under a digitalization framework. Two policy-driven hybrid and industry–leadership synergistic pathways are also extracted: a growth pathway for policy-supported hybrid organizations under insufficient industry digitalization and a policy-driven innovation substitution pathway compensating for the absence of university niche roles. Additionally, a multidimensional collaborative development pathway is identified, reflecting comprehensive collaboration. In the dynamic panel system GMM model, all five pathways collectively suppress total energy consumption and energy intensity, while also indirectly driving the achievement of dual-control energy targets through innovation in novel energy storage technologies. Pathways driven by government-light and digitally facilitated collaboration, industry leadership, and comprehensive collaboration show significant direct negative effects on energy consumption and intensity. However, the policy-driven innovation substitution pathway exhibits limited contribution due to the absence of university innovation components, thereby failing to significantly advance regional dual-control energy goals. Full article

Winter rapeseed (Brassica rapa L.) is an important crop for vegetable oil production in China. However, its productivity is frequently threatened by severe cold waves during winter. To investigate the role of the microtubule cytoskeleton in cold adaptation of winter rapeseed, a microtubule stabilizer paclitaxel (Tax) and a microtubule depolymerizer colchicine (Col) were sprayed on winter rapeseed and transgenic proBrAFP1 Arabidopsis, respectively. The mRNA levels of cold-induced genes, along with cell membrane stability, antioxidant enzyme activities, and hormone levels were assessed under cold stresses of 4 °C and −4 °C. The results showed that low temperature significantly activated the proBrAFP1 promoter activity and increased the mRNA levels of core cold signaling pathway genes, such as C-REPEAT BINDING FACTORS (CBFs), Cyclic Nucleotide-Gated Channel (CNGC), OPEN STOMATA 1 (OST1) and Inducer of CBF EXPRESSION 1 (ICE1). Notably, under low-temperature stress, exogenous application of the microtubule stabilizer Tax markedly suppressed proBrAFP1-driven reporter activity in transgenic Arabidopsis, with consistent inhibition observed across both stem and leaf tissues; meanwhile, the Tax application alleviated reactive oxygen species (ROS) accumulation and mitigated membrane damage. In contrast, under the same low-temperature stress, the Col treatment exacerbated oxidative stress, enhanced lipid peroxidation, and elevated membrane damage. Collectively, these findings establish that microtubule regulators play indispensable roles in the cold stress response of winter rapeseed. It provides new insights into the mechanism by which plant microtubule cytoskeleton regulators mediate the cold response. Full article

Frequent flash floods threaten human well-being, hydropower infrastructure, and ecosystems. However, the long-term evolution of flash flood patterns over recent decades remains insufficiently understood, particularly in data-scarce high-altitude regions. Using multi-source remote sensing data integrated with historical disaster records and field investigations, this study examined the spatiotemporal evolution and driving factors of flash floods across the Qinghai–Tibet Plateau (QTP). The results indicate that flash floods have increased exponentially, which may be influenced by disaster management policies, with peaks in July–August and frequent occurrences from April to September. The seasonal trajectory of the center of gravity of flash floods from April to September exhibited a clear directional pattern. Regions with the highest disaster density were concentrated in the headwaters of five major rivers, including the Yarlung Zangbo, Jinsha, Nu, Lancang, and Yellow Rivers. Shapley Additive Explanation (SHAP) and Random Forest analyses reveal that soil moisture, anthropogenic intensity, and seasonal runoff variability are the dominant driving factors. With ongoing socioeconomic development, intensified human activities have become a key contributor to the increasing frequency of flash floods. These findings highlight the value of remote sensing-based assessments for flash flood monitoring and early warning and provide scientific support for risk mitigation, loss reduction, and the advancement of water-related targets under the United Nations’ Sustainable Development Goals. Full article

To address the challenges associated with identifying causal relationships among event mentions in the event causality identification (ECI) task, ECI has emerged as a pivotal area of research for comprehending event structures. Recent studies have leveraged Transformer-based models, augmented by auxiliary components, to develop effective contextual representations for causality prediction. A critical step in ECI models involves transforming intricate event context representations into causal label representations, thereby facilitating the logical score calculations necessary for both training and inference. However, existing models frequently depend on simplistic feedforward networks for this transformation process, which often struggle to bridge the semantic gap between complex event contexts and target causal labels, particularly in linguistically nuanced scenarios. To address these limitations, we propose Contrastive Learning for Event Causality Identification (CLECI), an innovative ECI framework that enhances representation learning through the integration of contrastive learning techniques, a generator-discriminator mechanism with causal label embeddings. In contrast to traditional direct transformation methods, CLECI generates latent causal label embeddings that filter out irrelevant information while aligning with potential label representations. By incorporating contrastive learning principles, CLECI further augments the discriminative capability of event representations by constructing positive and negative pairs of events. Experimental evaluations conducted on the EventStoryLine (ESL), Causal-TimeBank (CTB), and MECI datasets demonstrate that CLECI achieves competitive performance, with F1-score improvements of 4.3%, 7.9%, and 2.5%, respectively, compared with the strongest baseline methods, while maintaining strong robustness in complex and noisy multilingual event contexts. Full article

Abies georgei var. smithii (Viguie & Gaussen) is a dominant conifer along the southeastern margin of the Qinghai–Tibet Plateau, where heart rot often develops covertly, complicating forest health monitoring and disease management. Fomitopsis subpinicola B.K. Cui, M.L. Han & Shun Liu is an important causal agent of heart rot affecting A. georgei var. smithii in this region, yet rapid, field-deployable molecular diagnostics of this pathogen remain limited. Here, we developed and evaluated two TEF1α-based isothermal platforms for specific detection of F. subpinicola: RAA and LAMP. To reduce potential cross-reactivity, TEF1α sequences from representative taxa within the F. pinicola species complex and closely related non-complex species were aligned for primer/probe design. Candidate RAA primers were screened by gel electrophoresis to select an optimal pair, and two LAMP primer sets were compared by specificity testing to identify the best-performing set. Both assays specifically detected F. subpinicola with no cross-amplification in the tested non-target fungi. Limits of detection were 9.97 copies/μL for fluorescent RAA (25 min), 9.97 × 10² copies/μL for RAA-LFD (15 min), and 9.97 × 10³ copies/μL for LAMP (35 min). In 30 increment core samples from A. georgei var. smithii, all methods consistently detected samples with obvious decay, while fluorescent RAA additionally yielded positives in some apparently asymptomatic samples, indicating promise for early or low-abundance screening. Together, these assays constitute a tiered and application-oriented detection system, enabling flexible selection of diagnostic approaches according to sensitivity requirements, operational conditions, and field surveillance needs for heart rot of A. georgei var. smithii. Full article

Deep learning has significantly improved landslide identification from remote sensing imagery, but accurately detecting multi-scale landslides under complex backgrounds remains challenging. This study proposes a lightweight YOLOv8-based model, namely YOLO-BEG, incorporating three improvements: a bidirectional feature pyramid network (BiFPN) for enhanced multi-scale feature fusion, an embedded Gaussian attention system (EGS) to improve discrimination under complex backgrounds, and a generalized intersection over union (GIoU) loss to optimize boundary localization. The model was evaluated on two datasets: a vegetation-covered Southwest landslide database and the Sichuan–Tibet Highway database. On the Southwest database, YOLO-BEG improved Precision, Recall, and F1-score by 16%, 13%, and 15% compared with YOLOv8, while using only one tenth of the parameters of Mask R-CNN. In the Sichuan–Tibet Highway database, which has more diverse background conditions, YOLO-BEG outperformed Mask R-CNN and Faster R-CNN by 32% and 13% in F1-score, respectively. These results demonstrate that YOLO-BEG is able to operate with fewer parameters and yield high-precision identification of landslides with different scales under complex backgrounds, making it a rapid and accurate tool for landslide identification. Full article

Wuweiganlu (WGL) is a traditional formulation widely applied in the treatment of rheumatoid arthritis (RA), yet the identity of its bioactive constituents remains inadequately defined. In this study, ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS) and untargeted serum metabolomics were employed to characterize the active components of WGL. Fraxin was identified as a principal compound from WGL. To investigate its therapeutic mechanism in RA, a series of in silico and experimental approaches were conducted. Network pharmacology analysis and RNA sequencing identified heat shock protein family member 8 (HSPA8) as a potential molecular target of Fraxin, which was further validated by molecular docking studies. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses indicated that Fraxin exerts its effects primarily by modulating cell apoptosis through the PI3K signaling pathway. In vitro experiments demonstrated that Fraxin significantly reduced inflammatory responses and downregulated HSPA8 expression in lipopolysaccharide (LPS)-stimulated fibroblast-like synoviocytes (FLs) and macrophages. In vivo, Fraxin administration markedly reduced paw swelling, alleviated bone deformities, and improved bone volume fraction (BV/TV) in male IL1RA-deficient mice exhibiting spontaneous arthritis. Histological analysis confirmed that Fraxin attenuated joint inflammation by modulating the inflammatory microenvironment. Additionally, Fraxin inhibited synovial hyperplasia by regulating mitochondrial membrane potential collapse in FLs. Functional assays revealed that this regulation occurred via the inhibition of HSPA8/PI3K/AKT signaling axis, thereby suppressing aberrant FLS proliferation and contributing to the attenuation of RA progression. Full article

Accurate prediction of embankment settlement and evaluation of its serviceability in permafrost regions are significantly challenged by scarce monitoring data and dynamic, non-stationary settlement processes. To address this, an integrated framework combining change-point detection with a novel dynamic prediction model is proposed. Analysis of long-term monitoring data from the Qinghai–Tibet Railway using the Pettitt test revealed a key change point around 2015, indicating a transition towards stabilization. Subsequently, an SAA-GRU-LSTM hybrid model, employing a dynamic compensation prediction strategy, was developed. The model successfully utilized only early-stage data to forecast future settlement trends, demonstrating robust performance in adapting to the identified abrupt change. Furthermore, by applying established engineering serviceability criteria to both historical and predicted data, the framework enables a dynamic and prospective serviceability assessment. This methodology provides a practical tool for the maintenance and risk management of infrastructure in permafrost environments under conditions of data scarcity and process uncertainty. Full article

Grain-scale heterogeneity strongly influences hydraulic fracture initiation and trajectory in crystalline rocks, yet its contributions to non-planar growth and the interaction of multiple nearby cracks remain insufficiently quantified. To address this gap, we perform numerical experiments on a model containing two parallel pre-existing cracks using a hydro-mechanical phase-field framework, systematically quantifying how mineral distribution and axial compression govern non-planar hydraulic fracture growth and inter-fracture competition. The results demonstrate that mineral distribution is the primary driver of fracture complexity. Even within the same Voronoi tessellation, redistributing minerals alone yields markedly different trajectories, deflections, branching patterns, and final morphologies. Furthermore, non-planar growth follows a stepwise, energy-threshold-driven mechanism. When cracks penetrate strong grains or undergo large-angle deflections, propagation is impeded, and injection pressure builds up. Once a critical energy threshold is reached, accumulated energy is rapidly released along the path of minimum incremental energy, manifested as abrupt pressure drops and rapid crack advance. Additionally, the two nearby fractures exhibit strong mechanical competition. Despite negligible hydraulic interference in low-permeability granite, early growth of one fracture redistributes stresses and suppresses the driving force of the other, resulting in asymmetric development. Finally, axial compression primarily governs the overall propagation orientation and influences local failure modes but has a limited effect on peak pressure relative to mineral distribution. Full article

The Qinghai–Tibet Plateau is undergoing rapid warming and humidification, with altered precipitation regimes increasingly affecting soil nitrogen cycling and N₂O emissions. Denitrification—a key nitrogen transformation pathway—is particularly sensitive to these hydrological changes. Here, we investigated the response of nirK-type denitrifying microbial communities to altered precipitation in an alpine wetland on the northern shore of Qinghai Lake. Using a long-term precipitation manipulation platform with five gradients (ambient, ±25%, and ±50%), we integrated high-throughput sequencing with bioinformatics to systematically assess community shifts. Short-term precipitation treatments did not significantly alter alpha diversity, but markedly restructured community composition. Extreme wetting (+50%) increased within-group heterogeneity. At the phylum level, Proteobacteria remained dominant across all treatments, whereas extreme drought (−50%) suppressed Planctomycetes. At the genus level, Ochrobactrum was enriched under reduced precipitation, while Rhodopseudomonas increased under increased precipitation. Functional predictions indicated that reduced precipitation enhanced nitrogen fixation potential, whereas increased precipitation favored nitrate respiration. Soil pH and carbon fractions were the key environmental filters driving community variation. Ecological process analysis revealed that community assembly was entirely governed by deterministic processes, specifically variable selection. Together, these findings elucidate how precipitation shifts reconfigure the structure and functional potential of denitrifying microbial communities in alpine wetlands, primarily via changes in soil pH and moisture under variable selection. This work provides critical insights into microbial regulation of the nitrogen cycle on the Tibetan Plateau under ongoing climate change. Full article

Groundwater is a strategic resource for maintaining ecological balance and supporting human development in arid inland basins. However, under the dual pressures of climate change and human activities, it faces threats in both quantity and quality. This study selects the Chahan Usu River watershed in the eastern Qaidam Basin, a typical arid inland basin on the Tibetan Plateau, to assess the current quality of groundwater resources and reveal the formation mechanisms and material sources of its hydrochemistry. The results show that the groundwater in the watershed is generally weakly alkaline, with some areas exhibiting high salinity. The dominant cations and anions are Na⁺ and Cl⁻, respectively. The hydrochemical type is mainly Cl-Na, with a minority being mixed Cl-Mg·Ca. Overall, the groundwater in the watershed is suitable for domestic use. However, in the middle and lower reaches of the Chahan Usu River, nitrate and ammonia nitrogen contamination reduce its suitability. Meanwhile, although long-term use of this groundwater would not lead to soil degradation, its widespread high salinity and high sodium content make it unsuitable for irrigation. Water–rock interactions with evaporites and silicate rocks are the main mechanisms controlling groundwater chemistry in the watershed. Among them, halite minerals contribute most of the Na⁺ and Cl⁻, while sulfate minerals provide Ca²⁺ and SO₄²⁻. In addition, cation exchange is widespread. This study provides a reference for ensuring the security and sustainable development of groundwater resources on the plateau. Full article

Despite the essential role of soil microbial communities in driving nutrient cycling within alpine meadows, their distribution patterns along elevational gradients and their responses to environmental changes remain largely unexplored. To investigate this, soil samples were collected from five elevations (3300–4500 m) in the northeastern Qinghai–Tibet Plateau to analyze bacterial community composition and diversity, as well as their associations with soil physicochemical properties and enzyme activities. The results showed significant variation in bacterial community composition and diversity across elevations. Actinomycetota, Pseudomonadota, and Acidobacteriota were the dominant phyla at all sampling sites. Community diversity, measured by the Shannon index, generally increased with elevation, peaking at 4500 m and lowest at 3300 m. Pearson correlation analysis and redundancy analysis (RDA) indicated that soil bacterial community structure was significantly correlated with both soil nutrient factors and enzyme activities. Among these variables, total potassium, available phosphorus, catalase, and urease were strongly correlated with bacterial community differentiation. In addition, PERMANOVA results showed that elevation was the primary factor driving community variation, explaining a substantial proportion of the variation in community composition at a statistically significant level. Overall, this study highlights the distribution of bacterial communities in alpine meadow soils along an elevational gradient and their environmental associations, providing foundational data for understanding microbial community responses to environmental changes in alpine ecosystems. Full article

Against the backdrop of ongoing climate change, the Qinghai–Tibet Plateau, a region highly sensitive to climatic variation, exhibits intricate spatiotemporal patterns in reference crop evapotranspiration (ET_O), with significant implications for regional water-resource planning. This study selected four agro-climatic zones across the plateau region (TSA, TSH, TAZ, and WCH). Long-term daily observations from 28 meteorological stations were used to estimate ET_O via the FAO 56 Penman–Monteith equation. This extensive dataset enabled robust trend analysis using the Mann–Kendall test, alongside a cloud-model framework, and analyses of sensitivity and contributions to evaluate ET_O’s spatiotemporal evolution, its distributional uncertainty, and the underlying drivers. Results reveal pronounced regional heterogeneity in the interannual variability of ET_O. Annual ET_O declined in TSH and TSA (trend rates of −1.12 and −6.58 mm·10a⁻¹, respectively) and increased in TAZ and WCH (15.76 and 10.74 mm·10a⁻¹, respectively). At monthly and seasonal timescales, ET_O exhibited an unimodal pattern, with the greatest stability in winter and spring and lower stability in summer and autumn. The cloud-model parameter He indicates that ET_O stability is greatest in TSH and weakest in WCH, with He values of 7.15 and 12.29 mm, respectively. Contribution-rate analyses identify T_max and T_mean as the principal determinants of rising ET_O across all study zones, reflecting the largest individual contributions. Temperature-related factors together account for the majority of ET_O variability across the regions, with their absolute contributions ranging from 5.61% to 8.63%, well above those of aerodynamic factors (0.62–1.78%). Stability assessments indicate that ET_O is generally more unstable than its meteorological drivers, with substantial regional disparities, implying that ET_O evolution cannot be explained by a single controlling factor. Overall, the study characterizes the uncertainty in ET_O variations under complex terrain, highlights the value of the cloud model for refined hydrological assessments, and provides a scientific basis for adaptive agricultural water-resource management in the region. Full article

Volatile fatty acids (VFAs), the primary end-products of microbial fermentation in the ruminant forestomach, supply approximately 70% of the host’s energy requirements and play a pivotal role in maintaining energy homeostasis. While the mechanisms governing ruminal VFA production, absorption, and metabolism are well-characterized in common ruminants like dairy and beef cattle, a systematic integration of these processes in yaks, an iconic species long-adapted to the extreme Qinghai–Tibet Plateau, remains incomplete. This review synthesizes current knowledge on the entire VFA pathway in the yak rumen, from production to tissue metabolism. We detail the critical roles of functional microbes, including fibrolytic bacteria and Prevotella, in VFA synthesis and how their activity is dynamically regulated by dietary composition and seasonal shifts. Building on the unique structural features of the yak rumen epithelium, the review analyzes VFA absorption mechanisms involving both passive diffusion and carrier-mediated transport. Furthermore, we systematically outline the metabolic fates and energy partitioning strategies of VFAs across the rumen epithelium, liver, and peripheral tissues. This synthesis aims to elucidate the highly efficient and adaptive physiological basis of VFA metabolism that underpins the yak’s exceptional ability to utilize energy under the low-energy conditions of the high-altitude environment. Ultimately, this work seeks to provide a theoretical foundation for understanding plateau-adapted energy efficiency and to inform precision nutritional strategies for ruminants in alpine regions. Full article

To prepare high-temperature-resistant dielectric composite films, a novel three-dimensional nanofiller was fabricated using carboxylated polyarylene ether nitrile as a bridge, which tightly loads BaTiO₃ nanoparticles onto WS₂ nanosheets (WS₂@BT) via in situ chemical bonding. Afterward, the WS₂@BT nanofiller was introduced into the polyarylene ether nitrile (PEN) matrix, and high-temperature heat treatment was performed to form a crosslinked network, yielding CPEN/WS₂@BT nanocomposites. Notably, the modified WS₂@BT effectively improves the compatibility between the nanoparticles and the PEN matrix, which is superior to the compatibility of unmodified nanofillers with the matrix. Moreover, after crosslinking, CPEN/WS₂@BT exhibits excellent comprehensive performance: when the filler content is 30 wt%, its glass transition temperature (T_g) reaches 257.83 °C, significantly higher than that of PEN/WS₂@BT, and its dielectric constant is 193% higher than that of pure CPEN. In addition, the dielectric temperature coefficient remains below 1 × 10⁻³ °C⁻¹ in the range of 25–220 °C. Overall, this work provides an effective and reliable strategy for preparing high-performance, high-temperature-resistant composite dielectric films. Full article