Search Results (5,338)

The efficient and rational development of hydropower in the Lancang–Mekong River Basin can promote green energy transition, reduce carbon emissions, prevent and mitigate flood and drought disasters, and ensure the sustainable development of the entire basin. In this study, based on publicly available hydrometeorological observation data and satellite remote sensing monitoring data from 2001 to 2020, a machine learning model of the Lancang–Mekong Basin was developed to reconstruct the basin’s hydrological processes, and identify the occurrence patterns and influencing mechanisms of water-related hazards. The results show that, against the background of climate change, the Lancang–Mekong Basin is affected by the increasing frequency and intensity of extreme precipitation events. In particular, Rx1day, Rx5day, R10mm, and R95p (extreme precipitation indicators determined by the World Meteorological Organization’s Expert Group on Climate Change Monitoring and Extreme Climate Events) in the northwestern part of the Mekong River Basin show upward trends, with the average maximum daily rainfall increasing by 1.8 mm/year and the total extreme precipitation increasing by 18 mm/year on average. The risks of flood and drought disasters will continue to rise. The flood peak period is mainly concentrated in August and September, with the annual maximum flood peak ranging from 5600 to 8500 m³/s. The Stung Treng Station exhibits longer drought duration, greater severity, and higher peak intensity than the Chiang Saen and Pakse Stations. At the Pakse Station, climate change and hydropower development have altered the non-drought proportion by −12.50% and +15.90%, respectively. For the Chiang Saen Station, the fragmentation degree of the drought index time series under the baseline, naturalized, and hydropower development scenarios is 0.901, 1.16, and 0.775, respectively. These results indicate that hydropower development has effectively reduced the frequency of rapid drought–flood transitions within the basin, thereby alleviating pressure on drought management efforts. The regulatory role of the cascade reservoirs in the Lancang River can mitigate risks posed by climate change, weaken adverse effects, reduce flood peak flows, alleviate hydrological droughts in the dry season, and decrease flash drought–flood transitions in the basin. The research findings can enable basin managers to proactively address climate change, develop science-based technical pathways for hydropower dispatch, and formulate adaptive disaster prevention and mitigation strategies. Full article

Straw removal is a method used to reduce cadmium (Cd) concentration in contaminated farmland. Experiments in Hunan Province tested different stubble heights (0, 15, 30, 45 cm) in three Cd-polluted paddy fields with different contamination levels. The results showed that lower stubble heights resulted in larger straw biomass and more Cd removed from the field, while the residual biomass and Cd returned to the field decreased accordingly. At stubble heights of 0, 15, 30, and 45 cm, the removed straw biomass accounted for 100%, 69.19%, 48.84%, and 28.17% of the total straw biomass, respectively. The corresponding Cd removal amounts were 12.89, 7.18, 4.18, and 1.83 g ha⁻¹, which constituted 100%, 54.06%, 29.85%, and 12.54% of the total Cd accumulation in straw for the season, respectively. According to the fitted curve, the biomass of returned and removed straw was equal at a stubble height of 31 cm, while at 23 cm, the Cd return and removal amounts were balanced. Rice varieties Huanghuazhan and Nongxiang 42 had better Cd removal but risked grain Cd exceeding limits. Since Cd concentration in straw determines removal efficiency, varieties with high straw Cd accumulation and low grain Cd are more suitable for remediation, rather than high-Cd-accumulating types. Full article

The growing use of remote-sensing technologies has placed greater demands on object-detection algorithms, which still face challenges. This study proposes a hierarchical adaptive feature aggregation network (HAF-YOLO) to improve detection precision in remote-sensing images. It addresses issues such as small object size, complex backgrounds, scale variation, and dense object distributions by incorporating three core modules: dynamic-cooperative multimodal fusion architecture (DyCoMF-Arch), multiscale wavelet-enhanced aggregation network (MWA-Net), and spatial-deformable dynamic enhancement module (SDDE-Module). DyCoMF-Arch builds a hierarchical feature pyramid using multistage spatial compression and expansion, with dynamic weight allocation to extract salient features. MWA-Net applies wavelet-transform-based convolution to decompose features, preserving high-frequency detail and enhancing representation of small-scale objects. SDDE-Module integrates spatial coordinate encoding and multidirectional convolution to reduce localization interference and overcome fixed sampling limitations for geometric deformations. Experiments on the NWPU VHR-10 and DIOR datasets show that HAF-YOLO achieved mAP50 scores of 85.0% and 78.1%, improving on YOLOv8 by 4.8% and 3.1%, respectively. HAF-YOLO also maintained a low computational cost of 11.8 GFLOPs, outperforming other YOLO models. Ablation studies validated the effectiveness of each module and their combined optimization. This study presents a novel approach for remote-sensing object detection, with theoretical and practical value. Full article

Mars has been a primary focus of planetary science, with significant advancements over the past two decades across disciplines including geological evolution, surface environment, and atmospheric and space science. However, the rapid growth of the related literature has rendered traditional manual review methods increasingly inadequate. This inadequacy is particularly evident in interdisciplinary research, which is often characterized by dispersed topics and complex semantics. To address this challenge, this study proposes an automated analysis framework based on natural language processing (NLP) to systematically review the Martian research in Earth and space science over the past two decades. The research database contains 151,196 Mars-related sentences extracted from 10,655 publications spanning 2001 to 2024. Using machine learning techniques, the framework clusters Mars-related sentences into semantically coherent groups and applies topic modeling to extract core research themes. It then analyzes their temporal evolution across the Martian solid, surface, atmosphere, and space environments. Finally, through sentiment analysis and semantic matching, it highlights unresolved scientific questions and potential directions for future research. This approach offers a novel perspective on the knowledge structure underlying Mars exploration and demonstrates the potential of NLP for large-scale literature analysis in planetary science. The findings potentially provide a structured foundation for building an interdisciplinary, peer-reviewed Mars knowledge base, which may inform future scientific research and mission planning. Full article

Knowledge graph technology holds significant importance for efficient fault diagnosis in household appliances. However, the scarcity of public fault diagnosis data and the lack of automated knowledge extraction pose major challenges to knowledge graph construction. To address issues such as ambiguous entity boundaries, severe entity nesting, and poor entity extraction performance in fault diagnosis texts, this paper proposes a dual-strategy progressive knowledge extraction framework. First, to tackle the high complexity of fault diagnosis texts, an entity recognition model named RoBERTa-zh-BiLSTM-MUL-CRF is designed, improving the accuracy of nested entity extraction. Second, leveraging the semantic understanding capability of large language models, a progressive prompting strategy is adopted for ontology alignment and relation extraction, achieving automated knowledge extraction. Experimental results show that the proposed named entity recognition model outperforms traditional models, with improvements of 3.87%, 5.82%, and 2.05% in F1-score, recall, and precision, respectively. Additionally, the large language model demonstrates better performance in ontology alignment compared to traditional machine learning models. The constructed knowledge graph for household appliance fault diagnosis integrates structured fault diagnosis information. It effectively processes unstructured fault texts and supports visual queries and entity tracing. This framework can assist maintenance personnel in making rapid judgments, thereby improving fault diagnosis efficiency. Full article

The safe and reliable operation of autonomous vehicles is contingent on comprehensive testing. However, the operational scenarios are inexhaustible. Corner cases, which critically influence autonomous vehicle safety, occur at an extremely low probability and follow a long-tail distribution. Corner cases can be defined as combinations of driving task and scenario elements. These scenarios are characterized by low probability, high risk, and a tendency to reveal functional limitations inherent to autonomous driving systems, triggering anomalous behavior. This study constructs a novel corner case dataset using naturalistic driving data, specifically tailored for autonomous vehicle testing. A scenario marginality quantification method is designed to analyze multi-source naturalistic driving data, enabling efficient extraction of corner cases. Heterogeneous scenarios are systematically transformed, resulting in a dataset characterized by diverse interaction behaviors and standardized formatting. The results indicate that the scenario marginality of the dataset constructed in this study is 2.78 times that of mainstream naturalistic driving datasets, and the scenarios exhibit considerable diversity. The trajectory and velocity fluctuations, quantified at 0.013 m and 0.021 m/s, respectively, are consistent with the kinematic characteristics of real-world driving scenarios. These results collectively demonstrate the dataset’s high marginality, diversity, and applicability. Full article

When measuring Stokes parameters using the rotating wave plate method, the angle error of the polarizer’s light transmission axis, the azimuth error of the wave plate’s fast axis, and the phase delay error are key factors restricting accuracy. To address the existing calibration methods’ insufficient accuracy and incomplete consideration of the error parameters, this study constructed an error-transfer analytical model for an in-depth analysis of the principle of measuring Stokes parameters using the rotating wave plate method. It also clarified the quantitative parameter relationship between the measurement, wave plate, and polarizer errors. A device parameter calibration scheme using multi-angle polarized light (horizontally linearly polarized, [1,1,0,0]^T, and 45° linearly polarized, [1,0,1,0]^T) was further proposed, and by using the deviation between the theoretical response of the standard incident light and the actual measurement data, an error equation was established to solve the device parameter error and precisely calibrate the polarization detection device. The experimental results show that after using this method, the calibration error of the Stokes parameters decreased from 4.83% to within 0.46%, significantly overcoming the traditional methods’ limitations regarding incomplete consideration of the error parameters and accuracy improvement, providing a more concise and reliable method for high-precision polarization measurement. Full article

This study develops an integrated environmental–economic assessment framework to evaluate the life cycle environmental impacts and economic costs of CO₂ geological storage and produced water treatment in saline formations in China. Using a case study of a saline aquifer carbon storage project in the Ordos Basin, eight full-chain carbon capture, utilization, and storage (CCUS) scenarios were analyzed. The results indicate that environmental and cost performance are primarily influenced by technology choices across carbon capture, transport, and storage stages. The scenario employing potassium carbonate-based capture, pipeline transport, and brine reinjection after a reverse osmosis treatment (S5) achieved the most balanced outcome. Breakeven analyses under three carbon price projection models revealed that carbon price trajectories critically affect project viability, with a steadily rising carbon price enabling earlier profitability. By decoupling CCUS from power systems and focusing on unit CO₂ removal, this study provides a transparent and transferable framework to support cross-sectoral deployment. The findings offer valuable insights for policymakers aiming to design effective CCUS support mechanisms under future carbon neutrality targets. Full article

An in-depth understanding of the spatiotemporal heterogeneity of ecosystem service (ES) trade-offs and synergies, along with their driving factors, is crucial for formulating key ecological restoration strategies and effectively allocating ecological environmental resources in the Hulunbuir region. This study employed an integrated analytical approach combining the InVEST model, ArcGIS geospatial processing, R software environment, and Optimal Parameter Geographical Detector (OPGD). The spatiotemporal patterns and driving factors of the interaction of four major ES functions in Hulunbuir area from 2000 to 2020 were studied. The research findings are as follows: (1) carbon storage (CS) and soil conservation (SC) services in the Hulunbuir region mainly show a distribution pattern of high values in the central and northeast areas, with low values in the west and southeast. Water yield (WY) exhibits a distribution pattern characterized by high values in the central–western transition zone and southeast and low values in the west. For forage supply (FS), the overall pattern is higher in the west and lower in the east. (2) The trade-off relationships between CS and WY, CS and SC, and SC and WY are primarily concentrated in the western part of Hulunbuir, while the synergistic relationships are mainly observed in the central and eastern regions. In contrast, the trade-off relationships between CS and FS, as well as FS and WY, are predominantly located in the central and eastern parts of Hulunbuir, with the intensity of these trade-offs steadily increasing. The trade-off relationship between SC and FS is almost widespread throughout HulunBuir. (3) Fractional vegetation cover, mean annual precipitation, and land use type were the primary drivers affecting ESs. Among these factors, fractional vegetation cover demonstrates the highest explanatory power, with a q-value between 0.6 and 0.9. The slope and population density exhibit relatively weak explanatory power, with q-values ranging from 0.001 to 0.2. (4) The interactions between factors have a greater impact on the inter-relationships of ESs in the Hulunbuir region than individual factors alone. The research findings have facilitated the optimization and sustainable development of regional ES, providing a foundation for ecological conservation and restoration in Hulunbuir. Full article

Traditional path planning algorithms often fail to simultaneously ensure operational efficiency, energy constraint compliance, and environmental adaptability in agricultural scenarios, thereby hindering the advancement of precision agriculture. To address these challenges, this study proposes a deep reinforcement learning algorithm, MoE-D3QN, which integrates a Mixture-of-Experts mechanism with a Bi-directional Long Short-Term Memory model. This design enhances the efficiency and robustness of UAV path planning in agricultural environments. Building upon this algorithm, a hierarchical coverage path planning framework is developed. Multi-level task maps are constructed using crop information extracted from Sentinel-2 remote sensing imagery. Additionally, a dynamic energy consumption model and a progressive composite reward function are incorporated to further optimize UAV path planning in complex farmland conditions. Simulation experiments reveal that in the two-level scenario, the MoE-D3QN algorithm achieves a coverage efficiency of 0.8378, representing an improvement of 37.84–63.38% over traditional algorithms and 19.19–63.38% over conventional reinforcement learning methods. The redundancy rate is reduced to 3.23%, which is 38.71–41.94% lower than traditional methods and 4.46–42.77% lower than reinforcement learning counterparts. In the three-level scenario, MoE-D3QN achieves a coverage efficiency of 0.8261, exceeding traditional algorithms by 52.13–71.45% and reinforcement learning approaches by 10.15–50.2%. The redundancy rate is further reduced to 5.26%, which is significantly lower than the 57.89–92.11% observed with traditional methods and the 15.57–18.98% reported for reinforcement learning algorithms. These findings demonstrate that the MoE-D3QN algorithm exhibits high-quality planning performance in complex farmland environments, indicating its strong potential for widespread application in precision agriculture. Full article

The O3-type layered oxide materials have the advantage of high specific capacity, which makes them more competitive in the practical application of cathode materials for sodium-ion batteries (SIBs). However, the existing reported O3-type layered oxide materials still have a complex irreversible phase transition phenomenon, and the cycle life of batteries needs, with these materials, to be further improved to meet the requirements. Herein, we performed structural characterization and electrochemical performance tests on O3-NaNi_0.6−xFe_0.25Mn_0.15Mg_xO₂ (x = 0, 0.025, 0.05, and 0.075, denoted as NFM, NFM-2.5Mg, NFM-5.0Mg, and NFM-7.5Mg). The optimized NFM-2.5Mg has the largest sodium layer spacing, which can effectively enhance the transmission rate of sodium ions. Therefore, the reversible specific capacity can reach approximately 148.1 mAh g⁻¹ at 0.2C, and it can even achieve a capacity retention of 85.4% after 100 cycles at 1C, demonstrating excellent cycle stability. Moreover, at a low temperature of 0 °C, it also can keep capacity retention of 86.6% after 150 cycles at 1C. This study provides a view on the cycling performance improvement of sodium-ion layered oxide cathodes with a high theoretical specific capacity. Full article

The Automatic Identification System (AIS) has become a key data source for ship behavior monitoring and maritime traffic management, widely used in trajectory prediction and anomaly detection. However, AIS data suffer from issues such as spatial sparsity, heterogeneous features, variable message formats, and irregular sampling intervals, while vessel trajectories are characterized by strong spatial–temporal dependencies. These factors pose significant challenges for efficient and accurate modeling. To address this issue, we propose a lightweight vessel trajectory prediction framework that integrates Spatial–Temporal Identity encoding with an MLP-Mixer architecture. The framework discretizes spatial and temporal features into structured IDs and uses dual MLP modules to model temporal dependencies and feature interactions without relying on convolution or attention mechanisms. Experiments on a large-scale real-world AIS dataset demonstrate that the proposed STID-Mixer achieves superior accuracy, training efficiency, and generalization capability compared to representative baseline models. The method offers a compact and deployable solution for large-scale maritime trajectory modeling. Full article

The cornea, the transparent anterior window of the eye, critically refracts light and protects intraocular structures. Corneal pathologies, including trauma, infection, chemical injury, metabolic diseases, genetic conditions, and age-related degeneration, can lead to significant visual impairment. While penetrating keratoplasty or full-thickness corneal transplantation remains a standard and effective intervention for severe corneal dysfunction, limitations in donor tissue availability and the risk of immunogenic graft rejection necessitate alternative therapeutic strategies. Furthermore, for cases of isolated epithelial disfunction, a full-thickness cornea graft may not be required or effective. This review examines the potential of corneal epithelial constructs derived from autologous stem cells with functional barrier properties for corneal reconstruction and in vitro pharmacotoxicity testing. In this review, we delineate the current limitations of corneal transplantation, the advantages of stem cell-based approaches, and recent advances in generating engineered corneal epithelium. Finally, we address remaining technical challenges and propose future research directions aimed at clinical translation. Full article

Background/Objectives: Senecio scandens Buch.-Ham. is a flavonoid-rich traditional medicinal plant with established immunomodulatory properties. However, the mechanisms underlying the immunoregulatory and intestinal protective effects of its flavonoid extract (Senecio scandens flavonoids—SSF) remain unclear. This study characterized SSF’s bioactive components and evaluated its efficacy against cyclophosphamide (CTX)-induced immunosuppression and intestinal injury. Methods: The constituents of SSF were identified using UHPLC/Q-Orbitrap/HRMS. Mice with CTX-induced immunosuppression were treated with SSF (80, 160, 320 mg/kg) for seven days. Immune parameters (organ indices, lymphocyte proliferation, cytokine, and immunoglobulin levels) and gut barrier integrity markers (ZO-1, Occludin, Claudin-1 protein expression; sIgA secretion; microbiota composition) were assessed. Network pharmacology combined with functional assays elucidated the underlying regulatory mechanisms. Results: Twenty flavonoids were identified in SSF, with six prototype compounds detectable in the blood. The SSF treatment significantly ameliorated CTX-induced weight loss and atrophy of the thymus and spleen. It enhanced splenic T- and B-lymphocyte proliferation by 43.6% and 29.7%, respectively; normalized the CD4⁺/CD8⁺ ratio (1.57-fold increase); and elevated levels of IL-2, IL-6, IL-10, TNF-α, IFN-γ, IgM, and IgG. Moreover, SSF reinforced the intestinal barrier by upregulating tight junction protein expression and sIgA levels while modulating the gut microbiota, enriching beneficial taxa (e.g., the Lachnospiraceae_NK4A136_group, Akkermansia) and suppressing pathogenic Alistipes. Mechanistically, SSF activated the TLR/MyD88/NF-κB pathway, with isoquercitrin identified as a pivotal bioactive constituent. Conclusions: SSF effectively mitigates CTX-induced immunosuppression and intestinal damage. These findings highlight SSF’s potential as a dual-functional natural agent for immunomodulation and intestinal protection. Subsequent research should validate isoquercitrin’s molecular targets and assess SSF’s clinical efficacy. Full article

In this paper, SiC and Si/SiC ceramics were fabricated using direct laser powder bed fusion with chemical vapor infiltration. Their microstructure, mechanical properties and the impacts of silicon addition were analyzed. The incorporation of silicon led to an increase in the relative density of the silicon carbide ceramics from 76.4% to 78.3% and the compression strength increased from 39 ± 13 MPa to 90 ± 8 MPa after laser powder bed fusion with chemical vapor infiltration. The melting and re-solidification of silicon allows the silicon to encapsulate the silicon carbide grains, changing the microstructure and the failure mechanism of the silicon carbide ceramics, resulting in a small amount of silicon residue. In the LPBF-CVI SiC ceramic specimen, the LPBF-formed SiC exhibits a microhardness of 24.2 ± 1.0 GPa. In LPBF-CVI Si/SiC, the spherical dual-phase structure displays a moderately increased hardness (25.9 ± 4.4 GPa), and the CVI-formed SiC exhibits a hardness of 55.3 ± 9.3 GPa. Full article