Search Results (9,860)

The ATL03 data from the photon-counting LiDAR onboard the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) holds substantial potential for shallow-water bathymetry due to its high sensitivity and broad spatial coverage. However, distinguishing signal photons from noise in low-photon-density and complex terrain environments remains a significant challenge. This study proposes an adaptive photon extraction algorithm based on linear feature analysis, incorporating resolution adjustment, segmented Gaussian fitting, and linear feature-based signal identification. To address the reduction in signal photon density with increasing water depth, the method employs a depth-dependent adaptive neighborhood search radius, which dynamically expands into deeper regions to ensure reliable local feature computation. Experiments using eight ICESat-2 datasets demonstrated that the proposed method achieves average precision and recall values of 0.977 and 0.958, respectively, with an F1 score of 0.967 and an overall accuracy of 0.972. The extracted bathymetric depths demonstrated strong agreement with the reference Continuously Updated Digital Elevation Model (CUDEM), achieving a coefficient of determination of 0.988 and a root mean square error of 0.829 m. Compared to conventional methods, the proposed approach significantly improves signal photon extraction accuracy, adaptability, and parameter stability, particularly in sparse photon and complex terrain scenarios. In comparison with the DBSCAN algorithm, the proposed method achieves a 30.0% increase in precision, 17.3% improvement in recall, 24.3% increase in F1 score, and 22.2% improvement in overall accuracy. These findings confirm the effectiveness and robustness of the proposed algorithm for ICESat-2 shallow-water bathymetry applications. Full article

This study investigated the effects of four selenium fertilizers (nano-Se, EDTA-chelated Se, organic Se, and microbial Se) at three concentrations (50, 25, and 12.5 mg·L⁻¹) on garlic (Allium sativum L. cv. ‘Xusuan 918’) through foliar application during critical growth stages. Comprehensive evaluation combining agronomic traits, yield components, nutritional quality (soluble sugars, vitamin C), and selenium accumulation revealed distinct fertilizer-specific responses. Organic Se at 50 mg·L⁻¹ (O1) maximized vegetative growth (21.83% increased plant spread), while 25 mg·L⁻¹ microbial Se (M2) showed optimal yield enhancement (10.04% higher bulb dry weight vs. CK). Notably, 50 mg·L⁻¹ nano-Se (N1) achieved simultaneous improvement in nutritional quality and selenium biofortification (29-fold bulb Se enrichment). Principal component analysis integrated with membership function method identified N1 treatment (D-value = 0.571) as the most effective protocol for selenium-enriched garlic production, demonstrating the importance of fertilizer selection for crop-specific selenium management strategies. Full article

Differences in venom within snake species can affect the efficacy of antivenom, but how this variation manifests across broad geographical scales remains poorly understood. Naja atra envenoming causes severe morbidity in China, yet whether intraspecific venom variation exists across mainland regions is unknown. We collected venom samples from seven biogeographical regions (spanning > 2000 km latitude). Venom lethality, systemic toxicity (organ damage biomarkers and coagulopathy), and histopathology of major organs were assessed. Neutralization by antivenom and label-free quantitative proteomics (LC-MS/MS) were also performed. The results revealed a non-uniform LD₅₀, with venom from Yunnan exhibiting the highest lethality (2.1-fold higher than venom from Zhejiang, p < 0.001). Commercial antivenom showed lower neutralization efficacy against the venom from the Yunnan, Guangxi, and Guangdong regions. Regarding organ damage and coagulopathy, venom from Yunnan caused severe liver damage, while venom from the Zhejiang region induced significant coagulopathy. Finally, proteomic profiles identified 175 proteins: venom from Yunnan was dominated by phospholipases, contrasting with eastern regions (Anhui/Zhejiang: cytotoxins CTXs > 30%). Venom from Guangdong contained higher levels of the weak neurotoxin NNAM2 (5.2%). Collectively, significant geographical divergence exists in Chinese Cobra venom composition, systemic toxicity, and antivenom susceptibility, driven by differential expression of key toxins. Our study provides a molecular basis for precision management of snakebites, and we call for optimized antivenom production tailored to regional variations. Full article

Background: Bladder cancer is a common and heterogeneous malignancy of the urinary tract. Traditional chemotherapy using bivalent platinum drugs such as cisplatin(CDDP) is often limited by severe side effects and acquired resistance. To overcome these limitations, we explored a novel Pt(IV) prodrug, DNP, designed to release both cytotoxic cisplatin and the anti-inflammatory cyclooxygenase-2 (COX-2) inhibitor naproxen(NPX). Methods: We evaluated the cytotoxic activity of DNP using both two-dimensional (2D) monolayer and three-dimensional (3D) spheroid models of bladder cancer cells. Transcriptomic analysis via RNA-seq identified apoptosis- and inflammation-related signaling pathways modulated by DNP. RNA-seq-based transcriptomic profiling revealed that DNP regulates signaling pathways associated with apoptosis and inflammation. The anti-inflammatory effects were evaluated using a lipopolysaccharide (LPS)-induced macrophage model, while the in vivo antitumor efficacy was assessed in an orthotopic MB49 bladder cancer model. Results: Compared with CDDP, DNP significantly increased intracellular platinum accumulation and exhibited superior cytotoxicity. It effectively inhibited tumor proliferation, induced apoptosis, and attenuated inflammation both in vitro and in vivo. Conclusions: These findings suggest that DNP exerts dual antitumor effects through enhanced delivery of cytotoxic and anti-inflammatory agents, offering a promising strategy for bladder cancer therapy. Full article

As a strategic crop of dry farming in northern China, the photosynthetic characteristics and stress resistance of foxtail millet (Setaria italica L.) are crucial to yield formation. This study aimed to explore the physiological characteristics of various foxtail millet varieties and screen high-efficiency varieties adapted to semi-arid climates. In the agro-pastoral ecotone of northern Shanxi Province, the physiological and ecological parameters, etc. of six cultivars were measured. The results showed that different cultivars had bimodal diurnal photosynthetic curves with distinct peak values and midday depression degrees, reflecting varied responses to high midday temperature and light stress. Dabaigu and Jingu 21 performed superiorly, with mean daily net photosynthetic rates (Pn) of 22.99 and 20.72 µmol·m⁻²·s⁻¹, significantly higher than Jinmiao K1 (12.87 µmol·m⁻²·s⁻¹). Chlorophyll fluorescence analysis showed Dabaigu had higher potential activity (F_v/F₀) of 3.98 than Jinmiao K1 (2.40). Jingu 21 synergistically optimized plant height, stem diameter, and biomass accumulation. Dabaigu and Jingu 21 are elite cultivars for the agro-pastoral ecotone of northern Shanxi Province due to high photosynthetic efficiency, strong photoprotection, and morphological plasticity. Full article

As critical assets for surveillance, reconnaissance, and transport, shipborne helicopters play an indispensable role in modern maritime operations. Ensuring the safety and stability of shipboard landings is therefore of paramount importance, particularly under complex sea conditions. This study presents a comprehensive investigation into the dynamic interaction between helicopters and moving ships during the landing phase, with a particular emphasis on the influence of ship motions on the unsteady aerodynamic flow field and rotor loads. A coupled numerical–theoretical framework is developed, which overcomes the limitations of traditional models that typically consider static or single-degree-of-freedom (SDOF) ship motions. This work systematically analyzes the effects of multi-degree-of-freedom (MDOF) ship motions—including roll, pitch, and heave—on the coupled aerodynamic environment and rotor dynamic response. The results demonstrate that each motion component imposes a distinct influence on the flow-field characteristics, with pitch identified as the dominant contributor to turbulence intensity, particularly during the mid-to-late landing phase. Furthermore, it is found that a linear superposition of individual motions cannot accurately represent the combined effect of MDOF motions. Instead, their interaction leads to complex nonlinear effects, which may attenuate certain flow instabilities. These findings provide critical insights into ship–helicopter dynamic coupling and offer a scientific basis for improving landing safety under adverse sea conditions. Full article

Porous CaCO₃ vaterite particles have been widely used as drug carriers for biomedical applications due to their high biocompatibility and low production costs. However, controlling the particle size and porosity of CaCO₃ nanoparticles with the desired crystalline phase is still challenging. In this study, we have systematically investigated the preparation of CaCO₃ nanoparticles under various conditions including precursor types/ratios/concentrations, additive concentrations (ethylene glycol), and temperatures. The materials were fully characterized by optical microscopy, scanning and transmission electron microscopy, infrared spectroscopy, powder X-ray diffraction, dynamic laser scattering, thermogravimetric analysis, and gas sorption. The impacts of the reaction parameters were rationalized and the mechanism for the formation of porous vaterite particles was suggested. It was possible to produce porous vaterite nanoparticles (200 nm) under the optimized conditions, which were further used as drug carrier to upload a model drug curcumin. The potential of using these vaterite particles for controlled drug release was demonstrated. Full article

Partial organic substitution (POS) is a promising strategy to enhance soil fertility and agricultural sustainability. However, the mechanisms by which varying organic substitution ratios affect soil quality and latex yields in rubber plantations remain unclear. We conducted a two-year field experiment in a rubber plantation with six treatments: no fertilizer (CK), 100% synthetic fertilizer (NPK), and synthetic nitrogen fertilizer substituted with 25% (25 M), 50% (50 M), 75% (75 M), and 100% (100 M) manure. The results indicated that POS treatments significantly increased pH, soil organic carbon (SOC), total phosphorus (TP), total nitrogen (TN), NH₄⁺-N, enzyme activity, and leaf nutrient (C, N, and P) content compared to NPK. Compared with NPK, the soil quality (evaluated through the soil quality index, SQI) increased by 15.30–43.42% under POS across both years, with maximal values observed at 50 M (2020) and 75 M (2021); similarly, the latex yield increased by 2.10–18.60%. SOC, NO₃⁻-N,C:P ratio, TN, and pH are the key factors that influence soil quality and latex yield. Structural equation modeling indicated that fertilization and soil factors collectively explained 82% of the variation in latex yield. These results demonstrated that POS effectively alleviated soil acidity, enhanced soil quality, and improved latex productivity, with 50% manure substitution treatment (50M) identified as the optimal short-term substitution strategy in rubber plantations. Full article

Conventional unconditional random field (URF) models were shown to neglect in-situ monitoring data and thus misrepresent real slope stability. To address this, a conditional random field (CRF) generator was proposed, in which Fast Fourier Transform (FFT) filtering was coupled with co-Kriging to assimilate site observations. A representative three-bench slope was adopted, and the failure-mode distribution and the statistics of the factor of safety (FoS) produced by the URF, the independent random field (IRF), and the CRF were examined across bedding-dip angles of 15–75° and two cross-correlation states ($\rho$ = −0.2, 0). It was found that eliminating cross-correlation decreased the mean FoS by 0.006, increased its standard deviation by 10.26%, and raised the frequency of low-FoS events from 7.49% to 12.30%. When field constraints were imposed through the CRF, the probability of through-going failure was reduced by 12%, the mean FoS was increased by 0.01, the standard deviation was reduced by 15.38%, and low-FoS events were suppressed to 2.30%. The CRF framework was thus demonstrated to integrate stochastic analysis with field measurements, enabling more realistic reliability assessment and proactive risk management of slopes. Full article

Technological advances have expanded the use of single-crystal in microscale applications—particularly in infrared optics, electronics, and aerospace. Conducting research on the surface quality of micro-milling processes for single-crystal superalloys has become a key factor in expanding their applications. In this paper, the nickel-based single-crystal superalloy DD5 is selected as the test object, and the finite element analysis software ABAQUS 2022 version is used to conduct a simulation study on its micro-scale milling process with reasonable milling parameters. A three-factor five-level L₂₅(5³) slot milling orthogonal experiment is conducted to investigate the effects of milling speed, milling depth, and feed rate on its milling force and surface quality, respectively. The results show that the milling depth has the greatest impact on the milling force during the micro-milling process, while the milling speed has the greatest influence on the surface quality. Finally, based on the experimental data, the optimal parameter combination for micro-milling nickel-based single-crystal superalloy DD5 parts is found—when the milling speed is 1318.8 mm/s; the milling depth is 12 µm; the feed rate is 20 µm/s; and the surface roughness value is at its minimum, indicating the best surface quality—which has certain guiding significance for practical machining. Full article

Inflammatory bowel disease (IBD), a chronic inflammatory disorder with relapsing/remitting characteristics, lacks reliable non-invasive biomarkers for accurate diagnosis and longitudinal monitoring. This study explored salivary exosomal miRNAs as potential biomarkers to address this unmet clinical need. Using discovery (24 IBD patients [11 active, 13 remission] and 6 healthy controls [HCs]) and validation cohorts (102 IBD patients [53 active, 49 remission] and 18 HCs), we analyzed miRNA profiles via reverse transcription quantitative PCR (RT-qPCR). Receiver operating characteristic (ROC) curves evaluated diagnostic performance, with area under the curve (AUC) quantifying discriminatory capacity. Initial screening revealed 23 miRNAs significantly upregulated in IBD salivary exosomes. An 8-miRNA signature distinguished IBD patients from HCs in validation analyses, with five miRNAs (hsa-miR-1246, hsa-miR-142-3p, hsa-miR-16-5p, hsa-miR-301a-3p, and hsa-miR-4516) showing strong correlations with disease activity. The combination of hsa-miR-16-5p and hsa-miR-4516 achieved robust discrimination (AUC = 0.925 for IBD vs. HCs; AUC = 0.82 for active disease vs. remission). A composite model integrating all five miRNAs demonstrated superior performance (AUC = 1.00 for IBD/HC differentiation; AUC = 0.86 for disease activity assessment). These findings reveal dynamic associations between salivary exosomal miRNA signatures and IBD progression, underscoring their utility as non-invasive diagnostic tools. This approach enables serial sampling, enhances patient compliance, and provides actionable insights for personalized disease management, establishing salivary exosomal miRNAs as promising candidates for clinical translation in IBD care. Full article

During electron beam cold hearth melting (EBCHM) of Ti-6wt%Al-4wt%V titanium alloy, aluminum volatilization causes compositional segregation in the ingot, significantly degrading material performance. Traditional methods (e.g., the Langmuir equation) struggle to accurately predict aluminum diffusion and compensation behaviors, while computational fluid dynamics (CFD), although capable of resolving multiphysics fields in the molten pool, suffer from high computational costs and insufficient research on segregation control. To address these issues, this study proposes a CFD-machine learning (backpropagation neural network, CFD-ML(BP)) approach to achieve precise prediction and optimization of aluminum segregation. First, CFD simulations are performed to obtain the molten pool’s temperature field, flow field, and aluminum concentration distribution, with model reliability validated experimentally. Subsequently, a BP neural network is trained using large-scale CFD datasets to establish an aluminum concentration prediction model, capturing the nonlinear relationships between process parameters (e.g., casting speed, temperature) and compositional segregation. Finally, optimization algorithms are applied to determine optimal process parameters, which are validated via CFD multiphysics coupling simulations. The results demonstrate that this method predicts the average aluminum concentration in the ingot with an error of ≤3%, significantly reducing computational costs. It also elucidates the kinetic mechanisms of aluminum volatilization and diffusion, revealing that non-monotonic segregation trends arise from the dynamic balance of volatilization, diffusion, convection, and solidification. Moreover, the most uniform aluminum distribution (average 6.8 wt.%, R² = 0.002) is achieved in a double-overflow mold at a casting speed of 18 mm/min and a temperature of 2168 K. Full article

Developing advanced nuclear fuel technologies is critical for high-performance applications such as nuclear thermal propulsion (NTP). This study explores the feasibility of direct ink writing (DIW) for fabricating ceramic pellets for potential nuclear applications. Zirconium carbide (ZrC) is used as a matrix material and vanadium carbide (VC) is used as a surrogate for uranium carbide (UC) in this study. A series of ink formulations were developed with varying concentrations of VC and nanocrystalline cellulose (NCC) to optimize the rheological properties for DIW processing. Post-sintering analysis revealed that conventionally sintered samples at 1750 °C exhibited high porosity (>60%), significantly reducing the compressive strength compared to dense ZrC ceramics. However, increasing VC content improved densification and mechanical properties, albeit at the cost of increased shrinkage and ink flow challenges. Spark plasma sintering (SPS) achieved near-theoretical density (~97%) but introduced geometric distortions and microcracking. Despite these challenges, this study demonstrates that DIW offers a viable route for fabricating ZrC-based ceramic structures, provided that sintering strategies and ink rheology are further optimized. These findings establish a baseline for DIW of ZrC-based materials and offer valuable insights into the porosity control, mechanical stability, and processing limitations of DIW for future nuclear fuel applications. Full article

Sirex noctilio is an invasive pest that contributes to pine tree decline, leading to visual symptoms such as needle discoloration, crown thinning, and eventual tree death. Detecting these visible phenotypic signs from drone imagery is challenging due to elongated or irregular crown shapes, weak color differences, and occlusion within dense forests. This study introduces YOLO-PTHD, a lightweight deep learning model designed for detecting visible signs of pine decline in UAV images. The model integrates three customized components: Strip-based convolution to capture elongated tree structures, Channel-Aware Attention to enhance weak visual cues, and a scale-sensitive dynamic loss function to improve detection of minority classes and small targets. A UAV-based dataset, the Sirex Woodwasp dataset, was constructed with annotated images of weakened, and dead pine trees. YOLO-PTHD achieved an mAP of 0.923 and an F1-score of 0.866 on this dataset. To evaluate the model’s generalization capability, it was further tested on the Real Pine Wilt Disease dataset from South Korea. Despite differences in tree symptoms and imaging conditions, the model maintained strong performance, demonstrating its robustness across different forest health scenarios. Field investigations targeting Sirex woodwasp in outbreak areas confirmed that the model could reliably detect damaged trees in real-world forest environments. This work demonstrates the potential of UAV-based visual analysis for large-scale phenotypic surveillance of pine health in forest management. Full article

Standoff tracking of a ground-moving target with a single fixed-wing unmanned aerial vehicle (UAV) is vulnerable to occlusions around the target, such as buildings and terrain, which can obstruct the line of sight (LOS) between the UAV and the target, resulting in tracking failures. To address these challenges, multi-UAV cooperative tracking is often employed, offering multi-angle coverage and mitigating the limitations of a single UAV by maintaining continuous target visibility, even when one UAV’s LOS is obstructed. Building on this idea, we propose a specialized two-layer framework for dual-UAV cooperative target tracking. This framework comprises a decision-making layer and a guidance layer. The decision-making layer employs a state-transition-based distributed role transition algorithm for dual UAVs. Here, the UAVs periodically share state variables based on their target observability. In the guidance layer, we devise a velocity-vector-field-based controller to simplify the complexity of controller design for cooperative tracking. To validate the proposed framework, three numerical simulations and one hardware-in-the-loop (HIL) simulation were conducted. These simulations confirmed that the role transition algorithm functions properly even under occlusion conditions. Additionally, the standoff tracking guidance controller demonstrated superior performance compared to baseline methods in terms of tracking accuracy and stability. Full article