Search Results (25,993)

Against the backdrop of global warming and the “warming and moistening” trend in northwestern China, arid inland river basins are highly sensitive to climate change, with their vegetation dynamics strongly controlled by upstream snowmelt water supply. The Keriya River Basin, situated on the northern slope of the Kunlun Mountains and the southern edge of the Taklamakan Desert, exhibits pronounced vertical zonation in vegetation cover and relies heavily on upstream snowmelt water supply for its water resources. To date, there has been a lack of systematic research into the spatiotemporal evolution patterns of long-term NDVI time series in this basin, its multiscale climate responses, and, in particular, future vegetation projections based on CMIP6 multi-scenario analyses and machine learning methods. To address this, this study utilised MODIS NDVI remote sensing data, historical data from the CMIP6 BCC-CSM2-MR model, and monthly temperature, precipitation, and snow cover data for three SSP scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5) and systematically analysed the spatiotemporal differentiation characteristics of NDVI in the Keriya River Basin and its multiscale coupling relationships with climatic factors. A multi-model selection and forecasting framework was developed, integrating feature engineering with the XGBoost machine learning algorithm. The study innovatively introduced a physically constrained scenario scaling factor based on historical correlations and future climate mean values, thereby addressing the bias where machine learning models’ predicted NDVI means converged across different SSP scenarios. This enabled the monthly estimation of NDVI under various emission pathways from 2015 to 2100. The results indicate: (1) During the historical period (2001–2024), the basin’s annual average NDVI showed an overall slight increase; the annual pattern was unimodal, peaking in July and reaching its trough in January–February; NDVI was highest in summer and lowest in winter. (2) NDVI initially increases and then decreases with altitude; the highest NDVI values are observed in the 3000–4000 m altitude band; in the mid-altitude band, NDVI rose significantly after 2010 and peaked in 2017; the low-altitude band exhibits the greatest interannual stability. (3) During the historical period, both temperature and precipitation in the catchment exhibited high levels of fluctuation, with annual mean temperatures ranging from 1.90 to 3.92 °C and annual precipitation ranging from 434.5 to 621.0 mm. NDVI showed a strong positive correlation with temperature (R = 0.86), a relatively strong negative correlation with snow cover (R = −0.71), and virtually no correlation with precipitation, indicating that upstream snowmelt is heat-driven and water-dependent. (4) Under the future SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios, temperature increases are projected to be 0.83 °C, 2.68 °C, and 5.35 °C, respectively, whilst snow cover is projected to decrease by 2.0%, 14.3%, and 34.0%, respectively; The multi-year mean NDVI values predicted using the XGBoost model (validation R² = 0.9097) are 0.0726, 0.0683, and 0.0690, respectively, all characterised by strong seasonal fluctuations. Given that these future projections are based on a single CMIP6 model and a statistical forecasting framework, they are subject to a degree of uncertainty; however, the low-emission scenario (SSP1-2.6) still indicates a trend that is relatively more conducive to maintaining vegetation stability in this region and may provide preliminary scientific guidance for water resource management along the southern margin of the Tarim Basin. Full article

The quality and flavor of vine tea are largely determined by its processing stages, which markedly influence its physical attributes and volatile organic compounds. Elucidating the dynamic changes in physicochemical and aromatic properties throughout processing is, therefore, essential for guiding optimized processing techniques and developing high-quality vine tea products. However, the specific effects of individual processing stages on quality attributes remain poorly understood. In this study, we combined assessments of color and physical properties with untargeted metabolomics, headspace solid-phase microextraction coupled with gas chromatography–mass spectrometry (HS-SPME-GC-MS), relative odor activity value (ROAV), and gas chromatography–olfactometry (GC-O) to identify key compounds contributing to vine tea quality. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were further applied to identify characteristic metabolites associated with aroma and flavor differentiation. A total of 280 volatile organic compounds were identified, among which 14 key VOCs (ROAV ≥ 1, aroma intensity ≥ 0.5) exhibited significant dynamic variation across processing stages. Notably, compounds such as β-ionone, β-myrcene, nonanal, and hexanal displayed higher ROAVs and strong aroma intensities (AI ≥ 1.0), indicating their substantial contribution to overall aroma. Furthermore, the metabolic transformation pathways—primarily including fatty acid degradation and carotenoid cleavage—and the content changes of key aroma-active compounds were inferred across different processing stages. Based on the differential accumulation patterns of the identified volatile markers, the possible involvement of fatty acid degradation and carotenoid cleavage pathways was inferred. Drying and fixation emerged as critical steps for vine tea aroma development, while the non-enzymatic degradation of fatty acids was potentially associated with the formation of its aroma characteristics. This study provides insights that may inform future efforts toward processing optimization and quality improvement of vine tea. Full article

We present a non-collisional mechanism for the formation of symmetric ring galaxies based on the dynamical relaxation of gas on top of quantized vortex configurations in Bose–Einstein Condensate Dark Matter (BECDM) solutions. For this we solve the fully coupled Gross–Pitaevskii–Poisson–Euler (GPPE) system in 3D, allowing for complete gravitational back-reaction between the baryonic gas and the dark matter component. Initializing the luminous matter as an ideal gas with random initial conditions on top of a vortex line with a topological winding number $m=1$, we systematically explore three dark matter self-interaction regimes, attractive, collisionless, and repulsive, for a characteristic ultra-light boson mass of $m_b={10}^{-22}\mathrm{eV}$. Our simulations reveal that the baryonic gas relaxes into stable, highly symmetric rings whose physical diameters from 11 to 16 kpc and total enclosed masses of order ${10}^{10}{\mathrm{M}}_{\odot }$ match the scales of intensely studied benchmarks like Arp 147. We use the detailed data from Arp 147 purely as a physical baseline to provide a structural proof of concept for our model. However, this self-consistent mechanism can be an interesting explanation for isolated ring galaxies-like Hoag-type objects, which lack the nearby companions or tidal debris required by standard collision models. Full article

Rainfall characteristics, differences in catchment storage, soil moisture dynamics, and geophysical properties influence spatial and temporal variability of runoff generation thresholds. This study addresses two questions: (1) Are there nonlinear thresholds of rainfall or antecedent moisture that trigger abrupt changes in agricultural catchments’ hydrological response? (2) What physical characteristics determine runoff generation? We analyzed precipitation and streamflow variability during the 2025–2026 hydrological year at three agricultural catchments (36.4–70.8 km²) in central Chile. Relying on high-resolution observational data and statistical segmented regression, this study focuses on how rainfall, soil moisture, and physical characteristics trigger runoff activation after exceeding specific thresholds. Based on 63 rainfall events, runoff occurred in only 16 events, demonstrating a strong nonlinear response governed by antecedent moisture. Segmented regression revealed consistent activation thresholds across all catchments when combined precipitation and deep soil moisture (PTOT + ASM100) exceeded ~505 mm, evidencing strong subsurface control. However, runoff efficiency diverged sharply along the land-use gradient. The most intensively agricultural catchment exhibited the highest specific peak discharge and a unique rainfall intensity threshold (14.6 mm/h), indicating rapid infiltration-excess runoff driven by degraded soil permeability. Conversely, catchments with higher headwater or riparian forest cover buffered these rapid flows, sustaining baseflow and extending recession times. Full article

The temporal dynamics of forest-associated insects are closely associated with climatic variability, and identifying their temporal patterns and climatic associations is important for understanding insect population dynamics. In this study, automatically recorded Coleoptera and Lepidoptera in Rui’an City, Zhejiang Province, China, were selected as the target insect groups. Based on automatic insect monitoring and meteorological data, the temporal characteristics of recorded insect abundance and their associations with climatic factors were analyzed. Five machine learning models, including Multiple Linear Regression, K-Nearest Neighbors, Random Forest, Support Vector Regression, and Generalized Additive Model, were then developed and compared for predicting recorded insect abundance. The results showed that Coleoptera exhibited periodic fluctuations with cycles of approximately 6–20 days, whereas Lepidoptera displayed persistent seasonal variation. Air temperature, specific humidity, and shortwave radiation were significantly and positively correlated with recorded insect abundance, while wind speed showed a weak negative correlation. Among the five models, Random Forest achieved the best prediction performance, with MAE, RMSE, and R² values of 1.37, 4.85, and 0.82 for Coleoptera and 0.57, 1.16, and 0.84 for Lepidoptera, respectively. Independent-year validation using 2023 data showed reduced predictive performance compared with random validation, indicating limited temporal generalization under the available observation period. These findings demonstrate that integrating temporal characteristics with climatic factors can support the prediction of automatically recorded insect abundance, while longer-term monitoring data are needed to further evaluate model robustness and temporal transferability. Full article

Aluminum nanoparticles (ANPs) possess a core–shell structure, yet the coupled roles of atomic stress and interfacial charge transfer in their slow-heating oxidation remain elusive. This study employs ReaxFF molecular dynamics simulations to investigate the oxidation of six core–shell ANPs with different particle sizes (5–10 nm) and shell thicknesses (0.5–2.0 nm) from 300 K to 1400 K. Results reveal that the stress evolution dictates the oxidation pathway. Thin shells (0.5–1.0 nm) undergo a compressive-to-tensile stress transition, leading to shell rupture at ~1060 K and subsequent outflow and rapid oxidation of Al into clusters, while thick shells (1.5–2.0 nm) maintain compressive confinement, preventing rupture but resulting in incomplete oxidation (58–84%). Mean squared displacement indicates earlier atomic diffusion onset for thin-shell particles (~7 ps) compared to thick-shell ones (~15 ps). Significantly, interfacial charge redistribution provides the electronic driving mechanism: thin shells facilitate charge homogenization and electron loss, lowering diffusion barriers, whereas thick shells sustain distinct charge separation, impeding atomic migration. These findings provide a theoretical basis for the atomic-scale stress–charge–diffusion coupling mechanism, offering crucial insights for the safety assessment and structural design of oxidation-resistant ANPs. Full article

For high-speed electric multiple units, derailment may lead to severe vehicle instability and safety hazards, making it essential to understand post-derailment dynamic behavior and protective mechanisms. To elucidate the dynamic evolution characteristics and anti-derailment mechanisms of high-speed electric multiple units under derailment conditions, a multibody vehicle–track derailment dynamics model was established for both motor and trailer cars, incorporating nonlinear multi-point contact interactions among wheelsets, gearboxes, traction motors, brake discs, rails, fasteners, and slab tracks. Static geometric clearance verification and dynamic simulations were combined to evaluate the anti-derailment performance of different vehicle configurations and the effectiveness of a carbody–bogie anti-yaw stopper. The results show that underframe components are the first structures to interact with the track after derailment and play a critical role in the evolution of vehicle attitude. Compared with the gearbox and traction motor of a motor car, the brake disc of a trailer car provides more effective lateral restraint and energy dissipation due to its lower installation position and more favorable load-transfer path. The anti-yaw stopper significantly suppresses the relative yaw motion between the carbody and bogie, reducing the peak yaw angle by approximately 30–70% and improving post-derailment stability. Furthermore, a time-sequential and complementary protection mechanism is identified between underframe structures and an anti-yaw stopper. These findings provide guidance for the design and evaluation of derailment protection systems for high-speed vehicles. Full article

Steel wire ropes (SWRs) are exceptional load-bearing elements. However, conventional designs often treat them as passive structures, lacking strategies to actively program their ultimate load-bearing capacity and failure behaviors. To address this gap, this study systematically investigates the tunable load capacity and fracture characteristics of SWRs by developing a simplified power-law hardening elastoplastic constitutive model and a finite element framework integrated with a ductile-damage criterion. Following material parameter calibration via single-wire tests and independent experimental validation of the baseline model using 1 × 7 strand tensile tests, comprehensive numerical parametric studies were conducted to evaluate the simulation-based influence of core diameter (d_core), overall rope diameter (D), layer count (F), and strand configuration (S) on mechanical responses. The numerical results reveal that these geometric parameters act as effective tuning knobs that govern internal stress transfer pathways and ultimate load-bearing capacity. Specifically, simulations predict that increasing the $d_{\mathrm{core}}$ to 1.00 mm elevates the peak tensile force by 9.6% while maintaining a 90.07% tensile force efficiency (TFE, defined as the ratio of mean to peak tensile force). Furthermore, implementing a hybrid multi-strand architecture (SWR-S3) achieves an optimized TFE of 99.76%. These structural modifications facilitate internal strain synchronization, which effectively buffers localized stress peaks and dictates the progressive fracture sequence. Ultimately, this study demonstrates the potential of complementing traditional material enhancement strategies with active geometric parametrization. Rather than offering immediate industrial design rules, it provides a conceptual theoretical framework for exploring custom-tailored tensile strength profiles and predictable failure behaviors. However, because these advanced structural configurations are evaluated using idealized quasi-static finite element models, further experimental validation addressing real-world manufacturing constraints, residual stresses, and dynamic loading is required before practical engineering deployment. Full article

Frost heave in seasonally frozen loess regions is fundamentally governed by pore water migration towards the freezing front driven by temperature gradients, forming ice lenses that damage engineered infrastructure. Because both freezing intensity and moisture availability evolve with climate, frost heave susceptibility is itself dynamic, yet existing assessments remain static and ignore future climate trajectories. This paper presents a physics-constrained machine learning framework that couples soil freezing characteristic curve (SFCC) prior knowledge with multi-source open data and CMIP6 climate projections to achieve dynamic frost heave susceptibility mapping for the Loess Plateau. Monotonicity constraints derived from the coupled phase-transition and cryosuction mechanisms described by the SFCC and the segregation potential theory are enforced during gradient-boosted tree training, ensuring that predictions respect the established relationships among freezing intensity, fine-grained content and ice segregation potential. An ordinal decomposition strategy is adopted to guarantee that the monotonicity constraint on each binary sub-model translates into monotonicity of the predicted ordinal susceptibility level. The best performer, physics-constrained XGBoost, reaches an overall accuracy of 88.7% and an AUC of 0.942 on a four-class susceptibility scheme. Independent validation against 156 field records and Sentinel-1 InSAR observations confirms that the model captures genuine frost heave patterns. Under SSP5-8.5, the area classified as high or very-high susceptibility contracts by approximately 38% by the 2080s owing to warming, while under SSP1-2.6 the reduction is only 12%, and transitional zones of moderate risk expand in both scenarios. These findings provide a temporally explicit and physically grounded basis for climate-adaptive infrastructure planning in cold loess regions. Full article

Background/Objectives: 18F-FDG PET/CT is increasingly used in pediatric Burkitt lymphoma (BL) for staging and treatment response assessment, but data on quantitative prognostic markers remain limited. Methods: We retrospectively evaluated 33 children with pathologically confirmed BL who underwent baseline and end-of-treatment (EOT) 18F-FDG PET/CT between 2009 and 2025. SUVmax, metabolic tumor volume (MTV), and total lesion glycolysis (TLG) were measured at baseline and EOT, and the Deauville score (DS) was assigned visually. Progression-free survival (PFS) and overall survival (OS) were estimated with the Kaplan–Meier method; receiver operating characteristic (ROC) analysis identified predictive thresholds. Results: Three-year and 5-year OS was 89.6%, and PFS was 90.1%. The percentage reduction in SUVmax from baseline to EOT (%ΔSUVmax) was the strongest predictor of survival (AUC = 0.94; optimal cutoff ≥ 73.6% reduction; sensitivity 100%, specificity 93%). Patients with an unfavorable EOT DS (4–5) had significantly lower OS than those with a favorable DS (1–3) (60.0% vs. 96.4%; log-rank p = 0.009). Baseline SUVmax, MTV, and TLG did not differ significantly by stage or splenic involvement. Given the small number of survival events (n = 3), the corresponding hazard ratios and confidence intervals should be regarded as exploratory and hypothesis-generating rather than definitive effect sizes. Conclusions: Dynamic metabolic response such as %ΔSUVmax and the EOT Deauville score are promising prognostic markers in pediatric BL. Given the small number of events, these findings warrant validation in larger, prospective cohorts. Full article

Laser powder bed fusion (L-PBF)-fabricated Inconel 718 lattice structures offer considerable potential for lightweight load-bearing applications, but their mechanical integrity depends on architecture, geometric scale, manufacturing defects, and post-processing condition. This study examined the combined effects of Diamond, Gyroid, and Octet architectures, nominal feature sizes of 1.0, 1.25, and 1.5 mm, and four heat-treatment conditions on tensile behavior and fracture characteristics. The specimens were evaluated using explicit-dynamics finite element analysis, room-temperature quasi-static tensile testing, and SEM fractography. A feature-size-dependent reversal in architecture ranking was observed: Gyroid exhibited the highest mean apparent tensile strength among the 1.0 mm lattices across the investigated heat-treatment conditions, whereas Diamond exhibited the highest mean apparent tensile strength among the 1.5 mm lattices across the same conditions. HT-C shifted the tensile response toward higher apparent strength and lower apparent deformation capacity, whereas selected HT-B-treated specimens exhibited more ductile fracture morphologies. These findings show that architecture, nominal feature size, and post-processing condition must be considered jointly when designing L-PBF Inconel 718 lattice components within the investigated design range. Full article

Introduction: Refeeding syndrome (RS) is a life-threatening metabolic complication of nutritional support. Prolonged fasting, frequently observed in malnourished patients referred for percutaneous endoscopic gastrostomy (PEG), may induce histological and ultrastructural changes in the intestinal mucosa, potentially influencing metabolic adaptation during nutritional reintroduction. Mixed-Meal Tolerance Tests (MMTT) combined with targeted metabolic profiling allow dynamic assessment of serum glucose, gastrointestinal hormones and metabolites, which may help to elucidate the metabolic adaptations associated with fasting and refeeding. Objective: The present study aims to perform MMTT in PEG patients to characterize enteroendocrine hormone responses and metabolomic profiles following a prolonged period of reduced nutritional intake and subsequent enteral refeeding. Methods: This prospective, single-center study includes adults referred for PEG after at least one month of oral intake below 50% of energy needs. The MMTT will be performed at PEG placement and after 3–6 months of enteral nutrition. Serial blood samples will be collected from baseline up to 120 minutes post-meal to measure serum glucose, insulin, C-peptide, electrolytes and gastrointestinal hormones (GLP-1, GIP, ghrelin and PYY), and for targeted metabolomic profiling by spectroscopy. Clinical and nutritional data will be prospectively recorded. Exploratory analyses will assess metabolic and hormonal changes over time and their potential associations with relevant clinical characteristics. The study was approved by the institutional ethics committee, and patient informed consent will be obtained. Conclusion: This study integrates MMTT and targeted metabolomic profiling to characterize hormonal and metabolic responses during fasting and refeeding in PEG patients considered at increased risk of RS due to prolonged markedly reduced oral intake. The findings may improve understanding of metabolic adaptations associated with nutritional reintroduction and may identify candidate hormonal and metabolic signatures to support future studies on RS pathophysiology and risk assessment. Full article

Titanium and its alloys are widely used in cardiovascular devices because of their favorable mechanical properties, corrosion resistance, and biocompatibility. Nevertheless, their surfaces do not fully prevent nonspecific protein adsorption, platelet activation, thrombosis, bacterial colonization, or long-term degradation under physiological conditions. Silicon-nitride-based (SiN_x) coatings represent a promising strategy for addressing these limitations by combining chemical stability, mechanical durability, hemocompatibility, and antibacterial activity. This review critically examines silicon-nitride-based (SiN_x) coatings on titanium for cardiovascular applications, focusing on deposition technologies, interfacial phenomena, surface characteristics, and biological performance. Particular attention is given to physical vapor deposition parameters, coating adhesion, residual stresses, interfacial reactions, corrosion resistance, and mechanical stability. Relationships between surface chemistry, wettability, protein adsorption, platelet response, hemolysis, and cellular behavior are discussed, alongside the effects of static and dynamic testing conditions. SiN_x is also compared with Au, TiN, TiO₂, ZrN, and silicon carbide-based coatings. Despite encouraging in vitro results, clinical translation remains limited by insufficient standardization, scarce long-term and flow-dependent data, and an incomplete understanding of degradation mechanisms. Future studies should integrate interface engineering with microfluidic models, standardized hemocompatibility testing, artificial intelligence-assisted optimization of process–structure–property–biological response relationships, and regulatory considerations to support the safe clinical translation of SiN_x-coated cardiovascular devices. Full article

This study systematically explored immune targets in gastric adenocarcinoma (GAC) suitable for mRNA vaccine development. Based on multi-omics data from public databases, we first screened a set of potential tumor-associated antigen genes. Subsequently, using ten machine learning algorithms, we constructed 101 prognostic models and, through optimization and comparison, selected the Random Survival Forest (RSF) method to establish a clinical prognostic model for GAC consisting of seven genes (TYMP, IFGN, ITGAX, GBP5, GBP4, STAT1, CD84). At both the genetic and protein levels, these genes were closely associated with the antigen presentation process, suggesting the potential functional role of this model in antigen presentation. Further analysis of the immune infiltration characteristics in GAC preliminarily revealed its possible immune evasion mechanisms. Building on this, we designed candidate mRNA vaccine templates for GAC using the mRNAdesigner platform. Additionally, this study investigated the potential roles of the above seven genes in GAC progression and screened small-molecule compounds targeting these genes. Molecular dynamics simulations (MD) were performed to verify the binding stability between these compounds and their corresponding proteins. This study comprehensively simulated the tumor microenvironment (TME) and antigen presentation process in GAC, evaluated the clinical translation potential of the neoantigen prognostic model and its predictive value for immunotherapy, and provided a preliminary design scheme for an mRNA vaccine against GAC. The findings offer new evidence for identifying immune therapy targets in GAC and are expected to advance the development of immunotherapy strategies for GAC. Full article

The widespread use of location-based services (LBSs) has led to the continuous collection of user location and trajectory data, increasing the risk of privacy leakage and creating a persistent tradeoff between privacy protection and data utility. To address this problem in discrete location query scenarios, this paper proposes a single-point location privacy protection method based on Q-R tree retrieval and differential privacy, termed QRDPP. QRDPP combines the adaptive spatial partitioning capability of a Q-tree with the minimum bounding rectangle (MBR)-based indexing capability of an R-tree. It applies an improved geometric privacy budget allocation strategy to leaf nodes and an arithmetic allocation strategy to non-leaf nodes, followed by Laplace perturbation of the corresponding location data and node information. For continuous trajectory query scenarios, this paper proposes a spatiotemporal generalization and differential privacy method, termed STG-DPTP, to address inadequate temporal protection, inappropriate generalization, and trajectory distortion. STG-DPTP performs hierarchical spatiotemporal clustering, separately models temporal and spatial distributions using Gaussian kernel density estimation, dynamically optimizes bandwidth parameters through Bayesian optimization, selects representative candidate subsets using the exponential mechanism, and generates protected trajectories through constrained sampling. Experiments on the GeoLife dataset evaluate the proposed methods in terms of query accuracy, computational efficiency, spatial trajectory similarity, reconstruction error, adversarial uncertainty, and temporal preservation. The results show that QRDPP improves the utility and efficiency of privacy-preserving spatial queries, while STG-DPTP better preserves the spatial distribution, trajectory structure, and temporal characteristics of the original data under the adopted differential privacy framework. Full article