Search Results (311)

Background: Medicinal–edible plants are abundant sources of natural bioactives and functional food precursors. Talinum paniculatum (Jacq.) Gaertn is a commonly utilized folk herb, but there is still a lack of systematic metabolomic research linking its organ-specific phytochemical variations to its medicinal and sensory attributes. Methodology: In this work, untargeted UPLC-MS/MS metabolomics was applied to methanolic extracts of the roots, stems, leaves and fruits of T. paniculatum. Multivariate statistics and KEGG enrichment screened differentially accumulated metabolites (DAMs), while multi-database matching against TCMSP, FlavorDB, and MFood-Flavor Innovation was performed to prioritize candidates with database-assigned pharmacological or flavor-related annotations. For candidates carrying flavor descriptors, relative abundance values were compared across organs to characterize organ-dependent distribution patterns. Results: After quality control filtering and annotation-based deduplication, 4521 putatively annotated metabolite entries spanning 21 chemical classes were retained across the four organs. Amino acid derivatives constituted the largest class, accounting for 31.72% of all annotated entries. Among the presumptively annotated DAMs, five were further proposed as candidate organ-discriminatory metabolites. TCMSP screening matched 348 metabolites with predicted pharmacological relevance; 55 met the OB and DL screening criteria, and of these, 22 showed preferential accumulation in fruits. Subsequent database annotation highlighted candidates carrying both predicted pharmacological relevance and database-assigned flavor attributes, including umami-associated pyroglutamic acid, sweet neoastilbin, and bitter-tasting melilotoside, helenalin, quercetin, cianidanol and glycitein. These dual-functional metabolites exhibited distinct organ-specific distribution patterns. Conclusions: The organ partitioned distribution of metabolites with database-assigned flavor attributes and predicted bioactivity provides testable hypotheses for selective organ utilization, pending targeted quantification, sensory evaluation, and bioactivity validation. Full article

The Surface Water and Ocean Topography (SWOT) mission represents a major advance in satellite hydrology by extending conventional nadir altimetry to two-dimensional wide-swath observations of the inland-water surface elevation, extent, width, and slope. This review combines a bibliometric analysis of 556 publications indexed in the Web of Science Core Collection from January 2019 to April 2026 with a thematic synthesis of representative post-launch studies. In contrast to earlier reviews centered largely on mission preparation or individual application domains, we examine the evolution of SWOT research together with study-level evidence on data products, water-surface-elevation retrieval, river-discharge estimation, hydrological applications, uncertainty, and multi-mission integration. The literature shows a clear transition from pre-launch algorithm development and simulation toward post-launch validation and application. Reported water-surface-elevation performance is strongly context dependent: representative studies include a mean RMSE of 0.29 m across Yangtze River stations, MAE below 0.10 m for Tibetan Plateau lakes against ICESat-2, and MAE of 6.7 cm for 1 km²-averaged elevations in herbaceous wetlands. These values should be interpreted as study-specific evidence rather than as single mission-wide accuracy because the validation design, spatial support, environmental conditions, and processing strategies differ substantially among studies. River-discharge estimation remains more uncertain because the bathymetry, roughness, channel geometry, slope estimation, and temporal sampling introduce additional uncertainty. Major remaining challenges concern observation constraints, processing and product uncertainty, transferability, operational implementation, and uneven validation evidence. Future progress will depend on standardized and reproducible processing, uncertainty-aware validation, cross-regional benchmarking, and the integration of SWOT with complementary satellite observations and hydrological or hydraulic models. Full article

Inverse Synthetic Aperture Radar (ISAR) imagery plays a crucial role in space situational awareness, yet its interpretation remains largely confined to tasks such as classification and segmentation, lacking the ability to generate detailed natural language captions. To address this limitation, this paper proposes ISARCap 1.0, the first dataset specifically designed for ISAR image captioning, covering 38 classes of space targets and comprising 102,965 simulated images and 514,825 image–text pairs. On this basis, this paper proposes ISAR-Mamba, a dual-stream gated state space model for ISAR image captioning. The model introduces a Dual-Stream Asymmetric Encoder (DSAE), which performs complementary patch partitioning and scanning along the range and azimuth dimensions, respectively, to accommodate the physical dimensional differences of ISAR images. Meanwhile, a Sparsity-Aware Gating Mechanism (SAGM) is introduced, which jointly suppresses the interference of empty patches on state updates by leveraging scattering energy priors and a learnable scoring module. Furthermore, a Hierarchical Spatial Summarization (HSS) strategy is proposed to extract structured summary tokens from different depths and spatial regions of the encoder, thereby enhancing visual information perception during text sequence generation. Experimental results on the ISARCap 1.0 dataset indicate that ISAR-Mamba outperforms existing image captioning methods on multiple metrics, suggesting the effectiveness of the proposed method and the usability of the dataset. Full article

The functions of most proteins encoded by the African swine fever virus (ASFV) remain largely unknown, although several have been reported to possess immunomodulatory properties. Among these, we identified that the DP71L protein exerts an inhibitory effect on inflammatory bowel disease. To investigate its protective role in murine colitis, we constructed and expressed a recombinant DP71L protein. Colitis was induced in mice using DSS, and the effects of DP71L treatment were evaluated by assessing histopathological changes, inflammatory cytokine profiles, oxidative stress markers, colonic tissue pathology, and the expression of tight-junction proteins (claudin-1, occludin, and ZO-1). Our results showed that DP71L intervention significantly attenuated body weight loss and organ damage and ameliorated DSS-induced colonic histopathological injury. Moreover, DP71L treatment markedly increased superoxide dismutase (SOD) activity and reduced malondialdehyde (MDA) content in colonic tissues. Mechanistically, DP71L suppressed both DSS-induced NF-κB and JAK-STAT activation and concurrently inhibited DSS-induced epithelial cell apoptosis. These events likely underlie the observed reduction in pro-inflammatory cytokines (IL-1β, IL-6, IFN-γ, and TNF-α) and the restoration of tight-junction protein expression, as DP71L treatment effectively prevented DSS-induced downregulation of claudin-1, occludin, and ZO-1, while also promoting the anti-inflammatory cytokines IL-10 and TGF-β. Collectively, our findings demonstrate that DP71L effectively inhibits the progression of murine colitis through coordinated anti-inflammatory and anti-apoptotic mechanisms. This study suggests that DP71L may function as a potential immunosuppressant, opening new avenues for the application of viral proteins in the treatment of immune-related disorders. Full article

Estuarine wetlands are highly dynamic ecosystems, and the vegetation serves as a critical indicator of ecological health. Accurate mapping of different vegetation types remains challenging due to spectral similarities and the high dimensionality of time-series data. This research introduces a Google Earth Engine (GEE)-based hierarchical framework for mapping eight typical vegetation types in the Liaohe Estuary using Sentinel-2 imagery from 2023 to 2025. To address data redundancy, a novel feature selection algorithm based on Mahalanobis distance and class separability (FSMD-CS) was developed, reducing 225 dimensions to seven optimal variables. Integrated with a hierarchical decision tree calibrated using the SEaTH approach, the framework achieved an overall accuracy of 86.39% (kappa = 0.832), surpassing single-temporal spectral imagery classification and unoptimized MPS feature classification, which achieved OAs of 72.31% and 82.68%, respectively. The majority of selected features originate from the early green-up and late senescence stages, indicating that seasonal phenological metrics offer superior discrimination of vegetation types compared to peak-summer spectral data. Overall, the proposed framework provides an efficient and interpretable solution for fine-scale estuarine vegetation mapping. Full article

High penetration of renewable energy reduces power-system inertia and increases the need for fast-frequency-support resources. This study proposes an integrated virtual synchronous machine (VSM) control framework for clusters of variable-frequency air conditioners (VFACs). The proposed method incorporates synchronous-machine-like inertia and damping into compressor-side power control, aggregates heterogeneous VFACs using fuzzy C-means clustering, and adaptively adjusts virtual inertia according to grid-frequency variations. Virtual-storage flexibility is further incorporated into coordinated frequency regulation. Simulations on the IEEE two-machine, five-node system verify the effectiveness of the proposed framework. Under a representative 3 MW load-increase disturbance, the frequency deviation from the nominal value is reduced from 0.11 Hz to 0.04 Hz. The results indicate that large-scale VFAC clusters can provide fast and coordinated demand-side frequency support while improving system frequency stability. Full article

Extremely hydrophobic fluoromonomers are highly prone to phase separation during emulsion polymerization. Overcoming macroscopic experimental limitations, this study employs all-atom molecular dynamics (AA-MD) simulations to investigate the molecular-level solubilization behavior and thermal adaptability of the novel eco-friendly zwitterionic fluorocarbon surfactant-perfluorohexyl (butyl) sulfonyl carboxy propylamino dimethyl betaine (PFSC) and the traditional hydrocarbon surfactant SDS toward the monomers tetrafluoroethylene (TFE) and perfluoromethyl vinyl ether (PMVE). Simulation results indicate that fluorinated monomers in the SDS system exhibit a more dispersed spatial distribution, with weaker local association in surfactant-enriched regions. In contrast, fluorinated monomers in the PFSC system tend to distribute within regions rich in perfluorinated segments. This spatial characteristic is consistent with thermodynamic analysis results dominated by solubility parameter matching and van der Waals interactions. Under high-temperature conditions, the perfluorohexyl sulfonyl carboxy propylamino dimethyl betaine (C6) system maintains relatively stable local spatial characteristics, with the fluorinated monomers exhibiting low migration behavior. These spatial distribution characteristics and thermal response behaviors suggest that a fluorine-rich environment may help preserve the local distribution of fluorinated monomers under high-temperature conditions. These findings provide molecular-level insights into the structure–property relationships of eco-friendly fluorinated surfactants and offer computational guidance for their rational design. Full article

Flexible pressure sensors featuring high sensitivity, a broad detection range, and excellent stability are pivotal components for high-precision electronic skins and human health monitoring. To circumvent the limitations of existing sensors in maintaining high responsiveness across extensive pressure ranges, herein, a novel flexible capacitive pressure sensor is developed based on a 3D porous ionogel foam composite (IL/EG/PVA@MF) coupled with a planar electrode array. This device leverages the synergistic structural engineering of the 3D hyperelastic melamine foam (MF) skeleton and the pressure-regulated fringe-field distribution and iontronic interfacial polarization of the porous ionogel. Experimental evaluations demonstrate that the sensor achieves a high normalized sensitivity of 62.45 kPa⁻¹ (2–10 kPa) and maintains reliable piecewise linear sensing performance across a broad working range of 0–50 kPa, accompanied by a rapid response time of within 8 ms. Furthermore, the sensor exhibits outstanding performance consistency after 6000 compression-release cycles at 50 kPa, verifying its good mechanical durability. In practical applications, the device can monitor diverse physiological signals with high fidelity, ranging from subtle radial artery pulses to large-scale joint movements and specific coughing patterns, underscoring its broad potential for integrated wearable systems and intelligent healthcare. Full article

Peach fruit develops chilling injury (CI) during cold storage, yet the symptoms are milder at 0 °C than at 5 °C; however, the underlying mechanism remains unclear. This study demonstrated that peach fruit stored at 0 °C maintains higher levels of structural phospholipids and membrane lipid unsaturation, along with lower activities of lipase and lipoxygenase (LOX), thereby alleviating membrane damage. Using lipidomics, we identified phosphatidic acid (PA) as a lipid closely associated with CI characteristics and revealed that it plays a signaling role at the early stage of storage at 0 °C, while its reduced levels at the later stage alleviate membrane damage, thereby participating in the regulation of CI in peach fruit. Moreover, at 0 °C, Phospholipase D (PLD) activity exhibited an initial increase followed by a decrease, which was consistent with the overall changes in PA content. During storage, PLD activity and PA content at 0 °C were significantly lower than those at 5 °C and 15 °C. These findings highlight the critical role of PA in temperature-dependent regulation and provide potential molecular targets for precise management of CI in the postharvest cold chain of peach fruit. Full article

Dam displacement forecasting is important for assessing whether long-term structural responses remain consistent with established operational behavior. In multi-point monitoring systems, however, irregular survey-line layouts and unequal numbers of monitoring points make it difficult to organize long-term records while preserving their engineering meaning. This study develops a field-oriented forecasting framework by reconstructing daily observations into a structured displacement-field object defined by survey-line order, monitoring-point alignment, and three displacement components. A valid-position-aware protocol is introduced to distinguish actual monitoring locations from structural padding, ensuring that model training and evaluation remain restricted to the same physical monitoring definition. Using long-term operational records from the Tianshengqiao First Dam, four representative models, namely SimVP, SimVPv2, PatchTST, and TimesNet, are evaluated under the same chronological split, causal forward-fill-only preprocessing, input window, prediction horizon, and evaluation boundary. All four models achieve strong predictive performance, with R² values above 0.97 in the X direction and above 0.99 in the Y and Z directions. No single trained model or forecasting route exhibits a consistent advantage across all displacement components and evaluation metrics. Under the present single-dam, case-specific setting, the relative ranking varies with displacement direction and forecasting horizon and should not be interpreted as evidence of general direction-specific suitability for any particular architecture. At the route level, the field-based route retains a slight advantage in Y-direction forecasting and overall MAE, whereas the sequence-based route remains competitive for Z-direction displacement and longer-horizon X-direction prediction. The proposed framework provides a practical and physically consistent digital representation for organizing irregular monitoring records, comparing forecasting routes, and supporting deployment-oriented model selection and subsequent model adaptation. Full article

Driven by the increasing demand for renewable energy integration and low-carbon transformation in the chemical industry, the production of green hydrogen from renewable electricity for subsequent ammonia synthesis has emerged as an important route for green ammonia production. However, the process still relies on the high-pressure Haber–Bosch synthesis loop, typically operating at 200–300 bar, where multi-stage compression, recycle gas treatment, and low-temperature condensation separation strongly influence energy consumption and feedstock utilization. In this study, a steady-state Aspen Plus (V15) model integrating an Alkaline Water Electrolysis Unit (AWE), cryogenic air separation for nitrogen production, and an ammonia synthesis loop was established for a liquid ammonia plant with an annual capacity of approximately 600,000 t. Under base-case conditions, the specific energy consumption of liquid ammonia production was 10.28 kWh/kg-NH₃, while the hydrogen and nitrogen elemental utilization rates reached 87.73% and 88.92%, respectively, both higher than those of conventional coal- and natural gas-based ammonia routes. Increasing the compression stages from 2 to 5 reduced the fresh syngas compression work by 6.69%, although the energy-saving benefit became marginal beyond four stages. A purge ratio of 1–2% achieved a reasonable balance between recycle compression work and hydrogen/nitrogen purge loss, while the preferred condensation temperature range for improved NH₃ recovery was −20 °C to −25 °C. Full article

Segmenting key components of space targets using Inverse Synthetic Aperture Radar (ISAR) images is an important interpretation task in space situational awareness. However, the scarcity of pixel-level annotated data, inter-class confusion caused by morphological differences among multiple target classes, and the absence of structural priors for components restrict the performance improvement in existing deep models on this task. Therefore, this paper proposes a Semi-Supervised Structural Prior-Guided Network (SSPNet). First, a Gated Manifold-Constrained Hyper-Connections Vision Transformer (GMHC-ViT) encoder is proposed to broaden the feature representation space via parallel multi-feature streams with adaptive gating, thereby alleviating inter-class confusion and enhancing cross-category generalization. Second, a Prior-Guided Module (PGM) is proposed to extract shape and edge priors of components, and it adaptively enhances the weakly activated channels of encoder features through cross-attention, thereby injecting structural knowledge independent of image quality into the segmentation process. Furthermore, to effectively leverage large amounts of unlabeled data, a strong perturbation strategy tailored to the characteristics of ISAR images is designed for consistency regularization. Experimental results on a simulated ISAR dataset containing 38 classes of space targets demonstrate that SSPNet outperforms existing methods and exhibits strong segmentation capability even under low signal-to-noise ratio (SNR) conditions. Full article

To overcome the challenges in conventional frame structure design, namely the difficulty in balancing lightweight design and performance enhancement, the low efficiency of finite element (FE) simulation, and the tedious process of multivariable preliminary screening, an efficient optimization framework for frame structures that integrates multi-objective size optimization, machine learning-based performance prediction, and feature correlation analysis is proposed. First, for the steel–aluminum hybrid frame (with the main load-bearing components made of 6005A aluminum alloy and the critical load-bearing supports and joints made of Q345 low-alloy high-strength steel), a trade-off solution is obtained through multi-objective size optimization. Verified by FE simulation, this solution reduces the frame mass by 6.37% and increases the torsional stiffness by 10.47% compared with the initial design. At the same time, the modal performance, structural strength, and deformation control capability are all significantly improved, achieving a precise balance between lightweighting and stiffness enhancement. Second, a linear regression prediction model is constructed to achieve high-accuracy predictions. The average prediction error for torsional stiffness is only 2%, and the maximum prediction error for the seventh-order modal frequency is less than 1%. The prediction time for a single sample is less than one second, which is more than 1000 times faster than conventional FE simulation, thus efficiently replacing time-consuming simulation analyses. Finally, feature correlation analysis is adopted as an alternative to traditional sensitivity analysis. The core variables identified by this method are highly consistent with those obtained from Hypermesh sensitivity analysis, enabling rapid multivariable screening without additional simulations and greatly improving the efficiency of the preliminary analysis phase. The proposed optimization framework achieves a favorable combination of optimization effectiveness, prediction accuracy, and design efficiency. It not only provides a feasible engineering solution for the lightweight design of frame structures but also serves as a technical reference for the efficient optimization of similar complex structures, demonstrating significant engineering application value. Full article

In ground-based space surveillance, space target attitude estimation is critical for space situational awareness, yet existing methods based on inverse synthetic aperture radar (ISAR) images suffer from three core limitations: phase information is discarded in amplitude-only processing, convolutional neural networks have a restricted global receptive field, and the physical topology of satellite components is not explicitly modeled. To address these issues, we propose a complex-domain Transformer–graph neural network (CD-TrGNN) that unifies global context modeling and adaptive topological reasoning in an end-to-end framework. Specifically, a complex-domain Transformer module (CD-Transformer) with tailored attention captures long-range dependencies among image patches while preserving both amplitude and phase information; a complex-domain graph convolution module (CD-GC) with learnable adjacency matrices and a dual-path update mechanism explicitly encodes the structural relationships among satellite parts. On a self-built ISAR complex image dataset, CD-TrGNN achieves a three-axis mean absolute error of only 1.70°, substantially outperforming six representative baselines. Ablation experiments confirm the effectiveness of complex-domain processing, global attention, and topological reasoning. At a 5 dB signal-to-noise ratio, the error remains at 2.81°, and the accuracy stays below 2° for two different satellite structures. These results demonstrate that CD-TrGNN can fully exploit the information in ISAR complex images, enabling high-accuracy and highly robust attitude estimation. Full article

Background/Objectives: To evaluate the association between HNF4A rs4812829 and type 2 diabetes (T2D) in a rural Chinese population and to investigate its interaction with blood lipids in the association. Methods: A total of 4496 participants free of diabetes at baseline from a family-based cohort in rural China were included. Demographic, lifestyle, and medical history data were collected via standardized questionnaires. Anthropometric and biochemical measurements were performed using standardized protocols and automated assays on fasting blood samples. Mixed-effects Cox proportional hazards models, accounting for familial clustering, were employed to examine the association between HNF4A rs4812829 and incident T2D risk. Additionally, multiplicative interaction terms were used to assess interactions. Results: After a median follow-up of 10.76 years, 895 incident T2D cases were identified. Under an additive genetic model, each additional G allele of rs4812829 was significantly associated with an increased risk of T2D (HR 1.38, 95% CI 1.19–1.59). A significant multiplicative interaction was observed between rs4812829 and HDL-C (p = 0.004). In genotype-stratified analyses, higher HDL-C levels were strongly associated with lower T2D risk among AA homozygotes (HR 0.04, 95% CI 0.003–0.43) and AG heterozygotes (HR 0.30, 95% CI 0.10–0.84), but not among GG homozygotes (HR 1.32, 95% CI 0.35–4.91). Conclusions: This study finds HNF4A intronic variant rs4812829 is significantly associated with the incident T2D risk in a rural Chinese population, and this association exhibits an interaction with HDL-C levels. These findings support further investigation of HNF4A-related lipid pathways and require replication in independent Chinese and multi-ancestry cohorts before genetic and lipid profiles can be considered for T2D risk stratification. Full article