Search Results (6,461)

SA-ConvLSTM is a recently proposed spatiotemporal model for total electron content (TEC) prediction, which effectively catches long-term temporal evolution and global-scale spatial correlations in TEC. However, its reliance on standard convolution limits spatial feature extraction to fixed regular regions, reducing the flexibility for irregular TEC variations. To address this limitation, we enhance SA-ConvLSTM by incorporating deformable convolution, proposing SA-DConvLSTM. This achieves adaptive spatial feature extraction through learnable offsets in convolutional kernels. Building on this improvement, we design ED-SA-DConvLSTM, a TEC spatiotemporal prediction model based on an encoder–decoder architecture with SA-DConvLSTM as its fundamental block. Firstly, the effectiveness of the model improvement was verified through an ablation experiment. Subsequently, a comprehensive quantitative comparison was conducted between ED-SA-DConvLSTM and baseline models (C1PG, ConvLSTM, and ConvGRU) in the region of 12.5° S–87.5° N and 25° E–180° E. The experimental results showed that the ED-SA-DConvLSTM exhibited superior performance compared to C1PG, ConvGRU, and ConvLSTM, with prediction accuracy improvements of 10.27%, 7.65%, and 7.16% during high solar activity and 11.46%, 4.75%, and 4.06% during low solar activity, respectively. To further evaluate model performance under extreme conditions, we tested the ED-SA-DConvLSTM during four geomagnetic storms. The results showed that the proportion of its superiority over the baseline models exceeded 58%. Full article

Astragalus sinicus, a key leguminous green manure widely cultivated in Southern China’s rice-based cropping systems, plays a pivotal role in sustainable agriculture by enhancing soil organic matter sequestration, improving rice yield, and elevating grain quality. The symbiotic nitrogen-fixing association between A. sinicus and its matching rhizobia is fundamental to its agronomic value; however, suboptimal inoculant efficiency and field application methodologies constrain its full potential. To address these limitations, we conducted a multi-phase study involving (1) rhizobial strain screening under controlled greenhouse conditions, (2) an optimized lyophilization protocol evaluating cryoprotectant (trehalose, skimmed milk powder and others), and (3) seed pelleting trails with rhizobial viability and nodulation assessments over different storage periods. Our results demonstrate that Mesorhizobium huakuii CCBAU 33470 exhibits a superior nitrogen-fixing efficacy, significantly enhancing key traits in A. sinicus, including leaf chlorophyll content, tiller number, and aboveground biomass. Lyophilized inoculants prepared with cryoprotectants (20% trehalose or 20% skimmed milk powder) maintained >90% bacterial viability for 60 days and markedly improved nodulation capacity relative to unprotected formulations. The optimized seed pellets sustained high rhizobial loads (5.5 × 10³ cells/seed) with an undiminished viability after 15 days of storage and nodulation ability after 40 days of storage. This integrated approach of rhizobial selection, inoculant formulation, and seed coating overcomes cultivation bottlenecks, boosting symbiotic nitrogen fixation for A. sinicus cultivation. Full article

Subcooling-based ice slurry production faces challenges in terms of energy efficiency and operational stability, which limit its applications for large-scale cold energy storage. A thermodynamic model is established to investigate the effects of key control parameters, including evaporation temperature, condensation temperature, subcooling degree, water flow rate, type of refrigerant, and adiabatic compression efficiency. The results show that using the refrigerant R161 achieves the highest energy efficiency, indicating that R161 is the optimal refrigerant in this research. When the evaporation and condensation temperatures are −10 °C and 30 °C, respectively, the system achieves the maximum comprehensive performance coefficient of 2.43. Moreover, under a flow velocity of 0.8 m/s and a temperature of 0.5 °C, the system achieves a peak ice production rate of 45.28 kg/h. A high water temperature and high flow velocity would significantly degrade the system’s ice production capacity. This research provides useful guidance for the design, optimization, and application of ice slurry-based cold energy storage systems. Full article

Numerous studies have demonstrated the positive effects of formulated feeds on gonadal and hepatopancreatic development of Eriocheir sinensis. However, there are limited studies on the effects of formulated feeds on the immune homeostasis and metabolism of muscle tissue in E. sinensis during the fattening period. Therefore, this study used metabolomic and lipidomic to systematically analyze the effects of formulated diets on muscle metabolism in female E. sinensis. The results indicate that the formulated feeds improved immune performance by inhibiting inflammatory responses, apoptosis and autophagy. In addition, the feed promoted amino acid metabolism and protein synthesis while decreasing muscle fatty acid metabolism. Metabolomic analysis reveal that pyrimidine metabolism is involved in the regulation of muscle physiological health in fattening female crabs. Lipidomic analysis revealed that the formulated feeds play a role in muscle immune homeostasis, amino acid and fatty acid metabolism by regulating the level of ceramide (Cer (d18:1/22:0)) in sphingolipid metabolism. Through subnetwork analysis, the functional interactions of sphingolipid metabolism with the pathways of sphingolipid signaling, apoptosis regulation, inflammatory response and lipid dynamic homeostasis were identified, which further defined the important role of sphingolipid metabolism in the regulation of muscle physiological health and metabolic homeostasis was further identified. In summary, the formulated feeds effectively promote immune homeostasis and metabolism in the muscle of female E. sinensis during the fattening period. These findings provide a solid theoretical foundation for feed formulation optimization and application in fattening practices. Full article

As a typical porous medium, unsaturated loess demonstrates critical hydro-mechanical coupling properties that fundamentally influence geohazard mitigation, groundwater resource evaluation, and foundation stability in geotechnical engineering. This investigation develops a novel theoretical framework to overcome the limitations of existing models in converting electrical resistivity tomography (ERT) profiles into water content distributions for unsaturated loess through quantitative inversion modeling. Systematic laboratory investigations on remolded loess specimens with controlled density and water content conditions revealed distinct resistivity–water interaction mechanisms. A characteristic two-stage decay pattern was identified: resistivity exhibited an exponential decrease from 420 Ω·m (water saturation (S_w = 10%)) to 90 Ω·m (S_w = 40%), followed by asymptotic stabilization at S_w ≥ 40%. The derived quantitative correlation provides a robust mathematical basis for water content profile inversion. Field validation through integrated ERT and borehole data demonstrated exceptional predictive accuracy in shallow strata (<20 m depth), achieving mean absolute errors of <5%. However, inversion reliability decreased with depth (>20 m), primarily attributed to density-dependent charge transport mechanisms. This underscores the necessity of incorporating coupled thermo-hydro-mechanical processes for deep-layer characterization. This study provides a robust framework for engineering applications of ERT in loess terrains, offering significant advancements in geotechnical monitoring and geohazard prevention. 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

It has long been accepted that breathing gases that are physiologically inert include helium (He), neon (Ne), nitrogen (N₂), argon (Ar), krypton (Kr), xenon (Xe), and hydrogen (H₂). The term “inert gas” has been used to describe them due to their unusually high chemical stability. However, as investigations have advanced, many have shown that inert gas can have specific biological impacts when exposed to high pressure or atmospheric pressure. Additionally, different inert gases have different effects on intracellular signal transduction, ion channels, and cell membrane receptors, which are linked to their anesthetic and cell protection effects in normal or pathological processes. Through a selective analysis of the representative literature, this study offers a concise overview of the state of research on the biological impacts of inert gas and their molecular mechanisms. Full article

Bamboo shoot boiled liquid (BSBL), a processing byproduct containing soluble proteins, peptides, amino acids, carbohydrates, and phenolics, is typically discarded, causing resource waste and environmental issues. This study analyzed metabolic changes in BSBL during Pediococcus pentosaceus B49 fermentation. The result of partial least squares discriminant analysis (PLS-DA) revealed significant metabolite profile differences across fermentation times (0 h, 24 h, 48 h, 72 h, 96 h). The most substantial alterations occurred within the first 24 h, followed by stabilization. Compared to unfermented BSBL, fermented samples exhibited significantly elevated signal intensities for 5,7-dimethoxyflavone, cinnamic acid, 3,4-dihydro-2H-1-benzopyran-2-one, 6,8-dimethyl-4-hydroxycoumarin, and 2-hydroxycinnamic acid (p < 0.05), showing upward trends over time. Conversely, (+)-gallocatechin intensity decreased gradually. Bitter peptides, such as alanylisoleucine, isoleucylisoleucine, leucylvaline, and phenylalanylisoleucine, in BSBL exhibited a significant reduction following fermentation with P. pentosaceus B49 (p < 0.05). KEGG enrichment indicated tyrosine metabolism (ko00350) and arginine/proline metabolism (ko00330) as the most impacted pathways. These findings elucidate metabolic regulation in BSBL fermentation, supporting development of functional fermented bamboo products. Full article

Early blight, caused by the pathogen Alternaria solani, is a major fungal disease impacting potato production globally, with reported yield losses of up to 40% in susceptible varieties. As one of the most common diseases affecting potatoes, its incidence has been steadily increasing year after year. This study aimed to elucidate the molecular mechanisms underlying resistance to early blight by comparing gene expression profiles in resistant (B1) and susceptible (D30) potato seedlings. Transcriptome sequencing was conducted at three time points post-infection (3, 7, and 10 dpi) to identify differentially expressed genes (DEGs). Weighted Gene Co-expression Network Analysis (WGCNA) and pathway enrichment analyses were performed to explore resistance-associated pathways and hub genes. Over 11,537 DEGs were identified, with the highest number observed at 10 dpi. Genes such as LOC102603761 and LOC102573998 were significantly differentially expressed across multiple comparisons. In the resistant B1 variety, upregulated genes were enriched in plant–pathogen interaction, MAPK signaling, hormonal signaling, and secondary metabolite biosynthesis pathways, particularly flavonoid biosynthesis, which likely contributes to biochemical defense against A. solani. WGCNA identified 24 distinct modules, with hub transcription factors (e.g., WRKY33, MYB, and NAC) as key regulators of resistance. These findings highlight critical molecular pathways and candidate genes involved in early blight resistance, providing a foundation for further functional studies and breeding strategies to enhance potato resilience. Full article

With the rapid increase in traffic volume and the number of heavy-duty vehicles, the load on asphalt pavements has increased significantly. Especially on sections with long longitudinal slopes, the internal stress conditions of asphalt pavement have become even more complex. This study aims to investigate the thermal–mechanical coupling behavior of asphalt pavement structures on long longitudinal slopes under the combined influence of temperature fields and moving loads. A pavement temperature field model was developed based on the climatic conditions of Nanning (AAT: 21.8 °C; Tmax: 37 °C; Tmin: 3 °C; AAP: 1453.4 mm). In addition, a three-dimensional finite element model of asphalt pavement structures on long longitudinal slopes was established using finite element software. Variations in pavement mechanical responses were compared under different vehicle axle loads (100–200 kN), slope gradients (0–5%), braking coefficients (0–0.7), and asphalt mixture layer thicknesses (2–8 cm). The results indicate that the pavement structure exhibits a strong capacity for pressure attenuation, with the middle and lower surface layers showing more pronounced stress reduction—up to 40%—significantly greater than the 6.5% observed in the upper surface layer. As the axle load increases from 100 kN to 200 kN, the internal mechanical responses of the pavement show a linear relationship with load magnitude, with an average increase of approximately 29%. In addition, the internal shearing stress of the pavement is more sensitive to changes in slope and braking coefficient; when the slope increases from 0% to 5% and the braking coefficient increases from 0 to 0.7, the shear stress at the bottom of the upper surface layer increases by 12% and 268%, respectively. This study provides guidance for the design of asphalt pavements on long longitudinal slopes. In future designs, special attention should be given to enhancing the shear strength of the surface layer and improving the interlayer bonding performance. In particular, under conditions of steep slopes and frequent heavy vehicle traffic, the thickness and modulus of the upper surface asphalt mixture may be appropriately increased. Full article

Mixed-flow assembly lines are widely employed in industrial manufacturing to handle diverse production tasks. For mixed flow assembly lines that involve mold changes and greater processing difficulties, there are currently two approaches: batch production and production according to order sequence. The first approach struggles to meet the processing constraints of workpieces with higher production difficulty, while the second approach requires the development of suitable scheduling schemes to balance mold changes and continuous processing. Therefore, under the second approach, developing an excellent scheduling scheme is a challenging problem. This study addresses the mixed-flow assembly shop scheduling problem, considering continuous processing and mold-changing constraints, by developing a multi-objective optimization model to minimize additional production time and customer waiting time. As this NP-hard problem poses significant challenges in solution space exploration, the conventional NSGA-II algorithm suffers from limited local search capability. To address this, we propose an enhanced NSGA-II algorithm (RLVNS-NSGA-II) integrating deep reinforcement learning. Our approach combines multiple neighborhood search operators with deep reinforcement learning, which dynamically utilizes population diversity and objective function data to guide and strengthen local search. Simulation experiments confirm that the proposed algorithm surpasses existing methods in local search performance. Compared to VNS-NSGA-II and SVNS-NSGA-II, the RLVNS-NSGA-II algorithm achieved hypervolume improvements ranging from 19.72% to 42.88% and 12.63% to 31.19%, respectively. Full article

The photon spin Hall effect (PSHE) arises from the spin–orbit interaction of light. Metasurfaces enable precise control over the PSHE through their influence. Using electromagnetic simulations as its foundation, this work engineers a metal–insulator–metal (MIM) metasurface for generating vortex beams in the near-infrared band, targeting enhanced modulation of the PSHE. Electromagnetic simulations embed vanadium dioxide (VO₂)—a thermally responsive phase-change material—within the MIM metasurface architecture. Numerical evidence confirms that harnessing VO₂’s insulator–metal-transition-mediated optical switching dynamically tailors spin-dependent splitting in the illuminated MIM-VO₂ hybrid, thereby achieving a significant amplification of the PSHE displacement. Electromagnetic simulations determine the reflection coefficients for both VO₂ phase states in the MIM-VO₂ structure. Computed spin displacements under vortex beam incidence reveal that VO₂’s phase transition couples to the MIM’s top metal and dielectric layers, modifying reflection coefficients and producing phase-dependent PSHE displacements. The simulation results show that the displacement change of the PSHE before and after the phase transition of VO₂ reaches 954.7 µm, achieving a significant improvement compared with the traditional layered structure. The dynamic modulation mechanism of the PSHE based on the thermal–optical effect has been successfully verified. Full article

Drought stress severely limits the productivity of sweet potato (Ipomoea batatas L.), yet the stage-specific molecular mechanisms of its adaptation remain poorly understood. Therefore, we integrated transcriptomics and extensive targeted metabolomics analysis to investigate the drought responses of the sweet potato cultivar ‘Luoyu 11’ during the branching and tuber formation stage (DS1) and the storage root expansion stage (DS2) under controlled drought conditions (45 ± 5% field capacity). Transcriptome analysis identified 8292 and 13,509 differentially expressed genes in DS1 and DS2, respectively, compared with the well-watered control (75 ± 5% field capacity). KEGG enrichment analysis revealed the activation of plant hormone signaling, carbon metabolism, and flavonoid biosynthesis pathways, and more pronounced transcriptional changes were observed during the DS2 stage. Metabolomic analysis identified 415 differentially accumulated metabolites across the two growth periods, with flavonoids being the most abundant (accounting for 30.3% in DS1 and 23.7% in DS2), followed by amino acids and organic acids, which highlighted their roles in osmotic regulation and oxidative stress alleviation. Integrated omics analysis revealed stage-specific regulation of flavonoid biosynthesis under drought stress. Genes such as CYP75B1 and IF7MAT were consistently downregulated, whereas flavonol synthase and glycosyltransferases exhibited differential expression patterns, which correlated with the selective accumulation of trifolin and luteoloside. Our findings provide novel insights into the molecular basis of drought tolerance in sweet potato and offer actionable targets for breeding and precision water management in drought-prone regions. Full article

The unique characteristics of desert vegetation, such as different leaf morphology, discrete canopy structures, sparse and uneven distribution, etc., pose significant challenges for remote sensing-based estimation of fractional vegetation cover (FVC). The Unmanned Aerial Vehicle (UAV) system can accurately distinguish vegetation patches, extract weak vegetation signals, and navigate through complex terrain, making it suitable for applications in small-scale FVC extraction. In this study, we selected the floodplain fan with Caragana korshinskii Kom as the constructive species in Hatengtaohai National Nature Reserve, Bayannur, Inner Mongolia, China, as our study area. We investigated the remote sensing extraction method of desert sparse vegetation cover by placing samples across three gradients: the top, middle, and edge of the fan. We then acquired UAV multispectral images; evaluated the applicability of various vegetation indices (VIs) using methods such as supervised classification, linear regression models, and machine learning; and explored the feasibility and stability of multiple machine learning models in this region. Our results indicate the following: (1) We discovered that the multispectral vegetation index is superior to the visible vegetation index and more suitable for FVC extraction in vegetation-sparse desert regions. (2) By comparing five machine learning regression models, it was found that the XGBoost and KNN models exhibited relatively lower estimation performance in the study area. The spatial distribution of plots appeared to influence the stability of the SVM model when estimating fractional vegetation cover (FVC). In contrast, the RF and LASSO models demonstrated robust stability across both training and testing datasets. Notably, the RF model achieved the best inversion performance (R² = 0.876, RMSE = 0.020, MAE = 0.016), indicating that RF is one of the most suitable models for retrieving FVC in naturally sparse desert vegetation. This study provides a valuable contribution to the limited existing research on remote sensing-based estimation of FVC and characterization of spatial heterogeneity in small-scale desert sparse vegetation ecosystems dominated by a single species. Full article