Search Results (4,133)

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

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

The complex system of high-entropy materials makes it challenging to reveal the specific function of each site for oxygen evolution reaction (OER). Here, with nickel foam (NF) as the substrate, FeCoNiCrMo/NF is designed to be prepared by metal–organic frameworks (MOF) as a precursor under an argon atmosphere. XRD analysis confirms that it retains a partial MOF crystal structure (characteristic peak at 2θ = 11.8°) with amorphous carbon (peaks at 22° and 48°). SEM-EDS mapping and XPS demonstrate uniform distribution of Fe, Co, Ni, Cr, and Mo with a molar ratio of 27:24:30:11:9. Electrochemical test results show that FeCoNiCrMo/NF has excellent OER characteristics compared with other reference prepared samples. FeCoNiCrMo/NF has an overpotential of 285 mV at 100 mA cm⁻² and performs continuously for 100 h without significant decline. The OER mechanism of FeCoNiCrMo/NF further reveal that Co and Ni are true active sites, and the dissolution of Cr and Mo promote the conversion of active sites into MOOH following the lattice oxygen mechanism (LOM). The precipitation–dissolution equilibrium of Fe also plays an important role in the OER process. The study of different reaction sites in complex systems points the way to designing efficient and robust catalysts. 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

The compound effects of extreme climate change and intensive urban development have led to more frequent urban inundation, highlighting the urgent need for the fine-scale evaluation of stormwater drainage system performance in high-density urban built-up areas. A typical basin, located in Shenzhen, was selected, and a pipe–river coupled SWMM was developed and calibrated via a genetic algorithm to simulate the storm drainage system. Design storm scenario analyses revealed that regional inundation occurred in the central area of the basin and the enclosed culvert sections of the midstream river, even under a 0.5-year recurrence period, while the downstream open river channels maintained a substantial drainage capacity under a 200-year rainfall event. To systematically identify bottleneck zones, two novel metrics, namely, the node cumulative inundation volume and the conduit cumulative inundation length, were proposed to quantify the local inundation severity and spatial interactions across the drainage network. Two critical bottleneck zones were selected, and strategic improvement via the cross-sectional expansion of pipes and river culverts significantly enhanced the drainage efficiency. This study provides a practical case study and transferable technical framework for integrating hydraulic modeling, spatial analytics, and targeted infrastructure upgrades to enhance the resilience of drainage systems in high-density urban environments, offering an actionable framework for sustainable urban stormwater drainage system management. 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

Accurate prediction of key ocean elements (e.g., chlorophyll-a concentration, sea surface temperature, etc.) is imperative for maintaining marine ecological balance, responding to marine disaster pollution, and promoting the sustainable use of marine resources. Existing spatio-temporal prediction models primarily rely on either physical or data-driven approaches. Physical models are constrained by modeling complexity and parameterization errors, while data-driven models lack interpretability and depend on high-quality data. To address these challenges, this study proposes OKG-ConvGRU, a domain knowledge-guided remote sensing prediction framework for ocean elements. This framework integrates knowledge graphs with the ConvGRU network, leveraging prior knowledge from marine science to enhance the prediction performance of ocean elements in remotely sensed images. Firstly, we construct a spatio-temporal knowledge graph for ocean elements (OKG), followed by semantic embedding representation for its spatial and temporal dimensions. Subsequently, a cross-attention-based feature fusion module (CAFM) is designed to efficiently integrate spatio-temporal multimodal features. Finally, these fused features are incorporated into an enhanced ConvGRU network. For multi-step prediction, we adopt a Seq2Seq architecture combined with a multi-step rolling strategy. Prediction experiments for chlorophyll-a concentration in the eastern seas of China validate the effectiveness of the proposed framework. The results show that, compared to baseline models, OKG-ConvGRU exhibits significant advantages in prediction accuracy, long-term stability, data utilization efficiency, and robustness. This study provides a scientific foundation and technical support for the precise monitoring and sustainable development of marine ecological environments. Full article

Improving the green innovation quality (GIQ) of center cities is crucial to achieve sustainable urban agglomeration development. Utilizing data on green patent citations and economic indicators across cities in the Yangtze River Delta urban agglomeration (YRD) from 2003 to 2022, this research examines the influence of center cities’ GIQ on the economic performance of peripheral municipalities. The results show the following: (1) Center cities’ GIQ exerts a significant suppressive effect on peripheral cities’ economic growth overall. Heterogeneity analysis uncovers a distance-dependent duality. GIQ stimulates growth in proximate cities (within 300 km) but suppresses it beyond this threshold. This spatial siphoning effect is notably amplified in national-level center cities. (2) Mechanisms suggest that GIQ accelerates the outflow of skilled labor in peripheral cities through factor agglomeration and industry transfer mechanisms. Concurrently, it impedes the gradient diffusion of urban services, collectively hindering peripheral development. (3) Increased government green attention (GGA) and industry–university–research cooperation (IURC) in centers can mitigate these negative impacts. This paper contributes to the theoretical discourse on center cities’ spatial externalities within agglomerations and offers empirical support and policy insights for the exertion of spillover effects of high-quality green innovation from center cities and the sustainable development of urban agglomeration. Full article

This investigation examines the influence of P. dabryanus density on the growth performance of P. nigromaculatus and the structural and functional dynamics of paddy soil microbial communities within a rice–frog–loach integrated aquaculture system. Field experiments were conducted with five density gradients of P. dabryanus (0.5, 1.0, 1.5, 2.0, and 2.5 × 10⁴ individuals/667 m²), designated as RFLS0.5, RFLS1.0, RFLS1.5, RFLS2.0, and RFLS2.5, respectively. Control treatments included rice monoculture (RM) and rice–frog co-culture (RFS). These findings demonstrated that as the density of loach increased, the weight gain ratio of P. nigromaculatus showed a unimodal pattern, reaching its peak in RFLS1. Metagenomic analysis on paddy soil revealed that the RFLS1 facilitated the enrichment of nitrogen-fixing bacteria (Proteobacteria), while concurrently suppressing proliferation of the potential pathogen Pseudomonas aeruginosa and microbial markers in metal-contaminated environments of Usitatibacter rugosus. Further, functional profiling indicated that RFLS1 group reached a peak activity in amino acid metabolism (14.52 ± 0.09%) and carbohydrate metabolism (14.44 ± 0.06%) and showed a higher proportion of glycosyltransferase (GT) abundance (41.93 ± 0.02%) than other groups. In summary, the optimal stocking density of P. dabryanus in rice–frog–loach integrated systems was determined to be 1.0 × 10⁴ individuals/667 m². This density not only promotes the growth of P. nigromaculatus but also improves the structure of paddy soil microbial communities. Full article

Structural collapses caused by accidents or disasters could create unexpected radiation shielding, resulting in sharp gradients within the radiation field. Traditional radiation mapping methods often fail to accurately capture these complex variations, making the rapid and precise localization of radiation sources a significant challenge in emergency response scenarios. To address this issue, based on standard Gaussian process regression (GPR) models that primarily utilize a single Gaussian kernel to reflect the inverse-square law in free space, a novel multi-kernel Gaussian process regression (MK-GPR) model is proposed for high-fidelity radiation mapping in environments with physical obstructions. MK-GPR integrates two additional kernel functions with adaptive weighting: one models the attenuation characteristics of intervening materials, and the other captures the energy-dependent penetration behavior of radiation. To validate the model, gamma-ray distributions in complex, shielded environments were simulated using GEometry ANd Tracking 4 (Geant4). Compared with conventional methods, including linear interpolation, nearest-neighbor interpolation, and standard GPR, MK-GPR demonstrated substantial improvements in key evaluation metrics, such as MSE, RMSE, and MAE. Notably, the coefficient of determination (R²) increased to 0.937. For practical deployment, the optimized MK-GPR model was deployed to an RK-3588 edge computing platform and integrated into a mobile robot equipped with a NaI(Tl) detector. Field experiments confirmed the system’s ability to accurately map radiation fields and localize gamma sources. When combined with SLAM, the system achieved localization errors of 10 cm for single sources and 15 cm for dual sources. These results highlight the potential of the proposed approach as an effective and deployable solution for radiation source investigation in post-disaster environments. Full article

Biochar serves as an effective adsorbent for the heavy metal cadmium, with its performance significantly influenced by its physicochemical properties and various environmental features. Traditional machine learning models, though adept at managing complex multi-feature relationships, rely heavily on expertise in feature engineering and hyperparameter optimization. To address these issues, this study employs an automated machine learning (AutoML) approach, automating feature selection and model optimization, coupled with an intuitive online graphical user interface, enhancing accessibility and generalizability. Comparative analysis of four AutoML frameworks (TPOT, FLAML, AutoGluon, H₂O AutoML) demonstrated that H₂O AutoML achieved the highest prediction accuracy (R² = 0.918). Key features influencing adsorption performance were identified as initial cadmium concentration (23%), stirring rate (14.7%), and the biochar H/C ratio (9.7%). Additionally, the maximum adsorption capacity of the biochar was determined to be 105 mg/g. Optimal production conditions for biochar were determined to be a pyrolysis temperature of 570–800 °C, a residence time of ≥2 h, and a heating rate of 3–10 °C/min to achieve an H/C ratio of <0.2. An online graphical user interface was developed to facilitate user interaction with the model. This study not only provides practical guidelines for optimizing biochar but also introduces a novel approach to modeling using AutoML. Full article

Medicinal plants and animal-derived proteins represent valuable natural sources of bioactive components with pharmaceutical potential. Whilst some medicinal plants and animal-derived proteins also offer rich sources of anticoagulant bioactive peptides, their development faces multiple challenges: anticoagulant evaluation relies on single-parameter assays with limited reliability, native proteins demonstrate suboptimal activity without enzymatic treatment, and few researchers investigate bioavailable peptides. Our study establishes an innovative framework using the leech as a case study to overcome these barriers. A novel anticoagulant evaluation model was first established with the Critic-G1 weighting method. And we optimized the enzymatically hydrolyzed extracts with high activity using Box–Behnken response surface methodology. Subsequently, the everted gut sac model was implemented to simulate intestinal absorption and screen for absorbable peptide fractions. Furthermore, peptidomics was employed to identify the bioactive peptides. Lastly, we identified the bioactivity using anticoagulation assays. Results indicated that the optimal hydrolysis conditions were achieved with trypsin at 50.48 °C, an enzyme-to-substrate ratio of 6.78%, 7.51 h, and pH of 8.06. The peptide DLRWM was identified through integrated peptidomics and molecular docking approaches, with subsequent activity validation demonstrating its potent anticoagulant effects. This study has successfully identified a novel anticoagulant peptide (DLRWM) with confirmed intestinal absorption properties and provides a template for unlocking the pharmaceutical potential of medicinal animal proteins. Full article

Robots with multiple degrees of freedom (DOFs), such as underwater vehicle–manipulator systems (UVMSs), are expected to optimize system performance by exploiting redundancy with various basic tasks while still fulfilling the primary objective. Multiple tasks for robots, which are expected to be carried out simultaneously with prescribed priorities, can be referred to as sets of tasks (SOTs). In this work, a hybrid vision/force control method with continuous task transitions is proposed for a UVMS to simultaneously track the reference vision and force trajectory during manipulation. Several tasks with expected objectives and specific priorities are established and combined as SOTs in hybrid vision/force tracking. At different stages, various SOTs are carried out with different emphases. A hierarchical optimization-based whole-body control framework is constructed to obtain the solution in a strictly hierarchical fashion. A continuous transition method is employed to mitigate oscillations during the task switching phase. Finally, comparative simulation experiments are conducted and the results verify the improved convergence of the proposed tracking controller for UVMSs. Full article

Developmental exposure to polybrominated diphenyl ethers (PBDEs), which are commonly used as flame retardants, results in irreversible cognitive impairments. Postnatal hippocampal neurogenesis, which occurs in the subgranular zone (SGZ) of the dentate gyrus, is critical for neuronal circuits and plasticity. Wnt7a-Frizzled5 (FZD5) is essential for both neurogenesis and synapse formation; moreover, Wnt signaling participates in PBDE neurotoxicity and also contributes to the neuroprotective effects of melatonin. Therefore, we investigated the impacts of perinatal decabromodiphenyl ether (BDE-209) exposure on hippocampal neurogenesis and synaptogenesis in juvenile rats through BrdU injection and Golgi staining, as well as the alleviation of melatonin pretreatment. Additionally, we identified the structural basis of Wnt7a and two compounds via molecular docking. The hippocampal neural progenitor pool (Sox2+BrdU+ and Sox2+GFAP+cells), immature neurons (DCX+) differentiated from neuroblasts, and the survival of mature neurons (NeuN+) in the dentate gyrus were inhibited. Moreover, in BDE-209-exposed offspring rats, it was observed that dendritic branching and spine density were reduced, alongside the long-lasting suppression of the Wnt7a-FZD5/β-catenin pathway and targeted genes (Prox1, Neurod1, Neurogin2, Dlg4, and Netrin1) expression. Melatonin alleviated BDE-209-disrupted memory, along with hippocampal neurogenesis and dendritogenesis, for which the restoration of Wnt7a-FZD5 signaling may be beneficial. This study suggested that melatonin could represent a potential intervention for the cognitive deficits induced by PBDEs. Full article