Search Results (22,499)

The objective of the study was to investigate the effects of low-protein amino acid-balanced (LPAB) diets supplemented with Astragalus polysaccharides (APSs) on the production performance, antioxidants, immunity, and biochemical index of laying hens in an elevated-temperature environment. Fifty-two-week-old Hy-Line Brown chickens (n = 768) were randomly divided into four groups, with eight replicates of 24 hens each. The control group was kept at 24 °C with a basal diet (CON), while the treatment groups were exposed to 32 °C and given the following diets: basal (HB), LPAB (HL), and LPAB with 0.5% APSs (HLA). Under heat stress, APSs increased the egg production rate and number of small white follicles, improved the yolk color, and lowered the feed conversion ratio. LPAB diets increased follicle-stimulating hormone, antioxidant enzyme activities, and anti-inflammatory cytokine activity and up-regulated related genes, whereas they reduced stress-related hormones, malondialdehyde concentrations, and triglyceride concentrations and down-regulated related genes. The addition of APSs enhanced immunoglobulin concentrations and cholesterol recovery and altered the expression of related genes. The study found that the adverse effects of high temperatures are directly related to oxidative stress. LAPB diets and APSs relatively alleviate these adverse effects. Therefore, the importance of feeding strategies such as LPAB diets and APSs for laying hens under heat stress conditions has been identified. Full article

While genetically modified crops bring significant economic benefits, the environmental safety issues they may pose have also received increasing attention. To study the impact of planting genetically modified insect-resistant crops on soil ecosystems, this research employed methods such as 16S rDNA amplicon full-length sequencing, using transgenic Cry1Ah insect-resistant corn HGK60 and its conventional counterpart Zheng 58 as subjects for a three-year continuous survey to analyze the effects of planting transgenic Cry1Ah insect-resistant corn HGK60 on the rhizosphere bacterial community. The following results were obtained. (1) A total of 216 corn rhizosphere soil samples were annotated to 51 phyla, 119 orders, 221 families, and 549 genera. (2) Overall, there was no significant difference in the composition of the rhizosphere bacterial community between HGK60 and Zheng 58 at the phylum, class, order, or family levels (p > 0.05), and the planting of HGK60 did not significantly affect the relative abundance of rhizosphere probiotics (p > 0.05). Some differences appeared only briefly and were not reproducible. (3) Alpha and beta diversity analyses showed that overall, the planting of HGK60 had no significant impact on the structure of the rhizosphere bacterial community (p > 0.05). (4) Significant changes in the rhizosphere bacterial community were observed across different growth stages of corn. It can be concluded that the planting of HGK60 has no significant impact on the rhizosphere bacteria. This study provides valuable data support for the environmental safety assessment of genetically modified crops. Full article

The temperature field distribution and temperature effects of concrete box girder bridges were found to be critical to their long-term service safety. Based on long-term structural health monitoring data, the temperature field and temperature effects of a curved continuous concrete box girder bridge in Wuhan were investigated. A finite element model of the temperature field was established through the combined application of finite element software. Extreme weather files were constructed to analyze the bridge’s temperature field and temperature effects. To enhance data reliability, wavelet analysis was employed for denoising the monitoring data. The results indicate a strong correlation between girder temperature and ambient temperature. Under solar radiation, significant vertical temperature differences and certain lateral temperature differences are observed within the concrete box girder. The accuracy of the finite element model was validated through comparison with measured data. Temperature field models featuring the most unfavorable vertical and transverse temperature gradient distribution patterns for concrete box girder bridges under extreme weather conditions in the Wuhan region were established. A distinct temperature difference not covered by specifications exists at the webs and bottom slabs of the bridge. Strong correlations were observed between both pier–girder relative displacement and bottom slab stress with the girder temperature. Full article

Background: The bacterial wilt of tomatoes, caused by Ralstonia solanacearum, is a soil-borne plant disease that causes substantial agricultural economic losses. Various nanoparticles have been utilized as antibacterial agents to mitigate pathogenic destructiveness and improve crop yields. However, there is a lack of in-depth research on how nanoparticles affect tomato metabolite levels to regulate the bacterial wilt of tomatoes. Methods: In this study, healthy and bacterial wilt-infected tomatoes were treated with Cu-Ag nanoparticles, and a metabolomics analysis was carried out. Results: The results showed that Cu-Ag nanoparticles had a significant prevention and control effect on the bacterial wilt of tomatoes. Metabolomic analysis revealed that the nanoparticles could significantly up-regulate the expression levels of terpenol lipids, organic acids, and organic oxygen compounds in diseased tomatoes, and enhance key metabolic pathways such as amino acid metabolism, carbohydrate metabolism, secondary metabolite metabolism, and lipid metabolism. These identified metabolites and pathways could regulate plant growth and defense against pathogens. Correlation analysis between the tomato microbiome and metabolites showed that most endophytic microorganisms and rhizospheric bacteria were positively correlated with fatty acyls groups and organic oxygen compounds. Conclusions: This study reveals that Cu-Ag nanoparticles can actively regulate the bacterial wilt of tomatoes by up-regulating the levels of lipid metabolism and organic oxygen compounds, providing an important theoretical basis for the application of nanoparticles in agriculture. Full article

This paper studies the collaborative inertia-frequency regulation strategies for the high renewable energy penetrated low inertia power system. Firstly, a systematic investigation is conducted to reveal the dominant dynamic characteristics and the possible challenges for such systems, and then proved the effectiveness of virtual inertia. Subsequently, a novel Laguerre-based model predictive control strategy is accordingly pro-posed, which ensures a better system states convergence ability and a reduced computational burden. The controller takes into account the system’s dual-mode feature to ensure timely response for both the inertia and the frequency support. Then, the regulation quality, operational burden and the cost are mathematically defined. The control trajectory is determined by the rolling optimization. The Gravity Searching Algorithm is utilized to determine the optimal control parameters. Finally, the proposed control strategy is validated through five case studies, demonstrating enhanced robustness, superior dynamic performance and cost-effective operation. This study provides new insights for the analysis and control strategies of the high RE penetrated low inertia systems. Full article

This study investigates the dynamic performance of mountainous crawler tractors during small-radius slope steering, providing theoretical support for power machinery design in hilly and mountainous regions. Addressing the mechanization demands in complex terrains and existing research gaps, a steering dynamics model is established. The model incorporates an amplitude-varied multi-peak cosine ground pressure distribution, employs position vectors and rotation matrices to characterize 3D pose variations in the tractor’s center of mass, and integrates slope angle, soil parameters, vehicle geometry, center-of-mass shift, bulldozing resistance, and sinkage resistance via d’Alembert’s principle. Numerical simulations using Maple 2024 analyzed variations in longitudinal offset of the instantaneous steering center, bilateral track traction forces, and bulldozing resistance with slope, speed, and acceleration. Variable-gradient steering tests on the “Soil-Machine-Crop” Comprehensive Experimental Platform demonstrated model accuracy, with <8% mean error and <12% maximum relative error between predicted and measured track forces. This research establishes a theoretical foundation for predicting, evaluating, and controlling the steering performance/stability of crawler tractors in complex slope conditions. Full article

Cross-modal semantic segmentation of individual tree LiDAR point clouds is critical for accurately characterizing tree attributes, quantifying ecological interactions, and estimating carbon storage. However, in forest environments, this task faces key challenges such as high annotation costs and poor cross-domain generalization. To address these issues, this study proposes a cross-modal semantic transfer framework tailored for individual tree point cloud segmentation in forested scenes. Leveraging co-registered UAV-acquired RGB imagery and LiDAR data, we construct a technical pipeline of “2D semantic inference—3D spatial mapping—cross-modal fusion” to enable annotation-free semantic parsing of 3D individual trees. Specifically, we first introduce a novel Multi-Source Feature Fusion Network (MSFFNet) to achieve accurate instance-level segmentation of individual trees in the 2D image domain. Subsequently, we develop a hierarchical two-stage registration strategy to effectively align dense matched point clouds (MPC) generated from UAV imagery with LiDAR point clouds. On this basis, we propose a probabilistic cross-modal semantic transfer model that builds a semantic probability field through multi-view projection and the expectation–maximization algorithm. By integrating geometric features and semantic confidence, the model establishes semantic correspondences between 2D pixels and 3D points, thereby achieving spatially consistent semantic label mapping. This facilitates the transfer of semantic annotations from the 2D image domain to the 3D point cloud domain. The proposed method is evaluated on two forest datasets. The results demonstrate that the proposed individual tree instance segmentation approach achieves the highest performance, with an IoU of 87.60%, compared to state-of-the-art methods such as Mask R-CNN, SOLOV2, and Mask2Former. Furthermore, the cross-modal semantic label transfer framework significantly outperforms existing mainstream methods in individual tree point cloud semantic segmentation across complex forest scenarios. Full article

In the field of intelligent manufacturing, image anomaly detection plays a pivotal role in automated product quality inspection. Most existing anomaly detection methods are adept at capturing local features of images, achieving high detection accuracy for structural anomalies such as cracks and scratches. However, logical anomalies typically appear normal within local regions of an image and are difficult to represent well by the anomaly score map, requiring the model to possess the capability to extract global context features. To address this challenge while balancing the detection of both structural and logical anomalies, this paper proposes a lightweight anomaly detection framework built upon EfficientAD. This framework integrates the reconstruction difference constraint (RDC) and a logical anomaly detection module. Specifically, the original EfficientAD relies on the coarse-grained reconstruction difference between the student and the autoencoder to detect logical anomalies; but, false detection may be caused by the local fine-grained reconstruction difference between the two models. RDC can promote the consistency of the fine-grained reconstruction between the student and the autoencoder, thereby effectively alleviating this problem. Furthermore, in order to detect anomalies that are difficult to represent by feature maps more effectively, the proposed logical anomaly detection module extracts and aggregates the context features of the image, and combines the feature-based method to calculate the overall anomaly score. Extensive experiments demonstrate our method’s significant improvement in logical anomaly detection, achieving 94.2 AU-ROC on MVTec LOCO, while maintaining strong structural anomaly detection performance at 98.4 AU-ROC on MVTec AD. Compared to the baseline, like EfficientAD, our framework achieves a state-of-the-art balance between both anomaly types. Full article

Beyond the extensively studied two-dimensional transition metal dichalcogenides, a wide range of non-stoichiometric transition metal sulfides, such as molybdenum sulfides and tungsten sulfides (Mo₂S₃, W₂S₃, Mo₆S₈, Mo₆S₆, NiMo₃S₄), have attracted significant attention for their promising applications in sensing, catalysis, and energy storage. It is necessary to review the current advanced progress of non-stoichiometric transition metal sulfides for various applications. Here, we systematically summarize the synthesis strategies of the non-stoichiometric transition metal sulfides, encompassing methods such as the molten salt synthesis method, high-metal-content growth strategy, and others. Particular emphasis is placed on how variations in the metal-to-sulfur ratio give rise to distinct crystal structures and electronic properties, and how these features influence their conductivity, stability, and performance. This review will deepen the understanding of the state of the art of non-stoichiometric transition metal sulfides, including the synthesis, characterization, modification, and various applications. Full article

Seasonal and environmental factors, including temperature, humidity, and storage conditions, significantly impact the stability of biochemical markers in dried blood spot (DBS) samples. This study investigates these influences specifically for adenosine (ADO) levels, a critical biomarker for neonatal screening of adenosine deaminase (ADA) deficiency. This study analyzed seasonal fluctuations in ADO concentrations across three regions in China (Ningbo, Nanjing, and Changsha) over 11 months, and evaluated ADO stability under different storage conditions (4 °C, 20 °C, and 40 °C). ADO levels demonstrated significant seasonal variability, peaking in July–August. Median concentrations increased by 111–189% in warmer months compared to winter across all sites. Storage experiments showed that ADO was most stable at 4 °C (fluctuations < 5% over 7 days), while levels at 40 °C increased by 18%. Re-adjusting the ADO reference range based on seasonal data reduced false positive rates from 2.48% to 0.15%, a 94% reduction. This study underscores the necessity of implementing seasonally dynamic reference ranges and strict cold-chain storage (4 °C) to enhance screening accuracy for ADA deficiency. The findings provide a robust foundation for optimizing neonatal screening protocols globally, especially in regions with distinct seasonal climates. Full article

Background: Brucellosis is a zoonotic bacterial disease primarily controlled through quarantine, culling, and vaccination. Live attenuated vaccines remain the most effective countermeasure, yet their application is limited by residual virulence and diagnostic interference. This study developed three rough-type attenuated Brucella melitensis mutants (G7, G8, G16) and evaluated their potential as DIVA (Differentiating Infected from Vaccinated Animals) vaccine candidates. Methods: Rough phenotypes were characterized through heat agglutination, acridine orange staining, and immunoblotting. Macrophage cytotoxicity was assessed via LDH release assays, while RT-qPCR analyzed macrophage activation capacity. Mouse infection and immunization-challenge experiments, complemented by histopathology, evaluated residual virulence and protective immunity. Antibody profiles were determined by ELISA, and DIVA capability was verified using LPS-coated ELISA. Results: G7 and G8 exhibited complete rough phenotypes, whereas G16 retained partial O-antigen (semi-rough). All rough mutants induced macrophage cytotoxicity and activation. The strains showed attenuated virulence with no viable bacteria recovered from spleens at 4 weeks post-inoculation. Histopathology revealed no liver lesions at 6 weeks post-inoculation. Immunized mice predominantly produced IgG2a-dominated Th1-type responses. The immune protection levels of G7 and G16 matched the reference vaccine M5–90Δ26, while G8 showed slightly lower efficacy. LPS-ELISA effectively differentiated vaccinated from infected animals via concurrent IgM/IgG detection. Conclusions: This study demonstrates that the rough-type B. melitensis mutants G7 and G16 serve as promising DIVA vaccine candidates, offering strong protection with low residual virulence while enabling serological differentiation between vaccinated and infected animals, highlighting their potential as effective vaccines for brucellosis control. Full article

Current research primarily focuses on using CFRP materials to strengthen small or medium-sized test specimens. To address this, our study employed ABAQUS software to analyze the axial compression behavior of large-scale reinforced concrete (RC) columns strengthened with prestressed carbon fiber reinforced polymer (CFRP) sheets. We conducted comparative analyses on key parameters: the prestress level applied to the CFRP, the width of CFRP strips, the spacing between strips, the confinement ratio, and the overall load–displacement curves of the columns. The results demonstrate that applying prestress significantly improves the efficiency of stress transfer in the CFRP sheet, effectively mitigating the stress lag phenomenon common in traditional CFRP strengthening, leading to a substantially enhanced strengthening effect. The CFRP wrapping method critically impacts performance: increasing the confinement ratio enhanced ultimate load capacity by 21.8–59.9%; reducing the strip spacing increased capacity by 21.8–50.4%; and widening the strips boosted capacity by 38.7–58%. Although full wrapping achieved the highest capacity increase (up to 73.2%), it also incurred significantly higher costs. To ensure the required strengthening effect while optimizing economic efficiency and CFRP material utilization, the strip wrapping technique is recommended. For designing optimal reinforcement, priority should be given to optimizing the confinement ratio first, followed by adjusting strip width and spacing. Proper optimization of these parameters significantly enhances the strengthened member’s ultimate load capacity, ductility, and energy dissipation capacity. This study enriches the theoretical foundation for prestressed CFRP strengthening and provides an essential basis for rationally selecting prestress levels and layout parameters in engineering practice, thereby aiding the efficient design of strengthening projects for structures like bridges, with significant engineering and scientific value. Full article

One of the important tasks in sand liquefaction assessment is to evaluate the likelihood of soil liquefaction. However, most liquefaction assessment methods are deterministic for influencing factors and fail to calculate the liquefaction probability by systematically considering the probability distributions of soil factors. Based on field liquefaction investigation cases, probability distribution fitting and a hypothesis test were carried out. For the variables that failed to pass the fitting and test, the kernel density estimation was conducted. Methods for calculating the liquefaction probability using a Monte Carlo simulation with the probability distribution were then proposed. The results indicated that for (N₁)₆₀, SM, S, and GM followed a Gaussian distribution, while CL and ML followed a lognormal distribution; for FC, SM and GM followed a lognormal distribution; and for d₅₀, ML and S followed a Gaussian and lognormal distribution, respectively. The other factors’ distribution curves can be calculated by kernel density estimation. It is feasible to calculate the liquefaction probability based on a Monte Carlo simulation of the variable distribution. The result of the liquefaction probability calculation in this case was similar to that of the existing probability model and was consistent with actual observations. Regional sample differences were considered by introducing the normal distribution error term, and the liquefaction probability accuracy could be improved to a certain extent. The liquefaction probability at a specific seismic level or the total probability within a certain period in the future can be calculated with the method proposed in this paper. It provides a data-driven basis for realistically estimating the likelihood of soil liquefaction under seismic loading and contributes to site classification, liquefaction potential zoning, and ground improvements in seismic design decisions. The practical value of seismic hazard mapping and performance-based design in earthquake-prone regions was also demonstrated. Full article

To accommodate the extreme thermodynamic effects and erosion damage in throttling equipment for ultra-high-pressure natural gas wells (175 MPa), a coupled multiphase flow erosion numerical model for nozzles was established. This model incorporates a real gas compressibility factor correction and is based on the renormalized k-ε RNG (Renormalization Group k-epsilon model, a turbulence model that simulates the effects of vortices and rotation in the mean flow by modifying turbulent viscosity) turbulence model and the Discrete Phase Model (DPM, a multiphase flow model based on the Eulerian–Lagrangian framework). The study revealed that the nozzle flow characteristics follow an equal-percentage nonlinear regulation pattern. Choked flow occurs at the throttling orifice throat due to supersonic velocity (Ma ≈ 3.5), resulting in a mass flow rate governed solely by the upstream total pressure. The Joule–Thomson effect induces a drastic temperature drop of 273 K. The outlet temperature drops below the critical temperature for methane hydrate phase transition, thereby presenting a substantial risk of hydrate formation and ice blockage in the downstream outlet segment. Erosion analysis indicates that particles accumulate in the 180° backside region of the cage sleeve under the influence of secondary flow. At a 30% opening, micro-jet impact causes the maximum erosion rate to surge to 3.47 kg/(m²·s), while a minimum erosion rate is observed at a 50% opening. Across all opening levels, the maximum erosion rate consistently concentrates on the oblique section of the plunger front. Results demonstrate that removing the front chamfer of the plunger effectively improves the internal erosion profile. These findings provide a theoretical basis for the reliability design and risk prevention of surface equipment in deep ultra-high-pressure gas wells. Full article

Infrared-visible image fusion quality is critical for nighttime perception in autonomous driving and surveillance but suffers severe degradation under extreme low-light conditions, including irreversible texture loss in visible images, thermal boundary diffusion artifacts, and overexposure under dynamic non-uniform illumination. To address these challenges, a Decomposition–Disentanglement–Dynamic Compensation framework, D3Fusion, is proposed. Firstly, a Retinex-inspired Decomposition Illumination Net (DIN) decomposes inputs into enhanced images and degradative illumination maps for joint low-light recovery. Secondly, an illumination-guided encoder and a multi-scale differential compensation decoder dynamically balance cross-modal features. Finally, a progressive three-stage training paradigm from illumination correction through feature disentanglement to adaptive fusion resolves optimization conflicts. Compared to State-of-the-Art methods, on the LLVIP, TNO, MSRS, and RoadScene datasets, D3Fusion achieves an average improvement of 1.59% in standard deviation (SD), 6.9% in spatial frequency (SF), 2.59% in edge intensity (EI), and 1.99% in visual information fidelity (VIF), demonstrating superior performance in extreme low-light scenarios. The framework effectively suppresses thermal diffusion artifacts while mitigating exposure imbalance, adaptively brightening scenes while preserving texture details in shadowed regions. This significantly improves fusion quality for nighttime images by enhancing salient information, establishing a robust solution for multimodal perception under illumination-critical conditions. Full article