Search Results (960)

Phosphorus (P) release from reservoir sediments critically influences water quality and ecosystem stability. This study analyzed surface sediments from four representative zones to investigate phosphorus fraction distribution, key influencing factors, and implications for water quality. Results showed that total phosphorus (TP) content in sediments from main and tributary inflow zones was significantly higher than in open-water and transition zones. Inorganic phosphorus (IP) was the dominant form, with iron-bound phosphorus (Fe-P) accounting for 33.2–42.0% of IP. A strong correlation existed between P release and the Fe/P molar ratio; notably, when the ratio approached 10, phosphorus desorption increased significantly, indicating a shift from sink to source. Sediments with grain sizes <0.01 mm had the highest P release rates, suggesting particle size, Fe content, and hydrodynamics jointly regulate P mobilization. Using the Diffusive Gradients in Thin Films (DGT) technique, phosphorus release in inflow zones exceeded 1 g/m² in all hydrological periods, contributing substantially to internal loading. Sediment-derived P primarily influenced bottom water, while surface water was more affected by external inputs. These findings highlight the spatial heterogeneity of P release and underscore the need for zone-specific management strategies in reservoir systems. Full article

To enhance the structural safety in wind-sand regions, this study employs the Euler-Lagrange numerical method to investigate the wind pressure characteristics of typical low-rise auxiliary buildings in a strong wind-blown sand environment. The results reveal that sand particle motion dissipates wind energy, leading to a slight reduction in average wind speed, while the increase in small-scale vortex energy enhances fluctuating wind speed. In the sand-laden wind field, the average wind pressure coefficient shows no significant change, whereas the fluctuating wind pressure coefficient increases markedly, particularly in the windward region of the building. Analysis of the skewness and kurtosis of wind pressure reveals that the non-Gaussian characteristics of wind pressure are amplified in the sand-laden wind, thereby elevating the risk of damage to the building envelope. Consequently, it is recommended that the design fluctuating wind load for envelopes and components of low-rise buildings in wind-sand regions be increased by 10% to enhance structural resilience. Full article

Illegal mining operations induce cascading ecosystem degradation by causing extensive ground subsidence, necessitating accurate underground goaf localization for effectively induced-hazard mitigation. The conventional locating method applied the synthetic aperture radar interferometry (InSAR) technique to obtain ground deformation to estimate underground goaf parameters, and the locating accuracy was crucially contingent upon the appropriateness of nonlinear deformation function models selection and the precision of geological parameters acquisition. However, conventional model-driven underground goaf locating frameworks often fail to sufficiently integrate prior geological information during the model selection process, potentially leading to increased positioning errors. In order to enhance the operational efficiency and locating accuracy of underground goaf, deformation model selection must be aligned with site-specific geological conditions under varying cases of prior information. To address these challenges, this study categorizes prior geological information into three different hierarchical levels (detailed, moderate, and limited) to systematically investigate the correlations between model selection and prior information. Subsequently, field validation was carried out by applying two different non-linear deformation function models, Probability Integral Model (PIM) and Okada Dislocation Model (ODM), with three different prior geological information conditions. The quantitative performance results indicate that, (1) under a detailed prior information condition, PIM achieves enhanced dimensional parameter estimation accuracy with 6.9% reduction in maximum relative error; (2) in a moderate prior information condition, both models demonstrate comparable estimation performance; and (3) for a limited prior information condition, ODM exhibits superior parameter estimation capability showing 3.4% decrease in maximum relative error. Furthermore, this investigation discusses the influence of deformation spatial resolution, the impacts of azimuth determination methodologies, and performance comparisons between non-hybrid and hybrid optimization algorithms. This study demonstrates that aligning the selection of deformation models with different types of prior geological information significantly improves the accuracy of underground goaf detection. The findings offer practical guidelines for selecting optimal models based on varying information scenarios, thereby enhancing the reliability of disaster evaluation and mitigation strategies related to illegal mining. Full article

Background: Influenza viruses are major pathogens responsible for respiratory infections and pose significant risks to densely populated urban areas. RT-qPCR has made substantial contributions in controlling virus transmission during previous COVID-19 epidemics, but it faces challenges in terms of detection time for large sample sizes and susceptibility to nucleic acid contamination. Methods: Our study designed loop-mediated isothermal amplification primers for three common influenza viruses: A/H3N2, A/H1N1, and B/Victoria, and utilized a 4-channel microfluidic chip to achieve simultaneous detection. The chip initiates amplification by centrifugation and allows testing of up to eight samples at a time. Results: By creating a closed amplification system in the microfluidic chip, aerosol-induced nucleic acid contamination can be prevented through physically isolating the reaction from the operating environment. The chip can specifically detect A/H1N1, A/H3N2, and B/Victoria and has no signal for other common respiratory viruses. The testing process can be completed within 1 h and can be sensitive to viral RNA at concentrations as low as 10⁻³ ng/μL for A/H1N1 and A/H3N2 and 10⁻¹ ng/μL for B/Victori. A total of 296 virus swab samples were further analyzed using the microfluidic chip method and compared with the classical qPCR method, which resulted in high consistency. Conclusions: Our chip enables faster detection of influenza virus and avoids nucleic acid contamination, which is beneficial for POCT establishment and has lower requirements for the operating environment. Full article

An oat protein isolate is an ideal raw material for producing a wide range of plant-based products. However, oat protein exhibits weak functional properties, particularly in emulsification. Casein-based ingredients are commonly employed to enhance emulsifying properties as a general practice in the food industry. pH-shifting processing is a straightforward method to partially unfold protein structures. This study modified a mixture of an oat protein isolate (OPI) and casein by combining a pH adjustment (adjusting the pH of two solutions to 12, mixing them at a 3:7 ratio, and maintaining the pH at 12 for 2 h) with an atmospheric cold plasma (ACP) treatment to improve the emulsifying properties. The results demonstrated that the ACP treatment significantly enhanced the solubility of the OPI/casein mixtures, with a maximum solubility of 82.63 ± 0.33%, while the ζ-potential values were approximately −40 mV, indicating that all the samples were fairly stable. The plasma-induced increase in surface hydrophobicity supported greater protein adsorption and redistribution at the oil/water interface. After 3 min of treatment, the interfacial pressure peaked at 8.32 mN/m. Emulsions stabilized with the modified OPI/casein mixtures also exhibited a significant droplet size reduction upon extending the ACP treatment to 3 min, decreasing from 5.364 ± 0.034 μm to 3.075 ± 0.016 μm. The resulting enhanced uniformity in droplet size distribution signified the formation of a robust interfacial film. Moreover, the ACP treatment effectively enhanced the emulsifying activity of the OPI/casein mixtures, reaching (179.65 ± 1.96 m²/g). These findings highlight the potential application value of OPI/casein mixtures in liquid dairy products. In addition, dairy products based on oat protein are more conducive to sustainable development than traditional dairy products. Full article

Lipopolysaccharide (LPS), the defining outer membrane component of Gram-negative bacteria, is a potent immunostimulant recognized by Toll-like receptor 4 (TLR4). While extensively studied for its roles in immune activation and barrier disruption, the potential function of LPS as a developmental cue remains largely unexplored. By leveraging Caenorhabditis elegans and its genetic and gnotobiotic advantages, we screened a panel of Escherichia coli LPS biosynthesis mutants. This screen revealed that the loss of outer core glycosylation in the ∆rfaG mutant causes significant developmental delay independent of bacterial metabolism. Animals exhibited developmental delay that was rescued by exogenous LPS or amino acid supplementation, implicating that LPS triggers nutrient-sensing signaling. Mechanistically, this developmental arrest was mediated by the host FOXO transcription factor DAF-16, which is the key effector of insulin/IGF-1 signaling (IIS). Our findings uncover an unprecedented role for microbial LPS as a critical regulator of host development, mediated through conserved host IIS pathways, fundamentally expanding our understanding of host–microbe crosstalk. Full article

Thoracic endovascular aortic repair (TEVAR) for cardiovascular diseases often encounters complications that are closely linked to the mechanical properties of stent-grafts. Both the design and material properties influence device performance, but the specific impacts of material properties remain underexplored and poorly understood. This study aims to fill this gap by systematically investigating how material science can modulate stent-graft mechanics. Four types of bare nitinol stents combined with expanded polytetrafluoroethylene (e-PTFE) or polyethylene terephthalate (PET) grafts were modeled via finite element analysis, creating eight stent-graft configurations. Key mechanical properties—flexibility, crimpability, and fatigue performance—were evaluated to dissect material effects. The results revealed that nitinol’s properties significantly influenced all performance metrics, while PET grafts notably enhanced flexibility and fatigue life. No significant differences in equivalent stress were found between PET and e-PTFE grafts, and both had minimal impacts on radial force. This work underscores the potential of material science-driven optimization to enhance stent-graft performance for improved clinical outcomes. Full article

Modeling and simulation of aneurysm formation, growth, and rupture plays an essential role in a wide spectrum of application scenarios, ranging from risk stratification to stability prediction, and from clinical decision-making to treatment innovation. Unfortunately, it remains a non-trivial task due to the difficulties imposed by the complex and under-researched pathophysiological mechanisms behind the different development stages of various aneurysms. In this paper, we present a novel computational method for aneurysm simulation using smoothed particle hydrodynamics (SPH). Firstly, we consider blood in a vessel as a kind of incompressible fluid and model its flow dynamics using the SPH method; and then, to simulate aneurysm growth and rupture, the relationship between the aneurysm development and the properties of fluid particles is established by solving the motion control equation. In view of the prevalence of aneurysms in bifurcation vessels, we further enhance the capability of the model by introducing a solution for bifurcation aneurysms simulation according to Murray’s law. In addition, a CUDA parallel computing scheme is also designed to speed up the simulation process. To evaluate the performance of the proposed method, we conduct extensive experiments with different physical parameters associated with morphological characteristics of an aneurysm. The experimental results demonstrate the effectiveness and efficiency of proposed method in modeling and simulating aneurysm formation, growth, and rupture. Full article

Multi-focus image fusion (MFIF) is an image-processing method that aims to generate fully focused images by integrating source images from different focal planes. However, the defocus spread effect (DSE) often leads to blurred or jagged focus/defocus boundaries in fused images, which affects the quality of the image. To address this issue, this paper proposes a novel model that embeds the Kolmogorov–Arnold network with convolutional layers in parallel within the U-Net architecture (KCUNet). This model keeps the spatial dimensions of the feature map constant to maintain high-resolution details while progressively increasing the number of channels to capture multi-level features at the encoding stage. In addition, KCUNet incorporates a content-guided attention mechanism to enhance edge information processing, which is crucial for DSE reduction and edge preservation. The model’s performance is optimized through a hybrid loss function that evaluates in several aspects, including edge alignment, mask prediction, and image quality. Finally, comparative evaluations against 15 state-of-the-art methods demonstrate KCUNet’s superior performance in both qualitative and quantitative analyses. Full article

Substation equipment defect detection is a critical aspect of ensuring the reliability and stability of modern power grids. However, existing deep-learning-based detection methods often face significant challenges in real-world deployment, primarily due to low detection accuracy and inconsistent anomaly definitions across different substation environments. To address these limitations, this paper proposes the Language-Guided Enhanced Anomaly Power Equipment Detection Network (LEAD-Net), a novel framework that leverages text-guided learning during training to significantly improve defect detection performance. Unlike traditional methods, LEAD-Net integrates textual descriptions of defects, such as historical maintenance records or inspection reports, as auxiliary guidance during training. A key innovation is the Language-Guided Anomaly Feature Enhancement Module (LAFEM), which refines channel attention using these text features. Crucially, LEAD-Net operates solely on image data during inference, ensuring practical applicability. Experiments on a real-world substation dataset, comprising 8307 image–text pairs and encompassing a diverse range of defect categories encountered in operational substation environments, demonstrate that LEAD-Net significantly outperforms state-of-the-art object detection methods (Faster R-CNN, YOLOv9, DETR, and Deformable DETR), achieving a mean Average Precision (mAP) of 79.51%. Ablation studies confirm the contributions of both LAFEM and the training-time text guidance. The results highlight the effectiveness and novelty of using training-time defect descriptions to enhance visual anomaly detection without requiring text input at inference. Full article

Owing to the intricate variability of underwater environments, images suffer from degradation including light absorption, scattering, and color distortion. However, U-Net architectures severely limit global context utilization due to fixed-receptive-field convolutions, while traditional attention mechanisms incur quadratic complexity and fail to efficiently fuse spatial–frequency features. Unlike local enhancement-focused methods, HMENet integrates a transformer sub-network for long-range dependency modeling and dual-domain attention for bidirectional spatial–frequency fusion. This design increases the receptive field while maintaining linear complexity. On UIEB and EUVP datasets, HMENet achieves PSNR/SSIM of 25.96/0.946 and 27.92/0.927, surpassing HCLR-Net by 0.97 dB/1.88 dB, respectively. Full article

This work systematically altered the molar ratio of CaO and MgO (R = [CaO]/[(CaO + MgO)], mol%) to elucidate the underlying mechanisms driving the observed changes in macroscopic properties. The results indicated that as CaO increasingly replaced MgO, the rise in the content of non-bridging oxygen led to the depolymerization of the glass structure. A quantitative analysis of Qⁿ units in the [SiO₄] tetrahedron using ²⁹Si MAS NMR revealed that a non-monotonic variation appeared when the Q⁴ unit reached a minimum at R = 0.7. Meanwhile, the chemical environment of aluminum also varies with the R, and the presence of high-coordinated aluminum species is observed when Ca²⁺ and Mg²⁺ ions coexist. In terms of overall performance, both density and molar volume exhibited a linear trend. However, thermal stability, viscosity, characteristic temperatures (including melting temperature, Littleton softening temperature, working point temperature, and glass transition temperature), and mechanical properties showed deviations from linearity. Additionally, four non-isothermal thermodynamics was employed to quantitatively assess the thermal stability of samples C-0.7 and C-1. The insights gained from this study will aid in the development of advanced glass materials with tailored properties for industrial applications. Full article

Background/Objectives: This study aimed to evaluate the effects of lung injury induced by insoluble uranium oxide particles on gut microbiota and related metabolites in rats. Methods: The rats were randomly divided into six UO₂ dose groups. A rat lung injury model was established through UO₂ aerosol. The levels of uranium in lung tissues were detected by ICP-MS. The expression levels of the inflammatory factors and fibrosis indexes were measured by enzyme-linked immunosorbent assay. Paraffin embedding-based hematoxylin & eosin staining for the lung tissue was performed to observe the histopathological imaging features. Metagenomic sequencing technology and HM700-targeted metabolomics were conducted in lung tissues. Results: Uranium levels in the lung tissues increased with dose increase. The expression levels of Tumor Necrosis Factor-α (TNF-α), Interleukin-1β (IL-1β), Collagen I, and Hydroxyproline (Hyp) in rat lung homogenate increased with dose increase. Inflammatory cell infiltration and the deposition of extracellular matrix were observed in rat lung tissue post-exposure. Compared to the control group, the ratio of Firmicutes and Bacteroides in the gut microbiota decreased, the relative abundance of Akkermansia_mucinphila decreased, and the relative abundance of Bacteroides increased. The important differential metabolites mainly include αlpha-linolenic acid, gamma-linolenic acid, 2-Hydroxybutyric acid, Beta-Alanine, Maleic acid, Hyocholic acid, L-Lysine, L-Methionine, L-Leucine, which were mainly concentrated in unsaturated fatty acid biosynthesis, propionic acid metabolism, aminoacyl-tRNA biosynthesis, phenylalanine metabolism, and other pathways in the UO₂ group compared to the control group. Conclusions: These findings suggest that uranium-induced lung injury can cause the disturbance of gut microbiota and its metabolites in rats, and these changes are mainly caused by Akkermansia_mucinphila and Bacteroides, focusing on unsaturated fatty acid biosynthesis and the propionic acid metabolism pathway. Full article

Physalis Alkekengi L. var. franchetii (Mast.) Makino (PAF), which is used in both food and medicine, has a long history of about 1800 years of application in China. There are many active constituents in the calyx of PAF. Physalins and physalins with a single oxygen bridge are the unique components of the PAF calyx. Physalins with multiple biological activities, including anticancer activity, antimicrobial activity, anti-inflammatory activity, etc., have been found. As such, physalins deserve to be studied further. In this study, we aimed to extract, separate, and identify the effective components of physalins from the calyx of PAF and investigate ability to inhibit the proliferation of tumor cell lines. Three new physalins, physalin VIII (1), 3α-hydroxy-2,3,25,27-tetrahydro-4,7-didehydro-7-deoxyneophysalin A (2), and physalin IX (3), along with three known compounds, physalin L (4), physalin D (5), and alkekengilin A (6) were isolated from PAF calyxes. Physalin D was superior to the positive control drug cisplatin in inhibiting the proliferation of five tumor cell lines. The physalin compounds exhibited potential antitumor activity, being deemed worthy of further research in the fields of antitumor drug development and the application in health foods. Full article

With the advancement of automation in modern agriculture, the demand for intelligence in the post-picking sorting of fruits and vegetables is increasing. As a significant global agricultural product, the defect detection and sorting of tomato is essential to ensure quality and improve economic value. However, the traditional detection method (manual screening) is inefficient and involves high labor intensity. Therefore, a defect detection model named YOLO-RGDD is proposed based on YOLOv12s to identify five types of tomato surface defects (scars, gaps, white spots, spoilage, and dents). Firstly, the original C3k2 module and A2C2f module of YOLOv12 were replaced with RFEM in the backbone network to enhance feature extraction for small targets without increasing computational complexity. Secondly, the Dysample–Slim-Neck of the YOLO-RGDD was developed to reduce the computational complexity and enhance the detection of minor defects. Finally, dynamic convolution was used to replace the conventional convolution in the detection head in order to reduce the model parameter count. The experimental results show that the average precision, recall, and F1-score of the proposed YOLO-RGDD model for tomato defect detection reach 88.5%, 85.7%, and 87.0%, respectively, surpassing advanced object recognition detection algorithms. Additionally, the computational complexity of the YOLO-RGDD is 16.1 GFLOPs, which is 24.8% lower than that of the original YOLOv12s model (21.4 GFLOPs), facilitating the model’s deployment in automated agricultural production. Full article