Search Results (19,937)

Background: Effective regenerative therapeutics for acute and chronic wounds remain a critical unmet need in biomedicine. Objectives: This study aimed to develop novel collagen–cerium oxide nanoparticle hydrogels designed to enhance cellular metabolism, proliferation, and antioxidant/antimutagenic activity, accelerating wound regeneration in vivo. Methods: Collagen–nanocerium composites were synthesized by combining a collagen extract with cerium oxide nanoparticles at defined concentrations. In vitro assays using human fibroblasts identified two formulations that enhanced proliferation and metabolic activity by 42–50%. FTIR spectroscopy confirmed chemical interactions within the composite matrix. Toxicity, antioxidant, and antigenotoxic effects were evaluated using Escherichia coli MG1655 lux-biosensors to assess their general toxicity, antioxidant and pro-oxidant activities, and antigenotoxic and promutagenic effects. In vivo efficacy was tested in Wistar rats with full-thickness skin wounds. Treated groups were compared to untreated controls and Dexpanthenol-treated positive controls. On days 3, 7, and 14, healing was assessed clinically, histologically, and morphometrically. Results: Biosensor analysis demonstrated non-toxicity and antigenotoxic activity of the nanocomposites, reduced DNA damage by up to 45%, providing 31–49% protection against H₂O₂ and 15–23% against O₂⁻ radicals. The animal study results demonstrated significantly accelerated healing with both nanocomposites versus control and comparison groups, evidenced by improved tissue regeneration, reduced inflammation, and increased fibroblast infiltration. Conclusions: The developed hydrogels exhibit promising pharmacological profiles, including antioxidant, antimutagenic, anti-inflammatory, and pro-regenerative effects validated across in vitro and in vivo models. Full article

Automatically segmenting coal cracks in CT images is crucial for 3D reconstruction and the physical properties of mines. This paper proposes an automatic pixel-level deep learning method called Attention Double U²-Net to enhance the segmentation accuracy of coal cracks in CT images. Due to the lack of public datasets of coal CT images, a pixel-level labeled coal crack dataset is first established through industrial CT scanning experiments and post-processing. Then, the proposed method utilizes a Double Residual U-Block structure (DRSU) based on U²-Net to improve feature extraction and fusion capabilities. Moreover, an attention mechanism module is proposed, which is called Atrous Asymmetric Fusion Non-Local Block (AAFNB). The AAFNB module is based on the idea of Asymmetric Non-Local, which enables the collection of global information to enhance the segmentation results. Compared with previous state-of-the-art models, the proposed Attention Double U²-Net method exhibits better performance over the coal crack CT image dataset in various evaluation metrics such as PA, mPA, MIoU, IoU, Precision, Recall, and Dice scores. The crack segmentation results obtained from this method are more accurate and efficient, which provides experimental data and theoretical support to the field of CBM exploration and damage of coal. Full article

Based on a metro project in Hangzhou, combined with the investigation of on-site seepage and leakage problems and finite element numerical simulation, the influence of local seepage and leakage in shield tunnels in water-rich silty clay strata on stratum settlement and lining structure deformation was studied. During the simulation process, two working conditions, namely leakage at the joint of the segment and local damage leakage, were, respectively, set up to analyze the distribution of pore water pressure, the development characteristics of stratum settlement and the response of the lining structure. The results show that the pore water pressure near the leakage area is significantly reduced. The pore pressure at the joint of the segment and the local leakage position is reduced by 81.22% and 76.88%, respectively, compared with the hydrostatic pressure at the same burial depth, and the reduction at the bottom of the model is 11.45% and 6.46%, respectively. Under different working conditions, the settlement rates all increased first and then tended to stabilize. The maximum surface settlements were 91 mm and 32 mm, respectively, and the former exceeded the control value. The surface settlement of local leakage is distributed in a concave pattern, and the peak settlement is located directly above the leakage point. The lining structure deforms significantly in both the upper and lower directions, both shifting downward towards the stratum. The maximum displacement and deformation caused by the leakage at the joint of the segment reached 78.26 mm and 24.38 mm, respectively, with obvious over-limits. It is recommended to prioritize the sealing treatment of the leakage area at the joint. The research results can provide theoretical references for the control of water leakage and structural safety evaluation of shield tunnels in water-rich and weak strata. Full article

Suspended monorail systems are increasingly adopted in urban rail transit due to their small land requirements and environmental benefits. However, welded details in track beams are prone to fatigue cracking under repeated service loads, posing risks to long-term structural safety. This study investigates the fatigue performance of suspended monorail track beams through 1:4 scaled fatigue experiments and finite element (FE) simulations. Critical fatigue-sensitive locations were identified at the mid-span longitudinal stiffener–bottom flange weld toe and the mid-span web–bottom flange weld toe. Under the most unfavorable operating condition (train speed of 30 km/h), the corresponding hot-spot stresses were 28.48 MPa and 27.54 MPa, respectively. Stress deviations between scaled and full-scale models were within 7%, verifying the feasibility of using scaled models for fatigue studies. Fatigue life predictions based on the IIW hot-spot stress method and Eurocode S–N curves showed that the critical details exceeded the 100-year design requirement, with estimated fatigue lives of 2.39 × 10⁸ and 5.95 × 10⁸ cycles. Furthermore, a modified damage equivalent coefficient method that accounts for traffic volume and train speed was proposed, yielding coefficients of 2.54 and 3.06 for the two fatigue-prone locations. The results provide a theoretical basis and practical reference for fatigue life evaluation, design optimization, and code development of suspended monorail track beam structures. Full article

The extraction of multiple coal seams not only increases the risk of water inrush disasters in mines but also exacerbates the long-term depletion of groundwater, posing challenges for sustainable resource management in ecologically sensitive areas. This study utilizes the plastic damage–permeability coupling model in Abaqus CAE to analyze the impact of coal seam thickness and pillar layout on the evolution of the plastic zone and groundwater loss in the Shen Dong mining area, specifically at the Buertai coal mine. The results indicate that coal seam thickness is a strong driving factor for aquifer depletion: the water inflow under a 10 m thick coal seam is 1.56 times that under a 4 m thick coal seam. In contrast, the optimized staggered pillar layout alters stress distribution and reduces the water inflow under deeper coal seams by approximately 38%, demonstrating excellent water-saving potential. To translate these findings into a sustainability framework, this study proposes three new indicators: the Groundwater Loss Index (GLI) to quantify depletion intensity, the Aquifer Protection Efficiency (APE) to assess protection benefits, and the Sustainability Trade-off Index (STI) to balance coal recovery, safety, and groundwater protection. These metrics establish a dual-objective optimization approach that ensures safe mining and the sustainability of the aquifer. This study provides practical benchmarks for environmental impact assessment and aligns with the global sustainable development agenda, particularly the United Nations Sustainable Development Goals concerning clean water (SDG 6), responsible consumption (SDG 12), and terrestrial ecosystems (SDG 15). By incorporating groundwater protection into the design of the Buertai coal mine, this study advances the transition of multi-seam mining at Buertai from disaster prevention to sustainability orientation. Full article

Background: Neuroblastoma (NB) presents significant challenges in pediatric oncology, particularly in high-risk cases where local recurrence occurs in ~35% of patients. Cold Atmospheric Plasma (CAP) has emerged as a promising treatment due to its selective cytotoxicity toward cancer cells while sparing normal cells. Methods: This study assessed CAP efficacy using in vitro NB cell lines (SK-N-AS and LAN-5) and in vivo xenograft murine models. In vitro, CAP was applied via a helium jet, and cellular responses were evaluated for viability, reactive oxygen species (ROS), lipid peroxidation, DNA damage, and cell cycle, while apoptosis was measured by Annexin V/PI flow cytometry. In vivo, CAP was applied to unresected tumors and residual tumors after incomplete resection. Tumor regrowth was monitored, and histological analysis was performed. Results: CAP reduced NB cell viability in a dose- and time-dependent manner by increasing intracellular ROS and lipid peroxidation. CAP-treated NB cells showed a 50% rise in oxidative DNA damage, a two-fold increase in apoptosis, and alterations in cell-cycle progression, while normal fibroblasts showed modest effects. CAP predominantly induced apoptosis, though secondary necrosis appeared with prolonged exposures, consistent with caspase-3 and PARP pathways. In xenografts, CAP reduced tumor diameter by 60% and increased caspase-3-positive cells, with minimal effects on normal tissue. Conclusions: CAP demonstrates strong therapeutic potential as a targeted, non-invasive NB treatment, particularly for residual tumors near vascular structures with consistent exposure times (60–300 s). Full article

To address prevalent challenges in field-based wheat pest recognition—namely, viewpoint perturbations, sample scarcity, and heterogeneous data distributions—a pest identification framework named CropCLR-Wheat is proposed, which integrates self-supervised contrastive learning with an attention-enhanced mechanism. By incorporating a viewpoint-invariant feature encoder and a diffusion-based feature filtering module, the model significantly enhances pest damage localization and feature consistency, enabling high-accuracy recognition under limited-sample conditions. In 5-shot classification tasks, CropCLR-Wheat achieves a precision of 89.4%, a recall of 87.1%, and an accuracy of 88.2%; these metrics further improve to 92.3%, 90.5%, and 91.2%, respectively, under the 10-shot setting. In the semantic segmentation of wheat pest damage regions, the model attains a mean intersection over union (mIoU) of 82.7%, with precision and recall reaching 85.2% and 82.4%, respectively, markedly outperforming advanced models such as SegFormer and Mask R-CNN. In robustness evaluation under viewpoint disturbances, a prediction consistency rate of 88.7%, a confidence variation of only 7.8%, and a prediction consistency score (PCS) of 0.914 are recorded, indicating strong stability and adaptability. Deployment results further demonstrate the framework’s practical viability: on the Jetson Nano device, an inference latency of 84 ms, a frame rate of 11.9 FPS, and an accuracy of 88.2% are achieved. These results confirm the efficiency of the proposed approach in edge computing environments. By balancing generalization performance with deployability, the proposed method provides robust support for intelligent agricultural terminal systems and holds substantial potential for wide-scale application. Full article

[Introduction] The study aimed to investigate the protective effect of Lycium ruthenicum polysaccharides (LRPs) against carbon tetrachloride (CCl₄)-induced acute liver injury (ALI). [Method] After LRP was extracted and characterized, a CCl₄-induced cell damage and mouse ALI model was established to evaluate its anti-inflammatory and antioxidant capacities. [Results] The results demonstrated that LRP markedly attenuated hepatocyte necrosis, alleviated cellular edema and degeneration, and preserved nuclear integrity to sustain hepatic function, thereby restoring hepatic architecture. It downregulated serum ALT and AST levels, reduced MDA content in liver tissue, and enhanced SOD activity. Additionally, LRP inhibited the expression of pro-inflammatory cytokines TNF-α and IL-6, while upregulating the anti-inflammatory cytokine IL-10. [Conclusions] These findings suggest that LRP effectively alleviates CCl₄-induced ALI through both antioxidant and anti-inflammatory effects, demonstrating its potential as a novel liver-protective agent. Full article

Grinding semiconductor wafers with high hardness, such as SiC, remains a significant challenge due to the need to maximize material removal rates while minimizing subsurface damage. In the back-grinding process, two key parameters—the elastic modulus (Eb) of the grinding wheel bond and the fracture toughness (K_IC) of the wafer—play a critical role in governing the behavior of diamond and the extent of wafer damage. This study systematically investigated the effect of Eb and K_IC on diamond protrusion height (h_p), surface roughness (Ra), grinding forces, and the morphology of generated debris. The study encompassed four wafer types—Si, GaP, sapphire, and ground SiC—using five Back-Grinding Wheels (BGWs), with Eb ranging from 95.24 to 131.38 GPa. A log–linear empirical relationship linking ℎₚ to Eb and K_IC was derived and experimentally verified, demonstrating high predictive accuracy across all wafer–wheel combinations. Surface roughness (Ra) was measured in the range of 0.486 − 1.118𝜇m, debris size ranged from 1.41 to 14.74𝜇m, and the material removal rate, expressed as a thickness rate, varied from 555 to 1546𝜇m/h (equivalent to 75−209 mm³/min using an effective processed area of 81.07 cm²). For SiC, increasing the bond modulus from 95.24 to 131.38 GPa raised the average h_p from 9.0 to 1.2 um; the removal rate peaked at 122.07 GPa, where subsurface damage (SSD) was minimized, defining a practical grindability window. These findings offer practical guidance for selecting grinding wheel bond compositions and configuring process parameters. In particular, applying a higher Eb is recommended for harder wafers to ensure sufficient diamond protrusion, while an appropriate dressing must be employed to prevent adverse effects from excessive stiffness. By balancing removal rate, surface quality, and subsurface damage constraints, the results support industrial process development. Furthermore, the protrusion model proposed in this study serves as a valuable framework for optimizing bond design and grinding conditions for both current and next-generation semiconductor wafers. Full article

Background: Dry Eye Disease (DED) is a multifactorial ocular disorder characterized by tear film instability, inflammation, oxidative stress, and ocular surface damage. Current therapeutic options often provide only temporary relief and are limited by poor patient compliance and inadequate drug retention at the ocular surface. Aim: This review aims to critically analyze the therapeutic potential of polyphenols and their nanoencapsulated formulations for the management of DED, focusing on pharmacological mechanisms, formulation strategies, and translational implications. Methods: A comprehensive literature search was conducted in PubMed, Scopus, and Web of Science databases using combinations of the following keywords: “dry eye disease,” “polyphenols,” “antioxidants,” “nanocarriers,” “ocular delivery,” and “bioavailability.” Studies published in English from 2000 to 2024 were considered. Inclusion criteria encompassed experimental, preclinical, and clinical studies evaluating polyphenol-based formulations for ocular application, while reviews without original data or studies unrelated to DED were excluded. Results: The analysis identified EGCG, curcumin, resveratrol, and quercetin as the most extensively investigated polyphenols, exhibiting antioxidant, anti-inflammatory, and cytoprotective activities through modulation of cytokines, reactive oxygen species, and immune signaling pathways. Nanoformulations such as lipid nanoparticles, micelles, and cyclodextrin complexes improved solubility, stability, ocular retention, and bioavailability, leading to enhanced therapeutic efficacy in preclinical DED models. Conclusions and Future Perspectives: Polyphenol-loaded nanocarriers represent a promising approach for improving the management of DED by enhancing local drug delivery and sustained release. However, further clinical studies are needed to assess long-term safety, scalability, and regulatory feasibility. Future research should focus on optimizing formulation reproducibility and exploring personalized nanotherapeutic strategies to overcome interindividual variability in treatment response. Full article

The seismic vulnerability of existing reinforced concrete (RC) buildings that constitute a large portion of the urban building stock has become a growing concern for urban safety. This situation was once again revealed by the massive destruction that occurred in RC structures following the 2023 Kahramanmaraş earthquakes. Particularly in buildings constructed before 1990 and without adequate engineering services, destruction and damage were much greater. In this paper, structural models were created with inadequate transverse reinforcement, low-strength concrete, and inadequate concrete cover thickness, which all play a critical role in the seismic performance of the buildings. Structural analyses were updated for high-performance concrete jacketing models, considering the deformation status obtained for each inadequate parameter. It has been determined that the high-performance concrete can significantly increase structural performance, especially significant increases in shear strength capacities without the need for transverse reinforcement. Full article

To accurately assess the seismic risk of bridges, this study systematically conducted probabilistic seismic hazard–fragility–risk assessments using a reinforced concrete continuous girder bridge as a case study. First, the CPSHA method from China’s fifth-generation seismic zoning framework was employed to calculate the Peak Ground Acceleration (PGA) with 2%, 10%, and 63% exceedance probabilities over 50 years as 171.16 gal, 98.10 gal, and 28.61 gal, respectively, classifying the site as being with 0.10 g zone (basic intensity VII). Second, by innovatively integrating the Response Surface Method with Monte Carlo simulation, the study efficiently quantified the coupled effects of structural parameter and ground motion uncertainties, a finite element model was established based on OpenSees, and the seismic fragility curves were plotted. Finally, the risk probability of seismic damage was calculated based on the seismic hazard curve method. The results demonstrate that the study area encompasses 46 potential seismic sources according to China’s fifth-generation zoning. The seismic fragility curves clearly show that side piers and their bearings are generally more susceptible to damage than middle piers and their bearings. Over 50 years, the pier risk probabilities for the intact, slight, moderate, severe damage, and collapse are 68.90%, 6.22%, 15.75%, 7.86%, and 1.27%, while the corresponding probabilities of bearing are 3.54%, 44.11%, 25.64%, 7.74%, and 18.97%, indicating significantly higher bearing risks at the moderate damage and collapse levels. The method proposed in this study is applicable to various types of bridges and has high promotion and application value. Full article

The automated inspection of civil infrastructure with Unmanned Aerial Vehicles (UAVs) is hampered by the challenge of accurately segmenting multi-damage in high-resolution imagery. While foundational models like the Segment Anything Model (SAM) offer data-efficient segmentation, their effectiveness is constrained by prompting strategies, especially for geometrically complex defects. This paper presents a comprehensive comparative analysis of deep learning strategies to identify an optimal deep learning pipeline for segmenting cracks, efflorescences, and exposed rebars. It systematically evaluates three distinct end-to-end segmentation frameworks: the native output of a YOLO11 model; the Segment Anything Model (SAM), prompted by bounding boxes; and SAM, guided by a point-prompting mechanism derived from the detector’s probability map. Based on these findings, a final, optimized hybrid pipeline is proposed: for linear cracks, the native segmentation output of the SAHI-trained YOLO model is used, while for efflorescence and exposed rebar, the model’s bounding boxes are used to prompt SAM for a refined segmentation. This class-specific strategy yielded a final mean Average Precision (mAP50) of 0.593, with class-specific Intersection over Union (IoU) scores of 0.495 (cracks), 0.331 (efflorescence), and 0.205 (exposed rebar). The results establish that the future of automated inspection lies in intelligent frameworks that leverage the respective strengths of specialized detectors and powerful foundation models in a context-aware manner. Full article

To accurately evaluate the health condition of the cables of a cross-sea cable-stayed bridge under typhoon effects and to improve the efficiency of damage identification, an accurate bridge damage identification method combining convolutional neural network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) is proposed. A numerical model of the cable-stayed bridge was first established in ANSYS. Based on the monitoring data of Super Typhoon Mujigae, a three-dimensional fluctuating wind field was generated by harmonic synthesis. Through transient analysis, the static and dynamic responses of the cable-stayed bridge under typhoon loads were analyzed, and the critical cable locations most susceptible to damage were identified. Subsequently, the acceleration signals of the structural damage states under typhoon were extracted, and the damage-sensitive features were obtained through the Hilbert transform. Finally, an intelligent damage identification model for cable-stayed bridges was established by combining CNN and BiLSTM, and the identification results were compared with those obtained using CNN and BiLSTM individually. The results indicate that the neural network model combining CNN and BiLSTM performs significantly better than either CNN or BiLSTM alone in predicting both the location and degree of damage. Compared with the standalone CNN and BiLSTM models, the proposed hybrid CNN–BiLSTM network improves the accuracy of damage-location identification by 1.6% and 2.42%, respectively, and achieves an overall damage-degree identification accuracy exceeding 98%. The findings of this study provide theoretical and practical support for the intelligent operation and maintenance of cable-stayed bridges in coastal regions. The proposed approach is expected to serve as a valuable reference for evaluating large-span bridge structures under extreme wind conditions. Full article

Concrete slabs in composite bridges are inevitably subjected to heavy vehicular loads during their service life. To evaluate the fatigue performance of the prestressed concrete slabs in composite bridges, two full-scaled models of prestressed concrete slabs were first designed and tested, with the load amplitude was selected as the variable. To simulate the damage caused by the initial passage of heavy vehicles, this was simplified into the form of a static cyclic load. The mechanical deformation state and crack distribution of the slab were analyzed. Further, a finite-element model was established, and a parametric analysis based on the variation in loading form, such as monotonic displacement loading, static cyclic loading followed by monotonic displacement loading, and cyclic displacement loading, was conducted to discuss the performance-enhancement mechanism of prestressed concrete slabs. Finally, in consideration of the influence of static cyclic damage on the fatigue performance of prestressed concrete slabs, evaluation parameters were proposed to account for static cyclic damage by considering the effects of stresses in concrete, tensile rebar, prestressed tendons, and external loading. A comprehensive fatigue performance evaluation method for prestressed concrete slabs, which neglects the tensile hardening behavior of cracked concrete in the tension zone, was established and verified by test results. The results indicate that the damage caused by static cyclic loading has a significant influence on the fatigue performance of the slab. Applying prestress can significantly mitigate the influence of initial damage on the mechanical and deformation behavior of the slab, which benefits from the prestress compensating for the cracking stress at the bottom of the slab. The proposed fatigue performance-evaluation method for prestressed concrete slabs, which considers static cyclic damage, can predict fatigue deformation behavior with an error of less than 10%, while reasonably determining the fatigue life and failure modes of prestressed concrete slabs. The parametric analysis reveals that when the prestress value exceeds 9 MPa, the failure mode of the prestressed concrete slab transfers from rebar fracture to concrete failure. Full article