Search Results (11,569)

Polyetheretherketone (PEEK), a high-performance thermoplastic, is utilized in bone tissue engineering due to its elastic modulus resembling that of human cortical bone. However, toxicological studies on PEEK remain limited. PEEK disrupts bone homeostasis by recruiting macrophages and inducing the aggregation of foreign body multinucleated giant cells, ultimately leading to bone resorption. The lack of effective therapeutic approaches underscores the importance of identifying novel treatments. This study systematically investigated the potential molecular mechanisms underlying PEEK-induced bone resorption using network toxicology, molecular docking techniques, and molecular dynamics simulations. We first conducted a network-based toxicological assessment based on the molecular structure of PEEK. By integrating and screening targets from multiple databases, we identified 139 potential targets associated with PEEK-induced bone resorption and constructed an interaction network diagram of these targets. Gene Ontology (GO)/KEGG enrichment analysis revealed that PEEK may induce bone resorption through pathways such as the PI3K-AKT signaling pathway and TNF signaling pathway. Further analysis using STRING and Cytoscape 3.9.0 software identified 53 core targets, including MAPK3, TNF, IL-6, AKT1, IL-1β, EGFR, and MMP9. We found that enriched highly correlated pathways encompassed core targets, supporting the scientific hypothesis that PEEK induces bone resorption. Furthermore, molecular docking and molecular dynamics simulation results confirmed that PEEK exhibits strong binding affinity with core targets, forming stable complexes. In summary, this study not only reveals the potential biological mechanisms underlying PEEK-induced bone resorption but also provides new evidence for future prevention and treatment of PEEK-induced bone imbalance. Full article

To address the challenge of accurately reconstructing multi-depth targets using single-photon Light Detection and Ranging (LiDAR) under few-frame conditions in high-precision applications such as autonomous driving perception, remote sensing, and military reconnaissance, this paper proposes a computational imaging method named the Jensen–Shannon Divergence Weighted Pixel Fusion Constant False Alarm Rate (JSWPF-CFAR) approach. First, the proposed method utilizes the Jensen–Shannon (JS) divergence to characterize the statistical similarity between adjacent pixels, thereby constructing adaptive weights to achieve the effective fusion of echo signals. The key innovation lies in the formulation of a JS divergence-based weighting factor, which fully exploits the inherent spatial correlation within 3D target structures to optimize the pixel fusion process and enhance the signal statistics of target echoes. Subsequently, a CFAR detection model tailored for Geiger-mode Avalanche Photodiode (GM-APD) multi-depth echo signals is constructed to estimate the noise photon count within a local sliding window; this estimate is then used to calculate a photon counting threshold for identifying and extracting high-confidence target intervals. Finally, a peak-picking method is employed to perform the 3D reconstruction of multi-depth targets. Compared with existing techniques such as matched filtering and Reversible Jump Markov Chain Monte Carlo (RJMCMC), the proposed method exhibits superior reconstruction quality under few-frame and low Signal-to-Background Ratio (SBR) conditions. The experimental results demonstrate that the proposed method achieves an improvement in target restoration degree (RD) of at least 21.16% and a relative variance (Var) optimization of at least 62.90% over the matched filtering and RJMCMC baselines. These results indicate that the proposed approach effectively enhances the multi-depth estimation performance of single-photon LiDAR in complex scenes. Full article

The rapid growth of Earth science observation and simulation data has made efficient data classification increasingly challenging, particularly under conditions of limited annotation resources and continuously evolving data semantics. Conventional classification methods rely heavily on large-scale labeled datasets, which are costly to construct and difficult to adapt to dynamic classification systems. This paper proposes a hierarchical classification framework for Earth science data that leverages large language models (LLMs) and explicitly incorporates hierarchical label relationships to constrain model inference and enhance classification consistency across complex, domain-specific semantic spaces. The framework further integrates retrieval-augmented generation (RAG) and knowledge graph (KG) techniques to introduce external domain knowledge and explicit semantic constraints, enhancing contextual understanding, interpretability, and adaptability to semantic evolution. A benchmark dataset with a two-level hierarchical label structure is constructed based on official NASA metadata. Experimental results demonstrate that by integrating few-shot learning and label space optimization strategies, the proposed framework steadily outperforms various baseline methods in hierarchical classification tasks. Compared with the Bert-BiLSTM model, it achieves an absolute improvement of 8.68% in Micro-F1 and 29.92% in Macro-F1 on the overall hierarchical paths. The framework demonstrates clear advantages in long-tailed data distributions, particularly for minority classes, highlighting its potential for scalable annotation and efficient management of large-scale Earth science datasets. Full article

This study examines the kalif structure, a unique and increasingly invisible component of the rural architecture in the Eastern Black Sea region that is currently under threat of extinction, along with the tradition of kalif-guarding integrated with this structure. Historically constructed to protect agricultural production from wildlife, kalifs are not merely functional shelters but also multi-layered memory objects where collective solidarity and social interaction are reproduced. A qualitative research method was adopted for the study, utilizing literature review, on-site physical documentation, and technical analysis centered on Yücehisar village in the Pazar district of Rize. Within the scope of the research, the material use and construction techniques of kalifs are detailed from an architectural perspective, and these practices are evaluated through the lens of Intangible Cultural Heritage. The findings indicate that the loss of the physical presence of kalifs due to the transition from corn to tea cultivation and rural migration signifies the dissolution of a production-based culture of living. Consequently, the study reveals the critical importance of incorporating the kalif and the act of kalif-guarding into academic literature and cultural memory within the framework of Intangible Cultural Heritage standards to preserve local identity and rural memory. Full article

Cement–soil mixing piles commonly face the problem of insufficient pile quality during on-site construction, and traditional measures such as increasing grouting pressure or enhancing mixing intensity are difficult to resolve effectively. The development of flowable solidified soil technology offers a new path for innovating soil pile reinforcement techniques. Based on an in situ test, this research proposes and introduces a new technology for pre-mixed fluid-solidified soil piles (PSPs). This technique effectively improves pile quality and significantly enhances pile bearing capacity by pre-mixing flowable solidified soil and then grouting it after pre-drilling holes with a screw drill. The results show that reinforcement of soil piles using the pre-mixed flowable solidified soil and pre-drilled grouting process has significantly improved pile quality, with better core sample integrity and uniformity. The results indicate that the characteristic bearing capacity of the uniform-section PSP is 252 kPa, meeting the design requirement of 130 kPa. The ultimate bearing capacity of the uniform-section PSP is 177% higher than that of the uniform-section CMP. In addition, the ultimate bearing capacity of the PSP after variable-section treatment is 153% higher than that of the uniform-section PSP. Finally, new design recommendations have been proposed, specifically calculation formulas for the load-bearing capacity and settlement of composite foundations based on current standards. Full article

This study focuses on 28 mining cities with the aim of promoting their sustainable development, particularly with regard to disaster resilience. The entropy-weight Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) model is adopted to measure comprehensive disaster reduction capacity, and spatial analysis/econometric models are used to reveal its spatial distribution pattern, correlation characteristics, and driving mechanism. The region’s comprehensive disaster reduction capacity is generally higher in the west and north and lower in the east and south. Significant differences are observed among cities with obvious spatial agglomeration characteristics, and both high- and low-value areas show a contiguous spatial structure. Economic development and disaster prevention infrastructure construction are the main factors driving the spatial differentiation of disaster reduction capacity. Geological disaster risk exerts a significant negative effect, and various regions exhibit stable positive spatial spillover. These results provide a scientific basis for formulating differentiated disaster reduction strategies and will facilitate the sustainable development of disaster-prone regions. Full article

Background: Achilles tendon re-rupture following operative repair remains a challenging complication, and biomechanical evidence guiding revision strategies is limited. The mechanical behavior of commonly used revision constructs has not been well characterized. The objective of this exploratory study was to provide a descriptive biomechanical characterization of commonly used Achilles tendon revision constructs, focusing on viscoelastic behavior, load-to-failure properties, and failure mechanisms under standardized loading conditions. Although limited by the absence of construct replication, this study provides hypothesis-generating biomechanical insight into the failure mechanisms of revision constructs, which may inform future comparative studies and surgical strategy selection. Methods: Four fresh-frozen human cadaveric lower limbs underwent standardized Achilles tendon transection with segmental excision to simulate revision conditions. Five revision techniques were evaluated: tensioned cross-lock Bunnell, Krakow, posterior tibial tendon (PTT) augmentation with Bunnell repair, double Kessler with circumferential running suture, and V–Y advancement combined with three simple sutures and double Kessler. All repairs were performed using No. 2 high-strength suturing by a single surgeon. Constructs underwent stress relaxation testing under a constant 100 N load followed by uniaxial load-to-failure testing. Mechanical parameters and failure modes were recorded. Results: All constructs demonstrated time-dependent stress relaxation. The tensioned cross-lock Bunnell repair retained the highest residual force during sustained loading. The PTT-augmented construct exhibited the highest load to failure among the constructs tested and failed at the tendon substance, whereas non-augmented repairs failed predominantly at the suture–tendon interface. The V–Y advancement construct failed at relatively low applied loads under the applied testing protocol. Conclusions: Achilles tendon revision constructs demonstrate distinct biomechanical behaviors. Augmented constructs exhibited higher resistance to tensile loading in this experimental setting and shifted failure away from the repair site, while non-augmented repairs were limited by suture–tendon interface strength. Given that each construct was tested only once and that one specimen was used sequentially for two repairs, the findings should be interpreted strictly as descriptive and hypothesis-generating, without any basis for comparative or inferential conclusions. Full article

Bamboo, as a rapidly renewable and sustainable material, has gained increasing attention in the construction, furniture, automotive interiors, and packaging industries due to its excellent mechanical properties, light weight, and environmental friendliness. However, the inherent flammability of bamboo, characterized by its porous structure and high hemicellulose content, poses a significant fire hazard that severely limits its wide application. This review systematically synthesizes recent advances in the fire performance and flame-retardant modification of bamboo-based materials. First, the thermal degradation behavior and combustion mechanisms of bamboo are discussed in relation to its primary chemical constituents, including cellulose, hemicellulose, and lignin. Subsequently, various flame-retardant strategies are reviewed, including inorganic flame retardants, phosphorus–nitrogen systems, nanomaterial-based additives, and bio-based flame-retardant approaches. The effectiveness of different modification techniques, such as impregnation treatment, adhesive modification, and surface coating, is also analyzed. Future research directions are proposed, emphasizing the development of environmentally friendly flame-retardant systems, multifunctional modification strategies, and the design of high-performance flame-retardant bamboo-based materials. This review aims to provide a comprehensive framework for advancing the fire safety design and sustainable application of bamboo-based materials. Full article

The transformative role of Digital Twins (DTs) in the Architecture, Engineering, and Construction (AEC) sector lies in their capacity to generate dynamic, data-driven representations of physical assets that support design, construction, and lifecycle management. To achieve their full potential, DTs must integrate accurate geometric models with continuously updated information reflecting real-world conditions. This information is inherently multidisciplinary and heterogeneous, encompassing structural, environmental, operational, and monitoring data characterized by different spatial and temporal scales. Integrating these diverse datasets into a unified DT environment presents significant challenges related to data heterogeneity, interoperability, varying resolutions, data quality, and uncertainty. This paper presents a PRISMA-based systematic literature review of data fusion techniques applied to DTs within the AEC sector, with particular emphasis on geotechnical and underground infrastructure. A Scopus search conducted on 31 March 2026 retrieved 10,124 records. After sequential screening, 1916 geotechnical-related records were retained for quantitative characterization, 719 records were assessed for eligibility, 454 reports were retained for manual assessment, and 82 studies were finally included in the detailed qualitative review. Existing approaches are classified according to their integration paradigms, methodological foundations, and application domains. Particular attention is given to applications in Geotechnical Engineering, where DTs must integrate sparse, indirect, and highly uncertain subsurface data. Geological conditions are characterized by strong spatial variability, limited observability, material heterogeneity, and epistemic uncertainty, which introduce additional complexities for data fusion compared to surface infrastructure systems. By synthesizing current developments and identifying methodological trends and research gaps, this review provides a structured framework to support the selection and adaptation of data fusion strategies for geotechnical DTs and other complex AEC applications operating under high uncertainty. Full article

Stabilizing weak soils is a well-known pavement and geotechnical engineering technique. This technique involves introducing minimal cementitious materials to improve the soil’s geotechnical characteristics. This paper investigates the use of recycled industrial polymer waste (IPW) as a reinforcement material in the presence of cementitious binders to stabilize weak silty sand soil (SM), supporting sustainable engineering practices. The randomly distributed IPW were added as percentages of 0%, 5%, and 10% to a mixture of lime soil and cement soil, with varying amounts of 0% to 6% of lime (L) and 0% to 6% of ordinary Portland cement (OPC), respectively. The laboratory experiments were conducted on natural and stabilized samples in wet (unsoaked) and submerged (soaked) conditions. The experimental program included Proctor compaction, California bearing ratio (CBR), unconfined compressive strength (UCS), durability tests, scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), and X-ray diffraction analyses. The resilient modulus (Mr) was estimated using an empirical equation. The outcomes of this experimental study show that adding a combination of IPW shreds with a small amount of L and/or OPC to the SM soil provides a significant increase in the UCS, CBR, durability and Mr values compared with case of SM with only L, which allows for superior characteristics and increases strength and stiffness parameters throughout any phase of earthwork construction design, resulting in stronger and stiffer subgrades. These results were reinforced by microstructural observations from SEM, EDS, and DRX, confirming the formation of cementitious gels and chemical compounds, consistent with the macro-scale mechanical improvements. The expected practical outcomes include potential reductions in pavement thickness, which can help lower pavement stabilization costs and extend its service life. Additionally, the use of waste materials to replace raw materials contributes to decreased energy consumption and emissions, although detailed assessments are needed to quantify these effects. Full article

Fuzzing is an important automated testing technique for discovering vulnerabilities and abnormal software behavior, but conventional and pre-LLM learning-based approaches often struggle with strict validity constraints, limited semantic understanding, and poor adaptability across targets. Recent advances in large language models (LLMs) create new opportunities for fuzzing by supplying semantic guidance from source code, documentation, traces, and execution feedback. As the literature on LLM-based fuzzing has grown rapidly, a systematic synthesis is needed. This paper presents a literature review on the integration of LLMs into fuzzing workflows. We review how LLMs are applied across the fuzzing workflow, compare these approaches with earlier deep learning-based fuzzing, and summarize the main patterns in task coverage, application strategies, targets, testing modes, and model choices. The survey shows that current work is concentrated in test case generation, while defect detection and post-processing remain less represented, and broader workflow integration is uneven. Based on these findings, we identify four main directions for future research as follows: broader coverage of the fuzzing workflow, stronger context construction and workflow engineering, richer reasoning for fuzzing control, and shared evaluation standards and benchmarks. Full article

Direct current (DC) surface flashover on polystyrene (PS) remains a critical bottleneck that impedes its reliable application in high-voltage insulation apparatus. To circumvent the protracted processing durations and stringent film-forming conditions inherent in conventional surface modification techniques, this study proposes a novel “liquid-film-assisted in situ rapid plasma curing” strategy. By harnessing atmospheric-pressure dielectric barrier discharge (DBD) technology within an argon ambient, the rapid (<6 min) and efficient deposition of a fluorosilane (FAS-13) functional coating onto the substrate was achieved. Microscopic characterizations coupled with isothermal surface potential decay (SPD) measurements reveal that this coating substantially mitigates the detrapping and surface migration of charge carriers. Macroscopic DC flashover testing corroborates that, under the optimal modification ratio, the surface breakdown voltage of PS is elevated to 14.04 kV, yielding an insulation gain of 26.94%. To elucidate the underlying physical mechanisms, density functional theory (DFT) calculations were conducted, revealing that the energy band misalignment between the wide-bandgap fluorinated layer and the substrate facilitates the construction of a high-density deep trap network (with a depth of ~0.8 eV) at the coating–substrate interface. By robustly anchoring primary electrons and inducing the formation of a homopolar space charge shielding layer, these deep traps physically arrest the evolution of the secondary electron emission avalanche (SEEA). Consequently, this work not only establishes a viable engineering framework for the rapid, large-scale surface reinforcement of DC insulation equipment but also provides profound quantum chemical insights into interfacial trap regulation within all-organic dielectrics. Full article

Stereo radargrammetry using Synthetic Aperture Radar (SAR) images is a powerful technique for all-weather 3D topographic measurements. However, conventional methods based on local template matching often struggle to establish accurate correspondences in mountainous or vegetated areas due to severe SAR-specific geometric modulations. In this paper, we propose a novel high-accuracy stereo radargrammetry framework by introducing RoMa, a robust Transformer-based deep learning model, for dense SAR image matching. Optical pre-trained deep learning models often suffer from a domain gap. To overcome this limitation, we develop an automated pipeline to construct a patch-based SAR image dataset using a reference Digital Surface Model (DSM) and an SAR projection model. By fine-tuning RoMa on this dataset, the model effectively adapts to the complex non-linear deformations of SAR images. Furthermore, unlike conventional methods, our approach establishes correspondences directly on the original slant-range images without requiring ground-range projection, thereby avoiding image quality degradation caused by pixel interpolation. Experimental results using airborne Pi-SAR2 images demonstrate that the fine-tuned RoMa significantly outperforms conventional methods, achieving an 82.86% matching accuracy at a 10-pixel threshold. In the 3D measurement evaluation, the proposed method achieves the lowest elevation mean error ($-1.24$ m) and the highest inlier ratio (74.1%), proving its effectiveness in generating accurate, dense, and wide-area 3D point clouds even in challenging terrains. Full article

The present paper proposes a methodology for the analysis and modelling of transient events in a data center based on real-world high-resolution voltage and current measurements. The proposed approach includes the identification of relevant events, temporal segmentation, multivariate representation, and the application of dimensionality reduction techniques to obtain compact representations of the observed dynamics. A total of eight representative transient events were identified in the available dataset. These events were characterized by short-duration disturbances of moderate magnitude, which is consistent with the operation of highly reliable infrastructures. Three main methods were evaluated: PCA/POD, Kernel PCA, and Autoencoder. The results show that all three approaches are capable of reconstructing the event dynamics with low reconstruction errors, suggesting the presence of a low-dimensional structure in the analyzed data. Among the evaluated methods, PCA/POD provided the best balance between compactness, interpretability, and computational efficiency, while Kernel PCA and Autoencoder offered advantages for representing nonlinear behaviors. The results provide case-study evidence on the feasibility of constructing reduced-order representations for the analysis and monitoring of transient events in data centers under limited-data conditions. Full article

The vertical jacking method is a trenchless construction technique in which standpipes are jacked upward from a horizontal main pipeline through overlying soil, and it is widely applied in marine outfall projects. Taking a deep-sea tailwater outfall in Ningbo as the case study, this research develops a three-dimensional finite element model using ANSYS 2021 R1 based on actual geological and construction conditions. The settlement and stress responses of the horizontal main pipeline during jacking are analyzed and validated against field monitoring data. The numerical results agree well with the field measurements, with an average error of 7.29% for settlement and less than 9.83% for circumferential stress. The main pipeline exhibits a settlement pattern characterized by larger deformation in the middle and smaller at both ends, with a maximum value of 0.90 mm, decreasing with distance from the opening ring. Circumferential stress initially increases and then stabilizes, with maximum tensile and compressive stresses of 0.082 MPa and 0.056 MPa, respectively. Overall stress levels remain well below structural strength, indicating that structural damage is unlikely under the investigated conditions. These findings provide a case-based reference for structural design, construction control, and monitoring layout in marine outfall projects with similar geological and construction conditions. Full article