Search Results (16,255)

This study evaluates the performance of asphalt concrete incorporating steel slag aggregates coated with recycled polyethylene terephthalate (PET). The aim was to enhance adhesion between aggregate and binder while addressing environmental concerns related to waste management. Laboratory testing was carried out to assess Marshall stability, indirect tensile strength, and tensile strength ratio, which are commonly used indicators of strength and moisture resistance in asphalt mixtures. The results showed that PET coating enhanced binder-aggregate bonding, resulting in higher stability, which indicates an improved resistance to plastic deformation and moisture damage compared to uncoated slag mixtures. Among the tested combinations, the mixes containing 20% slag with 10% PET and 30% slag with 15% PET demonstrated the most balanced performance. These mixes achieved greater durability while maintaining satisfactory strength values, indicating that PET-coated slag can serve as an effective partial replacement for natural aggregates in asphalt concrete. The study also highlights that the approach can help reduce reliance on natural stone, lower construction costs, and promote recycling of industrial byproducts and plastic waste. This contributes to more sustainable pavement practices while addressing issues of waste disposal and environmental degradation. The findings suggest that PET-coated steel slag can be considered a practical and resource-efficient material for asphalt mixtures. The research not only adds technical evidence to the growing interest in waste-based construction materials but also provides guidance for adopting such methods in developing countries, where cost and sustainability are critical factors. Full article

Repairing large bone defects remains a significant clinical challenge due to the limitations of current treatments, including infection risk, donor site morbidity, and insufficient vascularization. The autograft is still the gold standard for large bone defects. In this study, we developed chitosan-based (CS-based) scaffolds, incorporating with hydroxyapatite (HAp) and fluorapatite (FAp) ceramics, fabricated by UV crosslinking and freeze-drying, and loaded with P28 peptide, alone or in combination with vascular endothelial growth factor (VEGF), to evaluate the effect of dual bioactive factor delivery. We hypothesized that CS-based scaffolds would optimize ceramic composition and co-delivery of P28 and VEGF, and can enhance early-stage osteogenic differentiation and support bone regeneration. The CS-based scaffolds were characterized by their physicochemical properties, including swelling behavior, mechanical strength, porosity, and in vitro degradation. Biological evaluations were performed including cell proliferation assays, ALP activity, ARS staining, and RT-qPCR, to assess osteogenic differentiation. The results showed that the scaffolds had high porosity, excellent swelling behavior, and degraded within 8 weeks. Dual delivery of P28 and VEGF significantly enhanced early osteogenic markers, indicating a complementary effect. These findings demonstrated that CS-based scaffolds with an optimized ceramic ratio and bioactive factor incorporation have the potential to facilitate bone regeneration. Full article

The wastewater discharged from the aquaculture and textile industries often contains toxic organic dyes, such as methylene blue (MB) and malachite green (MG), which pose significant risk to public health and ecosystem stability due to their high chemical stability, bioaccumulation potential and resistance to degradation. To address these challenges, the development of an integrated system capable of both efficient degradation and highly sensitive detection of organic dyes is essential for ecological restoration and early pollution monitoring. Herein, bifunctional Fe₃O₄-Cu₂O-Ag-GO (FCA 2-GO) nanocomposites (NCs) were developed by depositing Cu₂O, Ag nanocrystals and graphene oxide (GO) onto the surfaces of Fe₃O₄ nanocrystals. This multifunctional material acted as both a photocatalyst and a surface-enhanced Raman scattering (SERS) platform, enabling simultaneous degradation and ultrasensitive detection of organic dyes. Under simulated sunlight irradiation, FCA 2-GO NCs achieved over 98% degradation of both MB and MG within 60 min, driven by the synergistic action of reactive oxygen species (·O₂⁻ and ·OH). The degradation kinetics followed pseudo-first-order behavior, with rate constants of 0.0381 min⁻¹ (MB) and 0.0310 min⁻¹ (MG). Additionally, the FCA 2-GO NCs exhibited exceptional SERS performance, achieving detection limits as low as 10⁻¹² M for both dyes, attributed to electromagnetic–chemical dual-enhancement mechanisms. Practical applicability was demonstrated in soil matrices, showcasing robust linear correlations (R² > 0.95) between SERS signal intensity and dye concentration. This work provides a dual-functional platform that combines efficient environmental remediation with trace-level pollutant monitoring, offering a promising strategy for sustainable wastewater treatment and environmental safety. Full article

To address the limitations of CNNs and RNNs in handling complex operating conditions, multi-scale degradation patterns, and long-term dependencies—with attention mechanisms often failing to highlight key degradation features—this paper proposes a remaining useful life (RUL) prediction framework based on a multi-scale dilated fusion attention (MDFA) module. The MDFA leverages parallel dilated convolutions with varying dilation rates to expand receptive fields, while a global-pooling branch captures sequence-level degradation trends. Additionally, integrated channel and spatial attention mechanisms enhance the model’s ability to emphasize informative features and suppress noise, thereby improving overall prediction robustness. The proposed method is evaluated on NASA’s C-MAPSS and N-CMAPSS datasets, achieving MAE values of 0.018–0.026, RMSE values of 0.021–0.032, and R² scores above 0.987, demonstrating superior accuracy and stability compared to existing baselines. Furthermore, to verify generalization across domains, experiments on the PHM2012 bearing dataset show similar performance (MAE: 0.023–0.026, RMSE: 0.031–0.032, R²: 0.987–0.995), confirming the model’s effectiveness under diverse operating conditions and its adaptability to different degradation behaviors. This study provides a practical and interpretable deep-learning solution for RUL prediction, with broad applicability to aero-engine prognostics and other industrial health-monitoring tasks. Full article

Sparse Matrix–Vector Multiplication (SpMV) is a critical computational kernel in high-performance computing (HPC) and artificial intelligence (AI). However, its irregular memory access patterns lead to frequent cache misses on multi-level cache hierarchies, significantly degrading performance. Scratchpad memory (SPM), a software-managed, low-latency on-chip memory, offers improved data locality and control, making it a promising alternative for irregular workloads. To enhance SpMV performance, we propose a vectorized execution framework targeting SPM-augmented processors. Recognizing the limitations of traditional formats for vectorization, we introduce Sorted-Row-Block ELL (SRB-ELL), a new matrix storage format derived from ELLPACK (ELL). SRB-ELL stores only non-zero elements, partitions the matrix into row blocks, and sorts them by block size to improve load balance and SIMD efficiency. We implement and evaluate SRB-ELL on a custom processor architecture with integrated SPM using the gem5 simulator. Experimental results show that, compared to vectorized CSR-based SpMV, the SRB-ELL design achieves up to 1.48× speedup and an average of 1.19×. Full article

Substantial time-dependent deformation and support failure in deep tunnels through water-rich red-bed soft rock present critical engineering challenges, yet the underlying mechanisms under hydro-mechanical coupling remain inadequately quantified. This study integrates wireless remote monitoring, laboratory testing, and theoretical analysis to investigate the stress-deformation behavior of surrounding rock and support structures. Results reveal that deformation evolves through four distinct stages as follows: sharp, slow, stable, and creep, with the creep stage—governed by pore-water pressure—accounting for over 40% of total displacement. Groundwater-induced clay mineral hydration and stress redistribution significantly weaken rock self-support capacity. Support elements exhibit degraded performance; rock bolts suffer interfacial bond failure, steel arches yield asymmetrically, and the secondary lining resists transmitted deformation pressure. A novel deformation rate-based failure criterion is proposed, revealing a progressive “local breakthrough-chain transmission–global instability” failure pathway. These findings provide a theoretical basis for stability control in deep buried tunnels under hydro-mechanical coupling. Full article

Perovskite nanocrystals (PeNCs) have attracted considerable interest as promising materials for next-generation optoelectronic devices owing to their high photoluminescence quantum yield, narrow emission linewidths, simple composition tunability, and solution processability. However, the practical applicability of these NCs is limited by their compositional, thermal, and environmental instabilities, which compromise their long-term operational performance and reliability. Compositional instability arises from ion migration and phase segregation, leading to spectral shifts and unstable emission. Thermal degradation is driven by volatile organic cations and weak surface bonding, while environmental factors such as moisture, oxygen, and ultraviolet irradiation promote defect formation and material degradation. This review describes the recent advances in improving the photoluminescent stability of PeNCs through compositional engineering (A-/B-site substitution), ligand engineering (X-/L-type modulation), and surface passivation strategies. These approaches effectively suppress degradation pathways while maintaining or improving the optical properties of PeNCs. By performing a comparative analysis of these strategies, this review provides guidelines for the rational design of stable and efficient PeNCs for light-emitting applications. Full article

This study examines the effects and mechanisms of different Enzyme-Induced Carbonate Precipitation (EICP) treatments on loess structure improvement. The study focuses on ordinary EICP and three modified methods using MgCl₂, NH₄Cl, and CaCl₂. A series of unconfined compressive strength (UCS) tests, scanning electron microscopy (SEM), energy-dispersive spectroscopy (EDS), and elemental mapping were used to assess both macroscopic performance and microscopic characteristics. The results indicate that ordinary EICP significantly enhances loess particle bonding by promoting calcite precipitation. MgCl₂-modified EICP achieves the highest UCS (820 kPa) due to delayed urea hydrolysis and the formation of aragonite alongside calcite, which results in stronger and more continuous cementation. In contrast, NH₄Cl reduces urease activity and reverses the reaction, which limits carbonate precipitation and weakens structural cohesion. Excessive CaCl₂ leads to a “hijacking mechanism” where hydroxide ions form Ca(OH)₂, restricting carbonate formation and diminishing the overall enhancement. This study highlights the mechanisms behind enhancement, degradation, and diversion in the EICP process. It also provides theoretical support for optimizing loess subgrade reinforcement. However, challenges such as uneven permeability, environmental variability, and long-term durability must be addressed before field-scale applications can be realized, necessitating further research. Full article

During deep and ultra-deep well drilling operations, the throttling performance of the hydraulic-while-drilling jar is significantly affected by the combined influence of temperature-induced differential thermal expansion among components and changes in the rheological properties of hydraulic oil. These effects often lead to unstable jarring behavior or even complete failure to trigger jarring during stuck pipe events. Here, we propose a high-temperature degradation evaluation model for the throttling performance of the throttle valve in an HWD jar based on thermal expansion testing of individual components and high-temperature rheological experiments of hydraulic oil. By using the variation characteristics of the throttling passage geometry as a linkage, this model integrates the thermo-mechanical coupling of the valve body with flow field simulation. Numerical results reveal that fluid pressure decreases progressively along the flow path through the throttle valve, while flow velocity increases sharply at the channel entrance and exhibits mild fluctuations within the throttling region. Under fluid compression, the throttling areas of both the upper and lower valves expand to some extent, with their spatial distributions closely following the pressure gradient and decreasing gradually along the flow direction. Compared with ambient conditions, thermal expansion under elevated temperatures causes a more pronounced increase in throttling area. Additionally, as hydraulic oil viscosity decreases with increasing temperature, flow velocities and mass flow rates rise significantly, leading to a marked deterioration in the throttling performance of the drilling jar under high-temperature downhole conditions. Full article

Three-dimensional measurement of translucent objects using structured light techniques remained fundamentally challenging due to severe degradation of fringe patterns caused by subsurface scattering, which inevitably introduced phase errors and compromised measurement accuracy. Although deep learning had emerged as a powerful tool for fringe analysis, its practical implementation was hindered by the impractical requirement for large-scale labeled datasets, particularly in scattering-dominant measurement scenarios. To overcome these limitations, we developed a self-learning-based fringe domain conversion method inspired by image style transfer principles, where degraded and ideal fringe patterns were treated as distinct domains for cyclic translation. The proposed framework employed dual generators and discriminators to establish cycle-consistency constraints while incorporating both numerical intensity-based and physical phase-derived optimization targets, effectively suppressing phase errors and improving fringe modulation without requiring paired training data. Experimental validation demonstrated superior performance in reconstructing high-fidelity 3D morphology of translucent objects, establishing this approach as a robust solution for precision metrology of complex scattering media. Full article

Detecting small moving objects under challenging lighting conditions, such as overexposure and underexposure, remains a critical challenge in computer vision applications including surveillance, autonomous driving, and anti-UAV systems. Traditional RGB-based detectors often suffer from degraded object visibility and highly dynamic illumination, leading to suboptimal performance. To address these limitations, we propose a novel RGB-Event fusion framework that leverages the complementary strengths of RGB and event modalities for enhanced small object detection. Specifically, we introduce a Temporal Multi-Scale Attention Fusion (TMAF) module to encode motion cues from event streams at multiple temporal scales, thereby enhancing the saliency of small object features. Furthermore, we design a Sparse Noisy Gated Attention Fusion (SNGAF) module, inspired by the mixture-of-experts paradigm, which employs a sparse gating mechanism to adaptively combine multiple fusion experts based on input characteristics, enabling flexible and robust RGB-Event feature integration. Additionally, we present RGBE-UAV, which is a new RGB-Event dataset tailored for small moving object detection under diverse exposure conditions. Extensive experiments on our RGBE-UAV and public DSEC-MOD datasets demonstrate that our method outperforms existing state-of-the-art RGB-Event fusion approaches, validating its effectiveness and generalization under complex lighting conditions. Full article

 Background: Micro- and nanoplastics (MNPs) have emerged as increasing environmental and public health concerns. Dentistry contributes to this exposure through polymer-based materials and personal oral care products. This review summarizes the current evidence on the sources, release mechanisms, physicochemical properties, and toxicological and biological effects of MNPs derived from dental sources and oral care products, as well as the synergistic effects of MNP oral exposure with environmental exposure. Methods: An electronic search was performed across the PubMed/MEDLINE, Scopus, and Web of Science databases to identify studies investigating the source, release mechanisms, physico/chemical properties, and toxicological/biological impact of MNPs related to dental materials, oral care products, and the synergic effects of MNPs oral and environmental exposure. Results: MNPs are released in the dental setting from resin-based composites, clear aligners, and prosthetic and impression materials through degradation, wear, and handling processes. Home-use products like toothpastes, toothbrushes, floss, and mouthwashes contribute to chronic oral exposure. Evidence from in vitro, in vivo, and human biomonitoring studies supports the biological activity and systemic distribution of MNPs. Despite this, clinical awareness remains limited, and regulatory oversight insufficient. Conclusions: Dentistry is both a source and vector of MNP exposure. Encouraging the use of safer, MNP-free materials, and raising awareness among dental professionals, may support more responsible and health-conscious practices. Further research and alignment with global policy strategies could help guide future innovation and risk mitigation in the dental field.  Full article

Unsaturated fatty acids are key contributors to the nutritional and sensory quality of lamb meat. To investigate the molecular basis of intramuscular unsaturated fatty acid variation, we selected lambs with divergent fatty acid profiles and performed integrated transcriptomic and untargeted metabolomic analyses of the longissimus dorsi muscle. The high unsaturated fatty acid group exhibited distinct gene expression patterns in pathways related to lipid metabolism, mitochondrial function, and immune responses. Metabolomic profiling revealed significant enrichment of metabolites involved in both the biosynthesis and degradation of fatty acids. Among the differentially expressed genes, MYH7 was markedly upregulated in lambs with higher unsaturated fatty acid content, suggesting a potential regulatory role in energy metabolism or lipid homeostasis. These findings provide new molecular insights into the mechanisms underlying unsaturated fatty acid deposition in lamb and identify MYH7 and other candidates as potential targets for improving meat quality through breeding or nutritional strategies. Full article

Terephthalic acid (TPA), a major monomer of polyethylene terephthalate (PET), represents a significant challenge in plastic waste management due to its persistence in the environment. In this study, we report a newly developed bacterial consortium capable of using TPA as the sole carbon source in a defined mineral medium. The consortium achieved stationary phase within five days and metabolized approximately 85% of the available TPA. Metabolite analysis by high-performance liquid chromatography (HPLC) and liquid chromatography tandem mass spectrometry (LC-MS/MS) revealed the activation of the benzoate degradation pathway during TPA catabolism. Additionally, the consortium secreted commercially relevant metabolites such as cis,cis-muconic acid and catechol into the culture medium. Genetic profiling using a reverse transcription quantitative polymerase chain reaction (RT-qPCR) and 16S rRNA sequencing identified Paraburkholderia fungorum as the dominant species, suggesting it plays a key role in TPA degradation. The ability of this microbial community to efficiently convert TPA into high-value by-products offers a promising and potentially economically sustainable approach to addressing plastic pollution. Full article

Wet cracks caused by leakage often exhibit visual and structural distortions due to surface contamination, salt crystallization, and corrosion byproducts. These factors significantly degrade the performance of sensor- and vision-based crack detection systems. In moist environments, the initiation and propagation of cracks tend to be highly nonlinear and irregular, making it challenging to distinguish crack regions from the background—especially under visual noise such as reflections, stains, and low contrast. To address these challenges, this study proposes a segmentation framework that integrates a dedicated preprocessing pipeline aimed at suppressing noise and enhancing feature clarity, all without altering the underlying segmentation architecture. The pipeline begins with adaptive thresholding to perform initial binarization under varying lighting conditions. This is followed by morphological operations and connected component analysis to eliminate micro-level noise and restore structural continuity of crack patterns. Subsequently, both local and global contrast are enhanced using histogram stretching and contrast limited adaptive histogram equalization. Finally, a background fusion step is applied to emphasize crack features while preserving the original surface texture. Experimental results demonstrate that the proposed method significantly improves segmentation performance under adverse conditions. Notably, it achieves a precision of 97.5% and exhibits strong robustness against noise introduced by moisture, reflections, and surface irregularities. These findings confirm that targeted preprocessing can substantially enhance the accuracy and reliability of crack detection systems deployed in real-world infrastructure inspection scenarios. Full article