Search Results (397)

Microbiologically influenced corrosion induced by sulfate-reducing bacteria (SRB) poses a significant threat to shale gas pipeline integrity. This study investigates an integrated control strategy combining the biocide glutaraldehyde with denitrifying bacteria (DNB) to synergistically inhibit SRB activity and corrosion. The efficacy and mechanisms were systematically evaluated using electrochemical measurements (EIS, LPR), weight-loss analysis, surface characterization (SEM, maximum pit depth), and microbial community profiling (16S rDNA sequencing). Compared to the SRB-inoculated system, the combined treatment reduced the average corrosion rate of L245 steel by 44.2% (to 0.01608 mm/a) and the maximum pit depth by 84.3% (to 1.53 μm). EIS results further confirmed the superior inhibition effect, showing the largest capacitive arc diameter and the highest polarization resistance in the combined system. Microbial community analysis indicated a substantial decline in SRB abundance from 62.7% (day 1) to 11.9% (day 14). This synergistic strategy presents an effective and more sustainable approach by reducing chemical dosage and leveraging the bio-competitive exclusion by DNB. Full article

Forecasting the spatio-temporal evolutions of cellular traffic is crucial for urban management. However, achieving accurate forecasting is challenging due to “complex correlation modeling” and “model-blindness” issues. Specifically, cellular traffic is generated within complex urban systems characterized by an intricate structure and human mobility. Existing approaches, often based on proximity or attributes, struggle to learn the latent correlation matrix governing traffic evolution, which limits forecasting accuracy. Furthermore, while substantial knowledge about urban systems can supplement the modeling of correlations, existing methods for integrating this knowledge—typically via loss functions or embeddings—overlook the synergistic collaboration between data and knowledge, resulting in weak model robustness. To address these challenges, we develop a data–knowledge collaborative learning framework termed the knowledge-empowered spatio-temporal neural network (KESTNN). This framework first extracts knowledge triplets representing urban structures to construct a knowledge graph. Representation learning is then conducted to learn the correlation matrix. Throughout this process, data and knowledge are integrated collaboratively via backpropagation, contrasting with the forward feature injection methods typical of existing approaches. This mechanism ensures that data and knowledge directly guide the dynamic updating of model parameters through backpropagation, rather than merely serving as a static feature prompt, thereby fundamentally alleviating the “model-blindness” issue. Finally, the optimized matrix is embedded into a forecasting module. Experiments on the Milan dataset demonstrate that the KESTNN exhibits excellent forecast performance, reducing RMSE by up to 23.91%, 16.73%, and 10.40% for 3-, 6-, and 9-step forecasts, respectively, compared to the best baseline. Full article

Corrosion in harsh environments causes global economic losses exceeding 3 trillion US dollars annually. Traditional silicone epoxy (SE) coatings are prone to failure due to insufficient physical barrier properties and lack of active protection. In this study, cerium dioxide (CeO₂) was in situ grown on the surface of hexagonal boron nitride (h-BN) mediated by polydopamine (PDA) to prepare BN-PDA-CeO₂ ternary nanocomposites, which were then incorporated into SE coatings to construct a multi-scale synergistic corrosion protection system. Fourier transform infrared spectroscopy (FT-IR), X-ray diffraction (XRD), and transmission electron microscopy (TEM) confirmed the successful preparation of the composites, where PDA inhibited the agglomeration of h-BN and CeO₂ was uniformly loaded. Electrochemical tests showed that the corrosion inhibition efficiency of the extract of this composite for 2024 aluminum alloy reached 99.96%. After immersing the composite coating in 3.5 wt% NaCl solution for 120 days, the coating resistance (Rc) and charge transfer resistance (Rct) reached 8.5 × 10⁹ Ω·cm² and 1.2 × 10¹⁰ Ω·cm², respectively, which were much higher than those of pure SE coatings and coatings filled with single/binary fillers. Density functional theory (DFT) calculations revealed the synergistic mechanisms: PDA enhanced interfacial dispersion (adsorption energy of −0.58 eV), CeO₂ captured Cl⁻ (adsorption energy of −4.22 eV), and Ce³⁺ formed a passive film. This study provides key technical and theoretical support for the design of long-term corrosion protection coatings in harsh environments such as marine and petrochemical industries. Full article

With rapid advancements in sub-meter satellite and aerial imaging technologies, high-resolution remote sensing imagery has become a pivotal source for geospatial information acquisition. However, current semantic segmentation models encounter two primary challenges: (1) the inherent trade-off between capturing long-range global context and preserving precise local structural details—where excessive reliance on downsampled deep semantics often results in blurred boundaries and the loss of small objects and (2) the difficulty in modeling complex scenes with extreme scale variations, where objects of the same category exhibit drastically different morphological features. To address these issues, this paper introduces MAFMamba, a multi-scale adaptive fusion visual Mamba network tailored for high-resolution remote sensing images. To mitigate scale variation, we design a lightweight hybrid encoder incorporating an Adaptive Multi-scale Mamba Block (AMMB) in each stage. Driven by a Multi-scale Adaptive Fusion (MSAF) mechanism, the AMMB dynamically generates pixel-level weights to recalibrate cross-level features, establishing a robust multi-scale representation. Simultaneously, to strictly balance local details and global semantics, we introduce a Global–Local Feature Enhancement Mamba (GLMamba) in the decoder. This module synergistically integrates local fine-grained features extracted by convolutions with global long-range dependencies modeled by the Visual State Space (VSS) layer. Furthermore, we propose a Multi-Scale Cross-Attention Fusion (MSCAF) module to bridge the semantic gap between the encoder’s shallow details and the decoder’s high-level semantics via an efficient cross-attention mechanism. Extensive experiments on the ISPRS Potsdam and Vaihingen datasets demonstrate that MAFMamba surpasses state-of-the-art Convolutional Neural Network (CNN), Transformer, and Mamba-based methods in terms of mIoU and mF1 scores. Notably, it achieves superior accuracy while maintaining linear computational complexity and low memory usage, underscoring its efficiency in complex remote sensing scenarios. Full article

REBa₂Cu₃O_7−δ (REBCO) coated conductors are considered a critical material for next-generation high-field superconducting applications owing to their superior superconducting performance at elevated temperatures and under strong magnetic fields. However, rapid degradation of the critical current density ($J_c$) under high-field and high-temperature conditions remains a major limitation for their practical applications. To address this, controlling flux pinning centers has emerged as a crucial strategy to enhance performance. Irradiation techniques, as one of the most commonly employed methods, have attracted considerable attention due to their capability to provide precise control, high reproducibility, and flexibility in tailoring the microstructure. In this review, we focus on the effects of proton, heavy-ion, and neutron irradiation on the microstructure and superconducting properties of REBCO coated conductors. We discuss the underlying mechanisms in terms of defect types and distributions, energy loss processes, flux pinning enhancement, and the evolution of $J_c$ and transition temperature ($T_c$). Furthermore, we compare different irradiation methods, highlighting their advantages and suitability across diverse temperature and magnetic field conditions. The potential of hybrid irradiation strategies for creating multiscale composite pinning landscapes is also examined. Future efforts should aim to synergistically combine different irradiation mechanisms and optimize defect structures to develop REBCO tapes with highly isotropic and stable flux pinning, which is essential for large-scale applications in fusion energy, high-field magnets, and aerospace electric motors. Full article

Bovine Respiratory Disease (BRD) represents one of the largest causes of economic loss and animal morbidity in the global cattle industry, second only to neonatal diarrhea. Its etiology is complex, originating from a multifactorial combination of host susceptibility, environmental stressors, viral infections, and secondary bacterial pathogens. Although viruses are often the initial cause of disease, suppressing the host’s respiratory defense mechanisms, most of the severe pneumonic damage and clinical signs can be attributed to bacterial infections. This review provides an overview of the primary bacterial agents identified within the BRD complex, including Mannheimia haemolytica, Pasteurella multocida, Histophilus somni, and Mycoplasma bovis. We discuss their role as commensals that then become opportunistic pathogens, and further how they interact in a synergistic relationship with a primary viral insult, leading to the resulting pathogenesis and the development of pneumonia. This manuscript discusses in further detail some of the challenges in BRD management, such as the limitations of current diagnostic methodologies, overreliance on antimicrobial therapy, and the growing concern of antimicrobial resistance. Lastly, the need for integrated approaches in management, better husbandry and biosecurity, coupled with the development of novel therapeutic alternatives, is underlined as a means of assuring a sustainable control of this serious syndrome. Full article

This paper provides a systematic review of the progress in encoded microsphere suspension array technology and its application in the multiplex detection of mycotoxins. Mycotoxins are diverse and frequently coexist in food matrices, leading to synergistic toxic effects. This poses significant challenges to existing risk assessment systems. Current multiplex detection methods still face technical bottlenecks such as target loss, matrix interference, and reliance on large-scale instruments. Suspension array technology based on encoded microspheres, combined with efficient signal amplification strategies, offers an ideal platform for achieving highly sensitive and high-throughput analysis of mycotoxins. This paper systematically reviews the core aspects of this technology, including encoding strategies such as physical, optical, and multi-dimensional approaches, along with new encoding materials like aggregation-induced emission materials and fluorescent proteins. It further covers matrix materials and preparation methods with an emphasis on green, biocompatible options and integrated fabrication techniques, as well as signal amplification mechanisms based on nucleic acid amplification, enzyme catalysis, and nanomaterials. The integration of magnetic separation techniques and the combination with portable, smartphone-based platforms for intelligent on-site detection are also highlighted. Finally, this review outlines future development trends such as the incorporation of artificial intelligence, 3D printing, and smart algorithms, aiming to provide theoretical references and technical support for research and applications in related fields. Full article

Identifying minute targets within UAV-acquired imagery continues to pose substantial technical hurdles, primarily due to blurred boundaries, scarce textural details, and drastic scale variations amidst complex backgrounds. In response to these limitations, this paper proposes BFRI-YOLO, an enhanced architecture based on the YOLOv11n baseline. The framework is built upon four synergistic components designed to achieve high-precision localization and robust feature representation. First, we construct a Balanced Adaptive Feature Pyramid Network (BAFPN) that utilizes a resolution-aware attention mechanism to promote bidirectional interaction between deep and shallow features. This is complemented by incorporating the Receptive Field Convolutional Block Attention Module (RFCBAM) to refine the backbone network. By constructing the C3K2_RFCBAM block, we effectively enhance the feature representation of small objects across diverse receptive fields. To further refine the prediction phase, we develop a Four-Shared Detail Enhancement Detection Head (FSDED) to improve both efficiency and stability. Finally, regarding the loss function, we formulate the Inner-WIoU strategy by integrating auxiliary bounding boxes with dynamic focusing mechanisms to ensure precise target localization. The experimental results on the VisDrone2019 benchmark demonstrate that our method secures mAP@0.5 and mAP@0.5:0.95 scores of 42.1% and 25.6%, respectively, outperforming the baseline by 8.8% and 6.2%. Extensive tests on the TinyPerson and DOTA1.0 datasets further validate the robust generalization capability of our model, confirming that BFRI-Yolo strikes a superior balance between detection accuracy and computational overhead in aerial scenes. Full article

High-resolution remote sensing images are gradually playing an important role in land cover mapping, urban planning, and environmental monitoring tasks. However, current segmentation approaches frequently encounter challenges such as loss of detail and blurred boundaries when processing high-resolution remote sensing imagery, owing to their complex backgrounds and dense semantic content. In response to the aforementioned limitations, this study introduces HMA-UNet, a novel segmentation network built upon the UNet framework and enhanced through a hybrid attention strategy. The architecture’s innovation centers on a composite attention block, where a lightweight split fusion attention (LSFA) mechanism and a lightweight channel-spatial attention (LCSA) mechanism are synergistically integrated within a residual learning structure to replace the stacked convolutional structure in UNet, which can improve the utilization of important shallow features and eliminate redundant information interference. Comprehensive experiments on the WHDLD dataset and the DeepGlobe road extraction dataset show that our proposed method achieves effective segmentation in remote sensing images by fully utilizing shallow features and eliminating redundant information interference. The quantitative evaluation results demonstrate the performance of the proposed method across two benchmark datasets. On the WHDLD dataset, the model attains a mean accuracy, IoU, precision, and recall of 72.40%, 60.71%, 75.46%, and 72.41%, respectively. Correspondingly, on the DeepGlobe road extraction dataset, it achieves a mean accuracy of 57.87%, an mIoU of 49.82%, a mean precision of 78.18%, and a mean recall of 57.87%. Full article

Landslides are among the most prevalent geological hazards worldwide, posing severe threats to public safety due to their sudden onset and destructive potential. The rapid and accurate automated segmentation of landslide areas is a critical task for enhancing capabilities in disaster risk assessment, emergency response, and post-disaster management. However, existing deep learning models for landslide segmentation predominantly rely on unimodal remote sensing imagery. In complex Karst landscapes characterized by dense vegetation and severe shadow interference, the optical features of landslides are difficult to extract effectively, thereby significantly limiting recognition accuracy. Therefore, synergistically utilizing multimodal data while mitigating information redundancy and noise interference has emerged as a core challenge in this field. To address this challenge, this paper proposes a Triple-Stream Guided Enhancement and Fusion Network (TriGEFNet), designed to efficiently fuse three data sources: RGB imagery, Vegetation Indices (VI), and Slope. The model incorporates an adaptive guidance mechanism within the encoder. This mechanism leverages the terrain constraints provided by slope to compensate for the information loss within optical imagery under shadowing conditions. Simultaneously, it integrates the sensitivity of VIs to surface destruction to collectively calibrate and enhance RGB features, thereby extracting fused features that are highly responsive to landslides. Subsequently, gated skip connections in the decoder refine these features, ensuring the optimal combination of deep semantic information with critical boundary details, thus achieving deep synergy among multimodal features. A systematic performance evaluation of the proposed model was conducted on the self-constructed Zunyi dataset and two publicly available datasets. Experimental results demonstrate that TriGEFNet achieved mean Intersection over Union (mIoU) scores of 86.27% on the Zunyi dataset, 80.26% on the L4S dataset, and 89.53% on the Bijie dataset, respectively. Compared to the multimodal baseline model, TriGEFNet achieved significant improvements, with maximum gains of 7.68% in Recall and 4.37% in F1-score across the three datasets. This study not only presents a novel and effective paradigm for multimodal remote sensing data fusion but also provides a forward-looking solution for constructing more robust and precise intelligent systems for landslide monitoring and assessment. Full article

Accurate detection and counting of oat ears are essential for yield estimation but remain challenging in complex field environments due to occlusion, significant scale variation, and fluctuating lighting. The aim of this study is to develop a high-precision detection and counting model to address these challenges. The adopted methodology was an improved YOLOv8n model, named YOLOv8n-DSP. To address significant scale variation, a Diverse Branch Block (DBB) was introduced into the backbone to enhance multi-scale feature representation. For improved detection of small, dense oat ears, the neck was augmented with a Spatial and Channel Synergistic Attention (SCSA) mechanism to strengthen discriminative feature extraction. Furthermore, to refine localization among overlapping oat ears, the PIoUv2 loss function was employed for bounding box regression. The main results revealed that the proposed model achieved a mean average precision (mAP) of 94.0% and an F1-score of 87.1% on the oat ear detection task, representing gains of 3.2 and 1.8 percentage points over the baseline YOLOv8n, respectively. For counting, it reached an accuracy of 82.5%, a 9.2-point improvement. In conclusion, the YOLOv8n-DSP model provides an effective and practical approach for in-field oat ear detection and counting, suggesting considerable potential as a reliable tool for future yield prediction systems and advanced intelligent agricultural equipment. Full article

This research systematically investigates the influence of multi-microalloying with Sn, Mn, Er, and Zr on the homogenized microstructure, aging behavior, and mechanical properties of a 6082 Al-Mg-Si alloy. The optimization of the homogenization treatment for the alloy was based on isochronal aging curves and conductivity measurements. The results show that the addition of Mn, Er, and Zr can precipitate thermally stable Al(Fe,Mn)Si dispersoids and Al(Er,Zr) dispersoids. The three-stage homogenization treatment resulted in the precipitation of more heat-resistant dispersoids, thereby achieving the best thermal stability. During direct artificial aging, the initial hardening rate of the Mn-containing alloy was slightly delayed, but its peak hardness was significantly increased. This is due to the dispersoids offering additional heterogeneous nucleation sites for the strengthening precipitates. Meanwhile, the Sn atoms release their trapped vacancies at the aging temperature, thereby promoting atomic diffusion. However, short-term natural aging before artificial aging accelerated the early-stage aging response of the Sn-containing alloy but resulted in a reduced peak hardness. Notably, the co-microalloying with Mn and Sn led to a higher peak hardness during direct artificial aging, while it caused a more significant hardness loss when a natural aging preceded artificial aging, revealing a distinct synergistic negative effect. The reason for the negative synergy effect might be related to the weakened ability of Sn to release vacancies after natural aging. This study clarifies the process dependence of microalloying effects, providing a theoretical basis for optimizing aluminum alloy properties through the synergistic design of composition and processing routes. Full article

Lithium iron phosphate (LiFePO₄, LFP) batteries dominate grid-scale energy storage, yet their cycle life is capped by its capacity fade issues. Conventional failure workflows suffer from redundant tests, high cost, and long turnaround time because the underlying mechanisms remain unclear. Herein, multi-scale characterization coupled with electrochemical tests have been quantitatively established to reveal four synergistic fade modes of LFP: active-Li loss, FePO₄ secondary-phase formation, SEI rupture, and particle fracture. A two-tier “screen–validate” protocol is proposed to accurately and efficiently disclose its mechanism. In the screening tier, capacity, cyclic voltammetry, electrochemical impedance spectroscopy, low-magnification scanning electron microscopy, and snapshot X-ray diffraction (XRD) rapidly flag the most probable failure cause. The validation tier then deploys mechanism-matched in situ/ex situ tools (operando XRD, TEM, XPS, ToF-SIMS, etc.) to build a comprehensive evidence chain of dynamic structural evolution, materials loss tracking, and quantitative proof. The streamlined workflow preserves scientific rigor and reproducibility while cutting analysis time and cost, offering a closed-loop route for fast failure diagnosis and targeted optimization of next-generation LFP batteries. Full article

In response to the safety threats posed by unauthorized unmanned aerial vehicles (UAVs), the importance of anti-UAV systems is becoming increasingly apparent. In tasks involving UAV detection, small UAVs are particularly difficult to detect due to their low resolution. Therefore, this study proposed YOLO-CoOp, a lightweight multi-module collaborative optimization framework for detecting small UAVs. First, a high-resolution feature pyramid network (HRFPN) was proposed to retain more spatial information of small UAVs. Second, a C3k2-WT module integrated with wavelet transform convolution was proposed to enhance feature extraction capability and expand the model’s receptive field. Then, a spatial-channel synergistic attention (SCSA) mechanism was introduced to integrate spatial and channel information and enhance feature fusion. Finally, the DyATF method replaced the upsampling with Dysample and the confidence loss with adaptive threshold focal loss (ATFL), aiming to restore UAV details and balance positive–negative sample weights. The ablation experiments show that YOLO-CoOp achieves 94.3% precision, 93.1% recall, 96.2% mAP⁵⁰, and 57.6% mAP⁵⁰⁻⁹⁵ on the UAV-SOD dataset, with improvements of 3.6%, 10%, 5.9%, and 5% over the baseline model, respectively. The comparison experiments demonstrate that YOLO-CoOp has fewer parameters while maintaining superior detection performance. Cross-dataset validation experiments also demonstrate that YOLO-CoOp exhibits significant performance improvements in small object detection tasks. Full article

This research investigated the impact of using lecithin and casein on lignocellulosic fiberboards on their characteristics and properties, including fire resistance. The six experimental variants created included: (1) unmodified reference fiberboards, (2) fiberboards coated with casein only, (3) fiberboards that were vacuum-impregnated with rapeseed or (4) soy lecithin, and (5, 6) fiberboards that were both vacuum-impregnated with lecithin and coated with casein. Evaluation of the board’s mass uptake, density profile, modulus of elasticity, compressive strength and fire behavior (single face exposure to mass loss, maximum posterior temperature, and area burned) demonstrated that vacuum-impregnation with lecithin was the primary driving force behind mass uptake (producing minor densification of the surface), while the casein coating produced only very minor changes to mechanical properties and modestly modified the fire performance. Lecithin alone produced an increase in both mass loss and area burned while producing a decrease in maximum posterior temperature (about 20%–25%). Lecithin-impregnated boards that were also casein-coated displayed a synergistic effect; these boards provided intermediate mechanical properties with the highest levels of fire performance (approximately 20%–30% lower than the reference fiberboards) in terms of both mass loss and area burned while also having approximately 20%–30% lower maximum posterior temperature compared to the reference. Full article