Search Results (4,239)

Although AWGs are widely used in FBG interrogation systems, conventional interrogators are often bulky and hard to deploy, limiting their use in complex field environments. Here, we developed an FBG interrogator based on a photonic AWG chip, comprising a photonic chip module, an optoelectronic detection and processing module, and an output interface module. The AWG chip measures only 280 µm × 150 µm, while the entire interrogator measures just 160 mm × 100 mm × 80 mm, achieving system miniaturization. Wavelength interrogation tests show that the FBG interrogator achieves a wavelength accuracy of 9.87 pm and a high-speed sampling rate of up to 10 kHz, enabling high-precision, real-time FBG demodulation under rapidly varying temperatures. Furthermore, the interrogator was subjected to engineering validation, with dynamic FBG wavelength demodulation experiments conducted under high-temperature shocks in a turbo-engine, verifying its reliability under extreme conditions and demonstrating its potential for broader engineering applications. Full article

The growing use of radiation technologies has increased the need for shielding materials that are lightweight, safe, and adaptable to complex geometries. While lead remains highly effective, its toxicity and weight limit its suitability, driving interest in alternative materials. The process of 3D printing enables the rapid fabrication of customized shielding geometries; however, only limited research has focused on 3D-printed polymer composites formulated specifically for mixed photon–neutron fields. In this study, we developed a series of 3D-printable ABS-based composites incorporating tungsten (W), bismuth oxide (Bi₂O₃), gadolinium oxide (Gd₂O₃), and boron nitride (BN). Composite filaments were produced using a controlled extrusion process, and all materials were 3D printed under identical conditions to enable consistent comparison across formulations. Photon attenuation at 120 kVp and neutron attenuation using a broad-spectrum Pu–Be source (activity 4.5 × 10⁷ n/s), providing a mixed neutron field with a central flux of ~7 × 10⁴ n·cm⁻²·s⁻¹ (predominantly thermal with epithermal and fast components), were evaluated for both individual composite samples and layered (sandwich) configurations. Among single-material prints, the 30 wt% Bi₂O₃ composite achieved a mass attenuation coefficient of 2.30 cm²/g, approximately 68% of that of lead. Layered structures combining high-Z and neutron-absorbing fillers further improved performance, achieving up to ~95% attenuation of diagnostic X-rays and ~40% attenuation of neutrons. The developed materials provided a promising balance between 3D-printability and dual-field shielding effectiveness, highlighting their potential as lightweight, lead-free shielding components for diverse applications. Full article

To address the issue of drought level confusion in the detection of drought stress during the seedling stage of the Yunnan large-leaf tea variety using the traditional YOLOv13 network, this study proposes an improved version of the network, MC-YOLOv13-L, based on animal vision. With the compound eye’s parallel sampling mechanism at its core, Compound-Eye Apposition Concatenation optimization is applied in both the training and inference stages. Simulating the environmental information acquisition and integration mechanism of primates’ “multi-scale parallelism—global modulation—long-range integration,” multi-scale linear attention is used to optimize the network. Simulating the retinal wide-field lateral inhibition and cortical selective convergence mechanisms, CMUNeXt is used to optimize the network’s backbone. To further improve the localization accuracy of drought stress detection and accelerate model convergence, a dynamic attention process simulating peripheral search, saccadic focus, and central fovea refinement in primates is used. Inner-IoU is applied for targeted improvement of the loss function. The testing results from the drought stress dataset (324 original images, 4212 images after data augmentation) indicate that, in the training set, the Box Loss, Cls Loss, and DFL Loss of the MC-YOLOv13-L network decreased by 5.08%, 3.13%, and 4.85%, respectively, compared to the YOLOv13 network. In the validation set, these losses decreased by 2.82%, 7.32%, and 3.51%, respectively. On the whole, the improved MC-YOLOv13-L improves the accuracy, recall rate and mAP@50 by 4.64%, 6.93% and 4.2%, respectively, on the basis of only sacrificing 0.63 FPS. External validation results from the Laobanzhang base in Xishuangbanna, Yunnan Province, indicate that the MC-YOLOv13-L network can quickly and accurately capture the drought stress response of tea plants under mild drought conditions. This lays a solid foundation for the intelligence-driven development of the tea production sector and, to some extent, promotes the application of bio-inspired computing in complex ecosystems. Full article

The development of a metalloenzyme-like catalytic system for the efficient oxidation of olefins under a dioxygen (O₂) atmosphere at room temperature is of significant interest in the field of catalysis. Herein, we present a highly active and selective aerobic epoxidation of olefins using metalloenzyme-like catalysts based on a non-heme ligand, tris(2-pyridylmethyl)amine (TPA). Notably, manganese chloride complexed with TPA (Mn(TPA)Cl₂) demonstrated excellent activity for the epoxidation of trans-stilbene using O₂ as the oxidant in the presence of a co-reductant at 30 °C. A quantitative conversion of 99% and high yield of 98%, as determined by gas chromatography using an external standard method, were achieved under optimum reaction conditions. Furthermore, Mn(TPA)Cl₂ exhibited a good substrate tolerance to styrene derivatives with electron-withdrawing or electron-donating groups, cyclic olefins with different substituents and substitution degrees, as well as long-chain olefins. Coupled with a high turnover frequency (TOF) of up to 30,720 h⁻¹, these results underscore the potential of Mn(TPA)Cl₂ as a promising metalloenzyme-like catalytic platform for the aerobic synthesis of diverse epoxides from olefins under ambient conditions. Full article

Concrete structures suffering from Mg²⁺ environments may suffer severe damage, which mainly has something to do with the coupled effect among Cl⁻, SO₄²⁻, and Mg²⁺. Based on a systematic review of Web of Science and Scopus database (2000–2025), we first summarized the migration behavior, reaction paths, and interaction mechanism of Cl⁻, SO₄²⁻, and Mg²⁺ in cementitious matrices. Secondly, from the perspective of Cl⁻ cyclic adsorption–desorption breaking the passivation film of steel bars, SO₄²⁻ generating expansion products leads to crack expansion, then Mg²⁺ decalcifies C-S-H and transforms into M-S-H; we analyzed the main damage mechanisms, respectively. In addition, under the coexistence conditions of three kinds of ions, the “fixation–substitution–redissolution” process and “crack–transport” coupling positive feedback mechanism further increase the development rate of damage. Then, some anti-corrosion measures, such as mineral admixtures, functional chemical admixtures, fiber reinforcements, surface coatings, and new binder systems, are summarized, and the pros and cons of different anti-corrosion technologies are compared and evaluated. Lastly, from two aspects of simulation prediction for the coupled corrosion damage mechanism and service life prediction, respectively, we have critically evaluated the advances and problems existing in the current research on the aspects of ion migration-reaction coupled models, multi-physics coupled frameworks, phase-field methods, etc. We found that there is still much work to be conducted in three respects: deepening mechanism understanding, improving prediction precision, and strengthening the connection between laboratory test results and actual projects, so as to provide theoretical basis and technical support for the durability design and anti-corrosion strategies of concrete in complex Mg²⁺ environments. Full article

Direction of arrival (DOA) estimation is a fundamental problem in array signal processing, with extensive applications in radar, communications, sonar, and other fields. Traditional DOA estimation methods, such as MUSIC and ESPRIT, rely on eigenvalue decomposition or spectral peak search, which suffer from high computational complexity and performance degradation under conditions of low signal-to-noise ratio (SNR), coherent signals, and array imperfections. Cylindrical arrays offer unique advantages for omnidirectional sensing due to their circular structure and three-dimensional coverage capability; however, their nonlinear array manifold increases the difficulty of estimation. This paper proposes a circular-attention residual network (CA-ResNet) for DOA estimation using uniform cylindrical arrays. The proposed approach achieves high accuracy and robust angle estimation through phase difference feature extraction, a multi-scale residual network, an attention mechanism, and a joint output module. Simulation results demonstrate that the proposed CA-ResNet method delivers superior performance under challenging scenarios, including low SNR (−10 dB), a small number of snapshots (L = 5), and multiple sources (1 to 4 signal sources). The corresponding root mean square errors (RMSE) are 0.21°, 0.45°, and below 1.5°, respectively, significantly outperforming traditional methods like MUSIC and ESPRIT, as well as existing deep learning models (e.g., ResNet, CNN, MLP). Furthermore, the algorithm exhibits low computational complexity and a small parameter size, highlighting its strong potential for practical engineering applications and robustness. Full article

The dynamic growth of the electrified vehicle (xEV) market, including both electric and hybrid vehicles, has increased the demand for advanced Battery Management Systems (BMS). From an energy-systems perspective, xEV batteries act as distributed energy storage units that strongly interact with power grids, renewable generation, and charging infrastructure, making their efficient control a key element of low-carbon energy systems. Traditional BMS methods face challenges in accurately estimating key battery states and parameters, especially under dynamic operating conditions. This review systematically analyzes the progress in applying artificial intelligence, machine learning, and other advanced computational and data-driven algorithms to improve the performance of xEV battery management with a particular focus on energy efficiency, safe utilization of stored electrochemical energy, and the interaction between vehicles and the power system. The literature analysis covers key research trends from 2020 to 2025. This review covers a wide range of applications, including State of Charge (SOC) estimation, State of Health (SOH) prediction, and thermal management. We examine the use of various methods, such as deep learning, neural networks, genetic algorithms, regression, and also filtering algorithms, to solve these complex problems. This review also classifies the research by geographical distribution and document types, providing insight into the global landscape of this rapidly evolving field. By explicitly linking BMS functions with energy-system indicators such as charging load profiles, peak-load reduction, self-consumption of photovoltaic generation, and lifetime-aware energy use, this synthesis of contemporary research serves as a valuable resource for scientists and engineers who wish to understand the latest achievements and future directions in data-driven battery management and its role in modern energy systems. Full article

Due to the continuous improvement in the usage area and retention quality of plastic films in China, the serious residue film pollution faced by China has become a major threat to crop production. To address the aforementioned issues and in accordance with the actual demand for residue film recovery machines in the Xinjiang region of China, a residual film impurity separation device suitable for the recovery machine of crop residue films has been designed. The overall structure and working principle of the machine were elaborated. Numerical simulations of the through-flow fan device of the residual film recovery machine were carried out using the ANSYS 2022 (CFX) finite element analysis platform, and the corresponding wind speed range of the fan at rotational speeds of 1000–1400 r/min was obtained. Based on the simulation results, the Depth of Machine Insertion into the Ground, Fan Wind Speed, and Forward Speed of the Machinery were selected as experimental factors, while the residual film recovery rate was taken as the evaluation index. A response surface experiment was conducted, and the optimization analysis was performed using Design-Expert software. The final experimental validation results indicated that when the Depth of Machine Insertion into the Ground was 32 mm, the Forward Speed of the Machinery was 5.29 km/h, and the Fan Wind Speed was 13.67 m/s, the machine could effectively overcome the influence of complex field operating conditions. This parameter combination was identified as the optimal operating condition of the machine, providing a valuable reference for the design and optimization of related agricultural machinery. Full article

Weed competition represents a primary constraint in sesame production, causing substantial yield losses typically ranging from 18 to 68% under inadequate control measures. Precise crop–weed discrimination remains challenging due to morphological similarities, complex field conditions, and vegetation overlapping. To address these issues, we developed Context-Aware Feature-Enhanced Detection Transformer (CAFE-DETR), an enhanced Real-Time Detection Transformer (RT-DETR) architecture optimized for sesame–weed identification. First, the C2f with a Unified Attention-Gating (C2f-UAG) module integrates unified head attention with convolutional gating mechanisms to enhance morphological discrimination capabilities. Second, the Hierarchical Context-Adaptive Fusion Network (HCAF-Net) incorporates hierarchical context extraction and spatial–channel enhancement to achieve multi-scale feature representation. Furthermore, the Polarized Linear Spatial Multi-scale Fusion Network (PLSM-Encoder) reduces computational complexity from O(N²) to O(N) through polarized linear attention while maintaining global semantic modeling. Additionally, the Focaler-MPDIoU loss function improves localization accuracy through point distance constraints and adaptive sample focusing. Experimental results on the sesame–weed dataset demonstrate that CAFE-DETR achieves 90.0% precision, 89.5% mAP₅₀, and 59.5% mAP_50–95, representing improvements of 13.07%, 4.92%, and 2.06% above the baseline RT-DETR, respectively, while reducing computational cost by 23.73% (43.4 GFLOPs) and parameter count by 10.55% (17.8 M). These results suggest that CAFE-DETR is a viable alternative for implementation in intelligent spraying systems and precision agriculture platforms. Notably, this study lacks external validation, cross-dataset testing, and field trials, which limits the generalizability of the model to diverse real-world agricultural scenarios. Full article

Agricultural nonpoint source pollution is emerging as one of the increasingly serious environmental concerns all over the world. This study conducted field experiments in Zengcheng District, Guangzhou City, from 2019 to 2023 to explore the mechanisms by which different crop types, fertilization modes, and meteorological conditions affect the loss of nitrogen and phosphorus in agricultural nonpoint source pollution. In rice and corn, the CK and PK treatment groups showed significant fitting advantages, such as the R² of rice-CK reaching 0.309. MAE was 0.395, and the R² of corn-PK was as high as 0.415. For compound fertilization groups such as NPK and OF, the model fitting ability decreased, such as the R² of rice-NPK dropping to 0.193 and the R² of corn-OF being only 0.168. In addition, the overall performance of the model was limited in the modeling of total phosphorus. A relatively good fit was achieved in corn (such as NPK group R² = 0.272) and in vegetables and citrus. R² was mostly below 0.25. The results indicated that fertilization management, crop types, and meteorological conditions affected nitrogen and phosphorus losses in agricultural runoff. Cornfields under conventional nitrogen, phosphorus, and potassium fertilizer (NPK) and conventional nitrogen and potassium fertilizer treatment without phosphorus fertilizer (NK) treatments exhibited the highest nitrogen losses, while citrus fields showed elevated phosphorus concentrations under NPK and PK treatments. Organic fertilizer treatments led to moderate nutrient losses but greater variability. Organic fertilizer treatments resulted in moderate nutrient losses but showed greater interannual variability. Meteorological drivers differed among crop types. Nitrogen enrichment was mainly associated with high temperature and precipitation, whereas phosphorus loss was primarily triggered by short-term extreme weather events. Linear regression models performed well under simple fertilization scenarios but struggled with complex nutrient dynamics. Crop-specific traits such as flooding in rice fields, irrigation in corn, and canopy coverage in citrus significantly influenced nutrient migration. The findings of this study highlight that nutrient losses are jointly regulated by crop systems, fertilization practices, and meteorological variability, particularly under extreme weather conditions. These findings underscore the necessity of crop-specific and climate-adaptive nutrient management strategies to reduce agricultural nonpoint source pollution. By integrating long-term field observations with machine learning–based analysis, this study provides scientific evidence to support sustainable fertilizer management, protection of water resources, and environmentally responsible agricultural development in subtropical regions. The proposed approaches contribute to sustainable land and water resource utilization and climate-resilient agricultural systems, aligning with the goals of sustainable development in rapidly urbanizing river basins. Full article

Cucumber downy mildew, angular leaf spot, and powdery mildew represent three predominant fungal diseases that substantially compromise cucumber yield and quality. To address the challenges posed by the irregular morphology, prominent multi-scale characteristics, and ambiguous lesion boundaries of cucumber foliar diseases in complex field environments—which often lead to insufficient detection accuracy—along with the existing models’ difficulty in balancing high precision with lightweight deployment, this study presents YOLOv11n-DSU (a lightweight hierarchical detection model engineered using the YOLOv11n architecture). The proposed model integrates three key enhancements: deformable convolution (DEConv) for optimized feature extraction from irregular lesions, a spatial and channel-wise attention (SCSA) mechanism for adaptive feature refinement, and a Unified Intersection over Union (Unified-IoU) loss function to improve localization accuracy. Experimental evaluations demonstrate substantial performance gains, with mean Average Precision at 50% IoU threshold (mAP₅₀) and mAP_50–95 increasing by 7.9 and 10.9 percentage points, respectively, and precision and recall improving by 6.1 and 10.0 percentage points. Moreover, the computational complexity is markedly reduced to 5.8 Giga Floating Point Operations (GFLOPs). Successful deployment on an embedded platform confirms the model’s practical viability, exhibiting robust real-time inference capabilities and portability. This work provides an accurate and efficient solution for automated disease grading in field conditions, enabling real-time and precise severity classification, and offers significant potential for advancing precision plant protection and smart agricultural systems. Full article

Continuous cropping leads to declines in soil productivity and biodiversity, as well as a deterioration of overall phytosanitary conditions. What if we rotate the intercrops instead of the main crops? In a stationary three-year field experiment, maize was intercropped with Fabaceae (faba bean, crimson and Persian clovers, and blue-flowered alfalfa), Poaceae (winter rye, annual ryegrass, spring barley, and common oat), and Brassicaceae (white mustard, spring oilseed rape, oilseed radish, and spring Camelina) intercrops in separate growing seasons. Fabaceae intercrops developed slowly and competed poorly with weeds. The highest air-dried biomass (ADM) was produced by Persian and crimson clovers (approx. 86 g m⁻²). Intercrops of the Poaceae family, particularly rye and oats, as well as ryegrass, which was the most productive at 200 g m⁻² ADM, germinated faster and competed effectively with weeds. Brassicaceae intercrops also developed rapidly, especially mustard, Camelina, and radish (the most productive 206 g m⁻² ADM). Most intercrops competed with maize and reduced its biomass productivity; however, their competitive effects were weaker than those of weeds. A strong negative correlation between maize and weed biomass was detected (max. r = −0.946; p < 0.01). Complex evaluation index (CEI) showed that the crimson clover–annual ryegrass–spring oilseed rape rotation (CC-AR-SR) was the most productive and was effective in suppressing major weeds Echinochloa crus-galli, Chenopodium album, Polygonum lapathifolium, and Cirsium arvense, less competitive with maize (CEI 4.82), and can be used as an Integrated Pest Management tool. 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

Downbursts generate strong and transient near-surface winds that significantly influence wind flows over complex terrains. In this study, two downburst models—the impinging jet model representing the near-field region and the wall jet model representing the fully developed outflow—were experimentally investigated. The study examined the characteristics of mountain wind fields within the fully developed region, considering variations in mountain height, slope, shape, and radial position. Results show that mountain height and shape exert only minor influences on the mountain speed-up ratio, whereas slope and radial position play dominant roles: the acceleration ratio decreases with increasing radial distance and with steeper slopes. The near-surface flow is mainly affected within a vertical range of approximately 1.5 times the mountain height and a radial distance of about four times the height. By explicitly comparing the two models, this study provides the quantitative experimental relationship linking the vertical position of maximum horizontal velocity between impinging jet and wall jet flows. The comparison of mountain wind fields under equivalent positions demonstrated consistent speed-up ratios, confirming that the wall jet model can effectively reproduce the fully developed stage of downburst winds over mountainous terrain. Thus, this work offers new experimental evidence and a validated modeling framework for studying mountain wind effects under downburst conditions. Full article

Fibrosis is a pathological condition resulting from an excessive tissue response during the repair process, often affecting various tissues such as the skin, organs, and joints, posing a significant threat to global health. Researchers have made substantial efforts to explore the endogenous mechanisms underlying fibrosis in recent years and have developed several therapeutic strategies to block this process. Historically, research on fibrotic diseases has focused on identifying highly relevant therapeutic targets and developing effective antifibrotic drugs. However, due to the complexity of the mechanisms of fibrosis and its effector cells, the effectiveness of antifibrotic therapies remains limited. With the advancement of high-throughput omics technologies and machine learning tools, we now have a clearer understanding of cellular heterogeneity, intercellular interactions, and the specific roles of cells in various biological processes. This enables tracking the trajectory of different cell types during the fibrotic process, facilitating early identification and discovery of new targets for fibrosis treatment, and conducting more precise targeted research. Supported by these novel technologies, numerous studies have revealed that, in addition to normal fibroblasts, a group of bone marrow–derived fibrocytes also contributes to the fibrosis of both parenchymal and non-parenchymal organs and tissues. Circulating fibrocytes are hematopoietic-derived cells that are recruited to injury sites during injury, disease, and aging, acting as participants in inflammation and tissue repair, and directly or indirectly promoting fibrosis in various tissues throughout the body. This review summarizes the general characteristics of circulating fibrocytes, the molecular mechanisms involved in their recruitment to different tissues, the process of their differentiation into fibroblasts, their potential roles in various diseases, and the latest research developments in this field. Given the key role of circulating fibrocytes in fibrosis across multiple tissues, they may serve as promising targets for the development of novel antifibrotic therapies. Full article