Search Results (650)

Harnessing solar energy for photocatalytic water splitting and hydrogen fuel production necessitates the development of advanced photocatalysts with broad solar spectrum absorption and efficient electron-hole separation. In this study, we systematically explore the potential of the SGa₂Se/TeMoS heterojunction as a water-splitting photocatalyst using first-principles calculations. Our results indicate that while the heterojunction exhibits type-II band alignment, its band edge positions are inadequate for initiating water redox reactions. To overcome this limitation, we successfully engineered a Z-scheme SGa₂Se/Zr/TeMoS heterojunction by incorporating a Zr layer to modulate the charge transfer mechanism between the SGa₂Se and TeMoS layers. The potential positions of the HER and OER in this Z-scheme heterojunction overcome the limitation of the bandgap on water decomposition, allowing the optimized heterojunction to exhibit suitable band edge positions for water splitting across a wide pH range (0 ≤ pH ≤ 11.3), from acidic to weakly basic conditions. Additionally, the heterojunction exhibits exceptional light absorption capabilities across the entire spectrum, particularly in the infrared and visible regions, which greatly enhances the utilization of solar energy and highlights its potential as an efficient broad-spectrum photocatalyst for water splitting. Full article

The electrochemical CO₂ reduction reaction (eCO₂RR), driven by renewable energy, represents a promising strategy for mitigating atmospheric CO₂ levels while generating valuable fuels and chemicals. Its practical implementation hinges on the development of highly efficient electrocatalysts. In this study, a novel dual-metal atomic catalyst (DAC), composed of niobium and palladium single atoms anchored on a ferroelectric α-In₂Se₃ monolayer (Nb-Pd@In₂Se₃), is proposed based on density functional theory (DFT) calculations. The investigation encompassed analyses of structural and electronic characteristics, CO₂ adsorption configurations, transition-state energetics, and Gibbs free energy changes during the eCO₂RR process, elucidating a synergistic catalytic mechanism. The Nb-Pd@In₂Se₃ DAC system demonstrates enhanced CO₂ activation compared to single-atom counterparts, which is attributed to the complementary roles of Nb and Pd sites. Specifically, Nb atoms primarily drive carbon reduction, while neighboring Pd atoms facilitate oxygen species removal through proton-coupled electron transfer. This dual-site interaction lowers the overall reaction barrier, promoting efficient CO₂ conversion. Notably, the polarization switching of the In₂Se₃ substrate dynamically modulates energy barriers and reaction pathways, thereby influencing product selectivity. Our work provides theoretical guidance for designing ferroelectric-supported DACs for the eCO₂RR. Full article

In this paper, we present a practical partitioned intelligent-reflecting-surface-aided transmit spatial modulation (PIRS-TSM) scheme, where spatial modulation is implemented at the transmitter and partitioning is conducted on the IRS to enhance the spectral efficiency (SE) and reliability for multiple-input single-output (MISO) systems. The theoretical analysis of average bit error rate (ABER) based on maximum likelihood (ML) detection and the computational complexity analysis are provided. Experimental simulations demonstrate that the PIRS-TSM scheme obtains a significant ABER enhancement under the same SE compared to the existing partitioned IRS-aided transmit space shift keying or generalized space shift keying schemes by additionally carrying modulated symbols. Moreover, the system performances with different configurations of antenna numbers and symbol modulation orders under the same SE are investigated as a practical application reference. Full article

Ensuring the security of remote sensing images is essential to prevent unauthorized access, tampering, and misuse. Deep learning-based digital watermarking offers a promising solution by embedding imperceptible information to protect data integrity. This paper proposes SIR-DCGAN, an attention-guided robust watermarking method for remote sensing image protection. It incorporates an IR-FFM feature fusion module to enhance feature reuse across different layers and an SE-AM attention mechanism to emphasize critical watermark features. Additionally, a noise simulation sub-network is introduced to improve resistance against common and combined attacks. The proposed method achieves high imperceptibility and robustness while maintaining low computational cost. Extensive experiments on both remote sensing and natural image datasets validate its effectiveness, with performance consistently surpassing existing approaches. These results demonstrate the practicality and reliability of SIR-DCGAN for secure image distribution and copyright protection. Full article

Driver distraction has become a critical factor in traffic accidents, necessitating accurate behavior recognition for road safety. However, existing methods still suffer from limitations such as low accuracy in recognizing drivers’ localized actions and difficulties in distinguishing subtle differences between different behaviors. This paper proposes RMTSE, a hybrid attention model, to enhance driver distraction recognition. The framework introduces a Manhattan Self-Attention Squeeze-and-Excitation (MaSA-SE) module that combines spatial self-attention with channel attention mechanisms. This integration enables simultaneous enhancement of discriminative features and suppression of irrelevant characteristics in driving behavior images, improving learning efficiency through focused feature extraction. We also propose to employ a transfer learning strategy utilizing pre-trained weights during the training process, which further accelerates model convergence and enhances feature generalization. The model achieves Top-1 accuracies of 99.82% and 94.95% on SFD3 and 100-Driver datasets, respectively, with minimal parameter increments, outperforming existing state-of-the-art methods. Full article

In this study, we demonstrate that the Sn₂Se₂P₄ monolayer exhibits intrinsic anisotropic electronic characteristics with the strain-synergistic modulation of carrier transport and optoelectronic properties, as revealed by various levels of density functional theory calculations combined with the non-equilibrium Green’s function method. The calculations reveal that a-axis uniaxial compression of the Sn₂Se₂P₄ monolayer induces an indirect-to-direct bandgap transition (from 1.73 eV to 0.97 eV, as calculated by HSE06), reduces the hole effective mass by ≥70%, and amplifies current density by 684%. Conversely, a-axis uniaxial expansion (+8%) boosts ballistic transport (a/b-axis current ratio > 10⁵), rivaling black phosphorus. Notably, a striking negative differential conductance arises with the maximum I_peak/I_valley in the order of 10⁵ under the 2% uniaxial compression along the b-axis of the Sn₂Se₂P₄ monolayer. Visible-range anisotropic absorption coefficients (~10⁵ cm⁻¹) are achieved, where −4% a-axis strain elevates the photocurrent density (6.27 μA mm⁻² at 2.45 eV) and external quantum efficiency (39.2%) beyond many 2D materials benchmarks. Non-monotonic strain-dependent photocurrent density peaks at 2.00 eV correlate with hole effective mass reduction patterns, confirming the carrier mobility of the Sn₂Se₂P₄ monolayer as the governing parameter for photogenerated charge separation. These results establish Sn₂Se₂P₄ as a multifunctional material enabling strain-tailored anisotropy for logic transistors, negative differential resistors, and photovoltaic devices, while guiding future investigations on environmental stabilization and heterostructure integration toward practical applications. Full article

Wrist fractures, especially those involving the elbow and distal radius, are the most common injuries in children, teenagers, and young adults, with the highest occurrence rates during adolescence. However, the demand for medical imaging and the shortage of radiologists make it challenging to ensure accurate diagnosis and treatment. This study explores how AI-driven approaches are used to enhance fracture detection and improve diagnostic accuracy. In this paper, we propose the latest version of YOLO (i.e., YOLO11) with an attention module, designed to refine detection correctness. We integrated attention mechanisms, such as Global Attention Mechanism (GAM), channel attention, and spatial attention with Residual Network (ResNet), to enhance feature extraction. Moreover, we developed the ResNet_GAM model, which combines ResNet with GAM to improve feature learning and model performance. In this paper, we apply a data augmentation process to the publicly available GRAZPEDWRI-DX dataset, which is widely used for detecting radial bone fractures in X-ray images of children. Experimental findings indicate that integrating Squeeze-and-Excitation (SE_BLOCK) into YOLO11 significantly increases model efficiency. Our experimental results attain state-of-the-art performance, measured by the mean average precision (mAP50). Through extensive experiments, we found that our model achieved the highest mAP50 of 0.651. Meanwhile, YOLO11 with GAM and ResNet_GAM attained a maximum precision of 0.799 and a recall of 0.639 across all classes on the given dataset. The potential of these models to improve pediatric wrist imaging is significant, as they offer better detection accuracy while still being computationally efficient. Additionally, to help surgeons identify and diagnose fractures in patient wrist X-ray images, we provide a Fracture Detection Web-based Interface based on the result of the proposed method. This interface reduces the risk of misinterpretation and provides valuable information to assist in making surgical decisions. Full article

The accurate extraction of mountainous cropland from remote sensing images remains challenging due to its fragmented plots, irregular shapes, and the terrain-induced shadows. To address this, we propose a deep learning framework, SE-ResUNet, that integrates Squeeze-and-Excitation (SE) modules into ResUNet to enhance feature representation. Leveraging Sentinel-1/2 imagery and DEM data, we fuse vegetation indices (NDVI/EVI), terrain features (Slope/TRI), and SAR polarization characteristics into 3-channel inputs, optimizing the network’s discriminative capacity. Comparative experiments on network architectures, feature combinations, and terrain conditions demonstrated the superiority of our approach. The results showed the following: (1) feature fusion (NDVI + TerrainIndex + SAR) had the best performance (OA: 97.11%; F1-score: 96.41%; IoU: 93.06%), significantly reducing shadow/cloud interference. (2) SE-ResUNet outperformed ResUNet by 3.53% for OA and 8.09% for IoU, emphasizing its ability to recalibrate channel-wise features and refine edge details. (3) The model exhibited robustness across diverse slopes/aspects (OA > 93.5%), mitigating terrain-induced misclassifications. This study provides a scalable solution for mountainous cropland mapping, supporting precision agriculture and sustainable land management. Full article

Retinal ischemic disorders present significant threats to vision, characterized by inadequate blood supply oxygen–glucose deprivation (OGD), oxidative stress, and cellular injury, often resulting in irreversible injury. Catalpol, an iridoid glycoside derived from Rehmannia glutinosa, has demonstrated antioxidative and neuroprotective effects. This study aimed at investigating the protective effects and mechanisms of catalpol against oxidative stress or OGD in vitro and retinal ischemia in vivo, focusing on the modulation of key biomarkers of retinal ischemia, including HIF-1α, vascular endothelial growth factor (VEGF), angiopoietin-2, MCP-1, and the Wnt/β-catenin pathway. Cellular viability was assessed using retinal ganglion cell-5 (RGC-5) cells cultured in DMEM; a 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide assay was performed. H₂O₂ (1 mM)/OGD was utilized. Vehicle or different catalpol concentrations were administered 15 min before the ischemic-like insults. The Wistar rat eyes’ intraocular pressure was increased to 120 mmHg for 60 min to induce retinal ischemia. Intravitreous injections of catalpol (0.5 or 0.25 mM), Wnt inhibitor DKK1 (1 μg/4 μL), anti-VEGF Lucentis (40 μg/4 μL), or anti-VEGF Eylea (160 μg/4 μL) were administered to the rats’ eyes 15 min before or after retinal ischemia. Electroretinogram (ERG), fluorogold retrograde labeling RGC, Western blotting, ELISA, RT-PCR, and TUNEL were utilized. In vitro, both H₂O₂ and OGD models significantly (p < 0.001/p < 0.001; H₂O₂ and OGD) induced oxidative stress/ischemic-like insults, decreasing RGC-5 cell viability (from 100% to 55.14 ± 2.19%/60.84 ± 4.57%). These injuries were insignificantly (53.85 ± 1.28% at 0.25 mM)/(63.46 ± 3.30% at 0.25 mM) and significantly (p = 0.003/p = 0.012; 64.15 ± 2.41%/77.63 ± 8.59% at 0.5 mM) altered by the pre-administration of catalpol, indicating a possible antioxidative and anti-ischemic effect of 0.5 mM catalpol. In vivo, catalpol had less effect at 0.25 mM for ERG amplitude ratio (median [Q1, Q3] 14.75% [12.64%, 20.48%]) and RGC viability (mean ± SE 63.74 ± 5.13%), whereas (p < 0.05 and p < 0.05) at 0.5 mM ERG’s ratio (35.43% [24.35%, 43.08%]) and RGC’s density (74.34 ± 5.10%) blunted the ischemia-associated significant (p < 0.05 and p < 0.01) reduction in ERG b-wave amplitude (6.89% [4.24%, 10.40%]) and RGC cell viability (45.64 ± 3.02%). Catalpol 0.5 mM also significantly protected against retinal ischemia supported by the increased amplitude ratio of ERG a-wave and oscillatory potential, along with recovering a delayed a-/b-wave response time ratio. When contrasted with DKK1 or Lucentis, catalpol exhibited similar protective effects against retinal ischemia via significantly (p < 0.05) blunting the ischemia-induced overexpression of β-catenin, VEGF, or angiopoietin-2. Moreover, ischemia-associated significant increases in apoptotic cells in the inner retina, inflammatory biomarker MCP-1, and ischemic indicator HIF-1α were significantly nullified by catalpol. Catalpol demonstrated antiapoptotic, anti-inflammatory, anti-ischemic (in vivo retinal ischemia or in vitro OGD), and antioxidative (in vitro) properties, counteracting retinal ischemia via suppressing upstream Wnt/β-catenin and inhibiting downstream HIF-1α, VEGF, and angiopoietin-2, together with its decreasing TUNEL apoptotic cell number and inflammatory MCP-1 concentration. Full article

Extracting nodal features is crucial for analyzing rock structure stability and plays a significant role in designing engineering projects. This study presents an enhanced version of the FraSegNet algorithm, focusing on improving its ability to identify nodal features in images. The updated FraSegNet incorporates the ResNet101 backbone and integrates the Squeeze-and-Excitation (SE) attention mechanism, enabling better concentration on key nodal characteristics. The primary improvements are as follows: (1) Multi-scale feature extraction: Leveraging the ResNet101 architecture for the effective extraction of detailed information from nodal images. (2) Better attention mechanisms: The SE module focuses on nodal regions, resulting in clearer and more refined feature representations. (3) Dynamic learning strategies: I incorporation of cosine annealing and warm-up techniques to optimize training efficiency. The algorithm was validated with the Barton–Bandis model and Hoek–Brown criterion. The experimental results demonstrate its superior performance, achieving 97.1% accuracy in nodal feature detection with an average error of only 1.5% compared to the rock mass parameter. This small error proves the model works well. FraSegNet offers accurate segmentation and precise geometric parameter extraction, making it a valuable tool for advancing rock stability analysis and practical applications in rock mechanics. Full article

The leaf water content (LWC) of citrus is a pivotal indicator for assessing citrus water status. Addressing the limitations of traditional hyperspectral modelling methods, which rely on single preprocessing techniques and struggle to fully exploit the complex information within spectra, this study proposes a novel strategy for estimating citrus LWC by integrating spectral preprocessing combinations with an enhanced deep learning architecture. Utilizing a citrus plantation in Guangxi as the experimental site, 240 leaf samples were collected. Seven preprocessing combinations were constructed based on multiplicative scatter correction (MSC), continuous wavelet transform (CWT), and first derivative (1st D), and a new multichannel network, EDPNet (Ensemble Data Preprocessing Network), was designed for modelling. Furthermore, this study incorporated an attention mechanism within EDPNet, comparing the applicability of SE Block, SAM, and CBAM in integrating spectral combination information. The experiments demonstrated that (1) the triple preprocessing combination (MSC + CWT + 1st D) significantly enhanced model performance, with the prediction set R² reaching 0.8036, a 13.86% improvement over single preprocessing methods, and the RMSE reduced to 2.3835; (2) EDPNet, through its multichannel parallel convolution and shallow structure design, avoids excessive network depth while effectively enhancing predictive performance, achieving a prediction accuracy (R² = 0.8036) that was 5.58–9.21% higher than that of AlexNet, VGGNet, and LeNet-5, with the RMSE reduced by 9.35–14.65%; and (3) the introduction of the hybrid attention mechanism CBAM further optimized feature weight allocation, increasing the model’s R² to 0.8430 and reducing the RMSE to 2.1311, with accuracy improvements of 2.08–2.36% over other attention modules (SE, SAM). This study provides a highly efficient and accurate new method for monitoring citrus water content, offering practical significance for intelligent orchard management and optimal resource allocation. Full article

Autonomous navigation in agricultural environments requires precise obstacle classification to ensure collision-free movement. This study proposes a convolutional neural network (CNN)-based model designed to enhance obstacle classification for agricultural robots, particularly in orchards. Building upon a previously developed YOLOv8n-based real-time detection system, the model incorporates Ghost Modules and Squeeze-and-Excitation (SE) blocks to enhance feature extraction while maintaining computational efficiency. Obstacles are categorized as “Real”—those that physically impact navigation, such as tree trunks and persons—and “Fake”—those that do not, such as tall weeds and tree branches—allowing for precise navigation decisions. The model was trained on separate orchard and campus datasets and fine-tuned using Hyperband optimization and evaluated on an external test set to assess generalization to unseen obstacles. The model’s robustness was tested under varied lighting conditions, including low-light scenarios, to ensure real-world applicability. Computational efficiency was analyzed based on inference speed, memory consumption, and hardware requirements. Comparative analysis against state-of-the-art classification models (VGG16, ResNet50, MobileNetV3, DenseNet121, EfficientNetB0, and InceptionV3) confirmed the proposed model’s superior precision (p), recall (r), and F1-score, particularly in complex orchard scenarios. The model maintained strong generalization across diverse environmental conditions, including varying illumination and previously unseen obstacles. Furthermore, computational analysis revealed that the orchard-combined model achieved the highest inference speed at 2.31 FPS while maintaining a strong balance between accuracy and efficiency. When deployed in real-time, the model achieved 95.0% classification accuracy in orchards and 92.0% in campus environments. The real-time system demonstrated a false positive rate of 8.0% in the campus environment and 2.0% in the orchard, with a consistent false negative rate of 8.0% across both environments. These results validate the model’s effectiveness for real-time obstacle differentiation in agricultural settings. Its strong generalization, robustness to unseen obstacles, and computational efficiency make it well-suited for deployment in precision agriculture. Future work will focus on enhancing inference speed, improving performance under occlusion, and expanding dataset diversity to further strengthen real-world applicability. Full article

This work presents a cost-effective optical frequency comb generator (CEOFCG) solution for generating multiple, equally spaced carriers in wavelength-division-multiplexing coherent optical fiber communication systems (WDM-COFCS). It enables the replacement of multiple laser sources with a single continuous-wave laser, eliminating the need for additional amplification and filtering setups. The CEOFCG provides stable multicarrier spacing, broad phase coherence, and compatibility with advanced modulation formats, enhancing the performance of WDM-COFCS. Digital signal processing (DSP) techniques, including digital filtering, detection, and impairment compensation, contribute to high transmission and spectral efficiency (SE). The results demonstrate the potential of CEOFCG in achieving cost reduction, complexity reduction, high SE, and optimal utilization of optical fiber bandwidth, particularly in higher-order QAM-based COFCS. Full article

With the development of the railway industry and the progression of deep learning technology, object detection algorithms have been gradually applied to track defect detection. To address the issues of low detection efficiency and inadequate accuracy, we developed an improved orbital defect detection algorithm utilizing the YOLO11 model. First, the conventional convolutional layers in the YOLO (You Only Look Once) 11backbone network were substituted with the SPD-Conv (Spatial Pyramid Dilation Convolution) module to enhance the model’s detection performance on low-resolution images and small objects. Secondly, the EffectiveSE (Effective Squeeze-Excitation) attention mechanism was integrated into the backbone network to enhance the model’s utilization of feature information across various layers, thereby improving its feature representation capability. Finally, a small target detection head was added to the neck network to capture targets of different scales. These improvements help the model identify targets in more difficult tasks and ensure that the neural network allocates more attention to each target instance, thus improving the model’s performance and accuracy. In order to verify the effectiveness of this model in track defect detection tasks, we created a track fastener dataset and a track surface dataset and conducted experiments. The mean Average Precision (mAP@0.5) of the improved algorithm on track fastener dataset and track surface dataset reached 95.9% and 89.5%, respectively, which not only surpasses the original YOLO11 model but also outperforms other widely used object detection algorithms. Our method effectively improves the efficiency and accuracy of track defect detection. Full article

Sleep apnea, characterized by its high prevalence and serious health consequences, faces a critical bottleneck in diagnosis. Polysomnography (PSG), the gold standard, is costly and cumbersome, while wearable devices struggle with quality control and patient compliance, rendering them as unsuitable for both large-scale screening and continuous monitoring. To address these challenges, this research introduces a contactless, low-cost, and accurate event-level sleep apnea detection method leveraging frequency-modulated continuous-wave (FMCW) radar technology. The core of our approach is a novel deep-learning model, built upon the U-Net architecture and augmented with self-attention mechanisms and squeeze-and-excitation (SE) modules, meticulously designed for the precise event-level segmentation of sleep apnea from FMCW radar signals. Crucially, we integrate blood oxygen saturation (SpO₂) prediction as an auxiliary task within a multitask-learning framework to enhance the model’s feature extraction capabilities and clinical utility by capturing physiological correlations between apnea events and oxygen levels. Rigorous evaluation in a clinical dataset, comprising data from 35 participants, with synchronized PSG and radar data demonstrated a performance exceeding that of the baseline methods (Base U-Net and CNN–MHA), achieving a high level of accuracy in event-level segmentation (with an F1-score of 0.8019) and OSA severity grading (91.43%). These findings underscore the significant potential of our radar-based event-level detection system as a non-contact, low-cost, and accurate solution for OSA assessment. This technology offers a promising avenue for transforming sleep apnea diagnosis, making large-scale screening and continuous home monitoring a practical reality and ultimately leading to improved patient outcomes and public health impacts. Full article