Search Results (1,882)

The transformation of toluene to benzaldehyde via green and sustainable routes is of great significance in the fine chemical industry. However, hard activation of benzylic C(sp³)-H bonds and facile overoxidation of the generated benzaldehyde collectively render the selective oxidation of toluene extremely challenging. In this study, we constructed a core–shell heterostructure photocatalyst, CeO₂@NiFe-LDH, employing molecular oxygen as the oxidant. Under mild conditions of room temperature and visible-light illumination, the catalyst achieves a toluene conversion rate of 1.936 mmol·g⁻¹·h⁻¹ with an excellent benzaldehyde selectivity of 81.0%, and its catalytic performance is significantly superior to that of the individual single-phase materials and the simple physical mixture. Optical and electrochemical measurements confirm enhanced visible-light absorption and utilization, as well as greatly improved separation and migration efficiency of photogenerated charge carriers. Furthermore, the CeO₂@NiFe-LDH heterostructure features staggered band alignment, promoting S-scheme charge transfer across the heterointerface, thereby substantially boosting the redox capacity of the composite catalyst. Consequently, the photogenerated carriers with high reactivity are fully engaged in catalytic reactions, enabling efficient carrier utilization and ultimately leading to a significantly enhanced photocatalytic performance. This study not only demonstrates the outstanding application potential of CeO₂@NiFe-LDH for the visible-light-driven selective oxidation of toluene to benzaldehyde, but also offers a novel strategy for enhancing the photocatalytic performance of LDH-based materials. Full article

Facing the dual challenges of energy shortage and environmental degradation, photocatalysis and electrocatalysis have emerged as key technologies for converting small molecules into value-added chemicals, yet their conflicting requirements on the electronic structure of catalysts prevent a single material from freely switching between the two modes. Here, we demonstrate a feasible strategy for achieving on-demand switching between these catalytic functions in a single ferroelectric heterojunction, Pd-C₃N₄@In₂Se₃, through polarization engineering. Using first-principles density functional theory calculations, we show that reversing the polarization direction of the α-In2Se3 layer induces a nonvolatile electronic phase transition. The downward polarization (P↓) configuration exhibits metallic behavior, whereas the upward polarization (P↑) state becomes semiconducting with a type-II band alignment. This transition arises from polarization-dependent interfacial built-in electric fields and charge transfer differences. Notably, the metallicity of the P↓ configuration is localized predominantly within the In₂Se₃ layer rather than delocalized over the entire heterostructure. This arises because the enhanced interfacial charge transfer, driven by the larger work-function difference, selectively populates the conduction band of In₂Se₃, pushing its band edge across the Fermi level, while the Pd-C₃N₄ layer remains semiconducting due to charge depletion and the absence of gap-closing hybridization at the interface. In the P↑ state, the heterojunction acts as an efficient photocatalyst for overall water splitting, with band edges straddling the redox potentials. Under illumination, photogenerated electrons and holes make the hydrogen evolution reaction and oxygen evolution reaction thermodynamically spontaneous. In contrast, the metallic P↓ state serves as an excellent electrocatalyst for hydrogen evolution, delivering a limiting potential as low as −0.11 V, attributed to strengthened N 2p and H 1s orbital hybridization. These findings resolve the conflicting electronic requirements of photocatalysis and electrocatalysis and offer a new paradigm for designing smart, dual-functional catalysts adaptable to varying energy inputs, providing valuable theoretical guidance for future experimental realization of switchable catalytic systems. Full article

Accurate detection of tomato flowering stages is important for greenhouse crop management and automated pollination, but it remains challenging because tomato flowers are small, densely distributed, frequently occluded, and visually similar across adjacent developmental stages. To address these problems, this study proposes YOLO11-FAL, an improved object detection model based on YOLO11 for tomato flowering stage detection in greenhouse environments. The original C3k2 modules are replaced with C3k2_Faster to reduce redundant spatial computation and enhance local structural feature representation. An Attentional Scale Sequence Fusion (ASF) structure is introduced into the neck network to strengthen multi-scale feature interaction, and a Localization Quality Estimation Head (LQEHead) is incorporated to recalibrate classification confidence using bounding-box distribution information. Experiments were conducted on a self-constructed tomato flower dataset containing Bud, Anthesis, and Post-anthesis stages under varying illumination conditions. YOLO11-FAL achieved 92.00% Precision, 88.88% mAP@0.5, 62.21% mAP@0.75, and 56.72% mAP@0.5:0.95. The model contained 2.384 M parameters and achieved 49.58 FPS on an NVIDIA Jetson AGX Orin (NVIDIA Corporation, Santa Clara, CA, USA) using TensorRT FP16, with a measured onboard power consumption of 11.07 W. These results indicate that YOLO11-FAL provides a practical and efficient visual perception approach for greenhouse tomato flowering stage detection. Full article

Image reconstruction in nighttime dynamic scenes is challenged by low illumination, long exposure, rapid camera or object motion, and sensor noise. Conventional RGB cameras, therefore, struggle to recover both sufficient brightness and clear structural details in nighttime dynamic scenes. To address this problem, we propose an event-guided image reconstruction method for nighttime dynamic visual perception. The method constructs a multi-channel event voxel representation by jointly encoding event count, event intensity, timestamp distribution, and blurred-frame intensity priors. A parameter-efficient local–global reconstruction network is then designed to restore fine-grained textures and model holistic structures. In addition, edge-alignment and blur-alignment constraints are introduced to improve geometric consistency and imaging plausibility. Experiments on the HQF and REDS datasets show that the proposed method outperforms existing methods in the MSE, PSNR, and SSIM. Compared with DeblurSR, it reduces the MSE by 14.81% on HQF and 10.00% on REDS, while improving the PSNR by 1.603 dB and 1.053 dB, respectively. Qualitative results further show sharper edges, lower structural errors, and better edge consistency. Low illumination, dynamic blur, rapid brightness variation, and event noise are also common degradation factors in nighttime UAV imaging, making the investigated problem technically relevant to that setting. However, because neither REDS nor HQF was acquired during an actual UAV flight, the reported results establish benchmark-level reconstruction performance rather than UAV-specific operational effectiveness. Full article

Image-level monitoring and variety-level detection of three specialty tomato cultivars, Kiss, Millennium, and White Jade, remain challenging in unstructured orchards because of foliage occlusion, overlapping fruit clusters, and variable illumination. Conventional downsampling may weaken fine spatial details of small targets, whereas larger detectors can impose computational demands that are unsuitable for mobile or edge-based agricultural platforms. To address these limitations, we propose WCAF-YOLO, a lightweight two-dimensional tomato detector based on a modified YOLOv26n architecture. The model replaces the P3 backbone downsampling operation with space-to-depth convolution (SPD-Conv) to retain fine-grained spatial information. Its weighted channel-aware fusion (WCAF) neck combines learnable branch weighting with parameter-free three-dimensional attention to refine fused features. Bounding-box regression uses focaler-minimum point distance intersection over union (Focaler-MPDIoU). Across five random seed runs on the internal held-out test subset of a custom single-site orchard dataset, WCAF-YOLO obtained a mean ${\mathrm{mAP}}_{50–95}$ of $0.9048\pm 0.0013$ and a mean recall of $0.9280\pm 0.0019$. The corresponding mean improvements over the YOLOv26n baseline were 2.14 and 3.42 percentage points, respectively. The model contained 2.36 M parameters and required 6.36 GFLOPs. Under the evaluated protocol, the model combined a compact parameter count with higher mean detection metrics than the YOLOv26n baseline. The detector outputs two-dimensional bounding boxes and variety labels for image-level orchard monitoring and variety-level assessment. Integration into agricultural field platforms remains to be validated. Full article

Skeleton-based action recognition has attracted considerable research interest because skeleton data are inherently robust to illumination changes, viewpoint variation, background clutter, and camera motion. Nevertheless, extracting informative representations from skeleton sequences remains a challenging problem, as it requires capturing both the spatial co-occurrence patterns among body joints and the fine-grained kinematic cues that distinguish different actions. In this paper, we propose a single-stream architecture that constructs an action-specific skeleton graph directly from motion and processes it with a kinematic-aware Transformer. Rather than relying on a fixed skeleton topology, a motion-guided dynamic-graph construction module infers a per-frame adjacency matrix from short-term motion cues through a differentiable edge predictor and Gumbel-Softmax sparsification, allowing the model to discover action-driven connections between distant joints that lack direct bone connectivity (e.g., coordinated hand motion during clapping). Each joint is described by kinematic node features that combine its 3D position, instantaneous velocity, and limb-angle encodings within a single descriptor, so that both motion dynamics and higher-order limb configurations are available to the spatial encoder from the outset. A graph-attention network (GAT) encodes the spatial configuration of every frame over the learned graph, and the resulting sequence of frame descriptors is processed by a Transformer encoder that models long-range temporal dependencies; a learnable classification token aggregates the sequence, and a multi-layer perceptron (MLP) produces the final action classification. The entire model is trained end-to-end from action labels alone. We conduct a comprehensive ablation study and evaluate the proposed method on the large-scale NTU RGB+D 60 and NTU RGB+D 120 benchmarks, where the results demonstrate that our approach achieves competitive performance compared to state-of-the-art architectures. Full article

In the era of global energy transition, the development of third-generation photovoltaic technologies, such as dye-sensitized solar cells, has emerged as a paramount challenge in materials engineering. This study is dedicated to the synthesis and implementation of graphene quantum dots as eco-friendly sensitizers within DSSC architectures. The GQDs were synthesized via a microwave-assisted hydrothermal route using biodegradable organic precursors, providing a “green” alternative to conventional, toxic heavy-metal-based materials. The nanocrystalline structure and optoelectronic properties of the sensitizer were verified through UV-Vis and visual photoluminescence assessment. A focal point of this research was the optimization of the GQD concentration on the mesoporous surface of the titanium dioxide photoanode. Measurements were conducted utilizing a custom-designed experimental setup integrated with 3D-printed (FDM) components and an Arduino microcontroller, ensuring precise data acquisition under controlled illumination conditions (405–625 nm). The results indicated an optimal operational point at a fivefold dilution of the stock solution (0.4 g/dm³), which yielded the highest open-circuit voltage ($V_{\mathrm{oc}}$) of 545.4 mV under UV irradiation. The decline in photovoltaic performance observed at higher concentrations was attributed to excessive nanostructure agglomeration, which effectively blocked the mesopores of the semiconductor. Furthermore, the demonstrated high chemical capacitance of the system imparts electrochemical capacitor-like characteristics to the cell, enabling energy stabilization under fluctuating illumination. To elucidate the underlying sensitization mechanisms at the atomic level, computational simulations were conducted utilizing the MACE machine-learning potential and the GFN2-xTB semi-empirical method. The theoretical models revealed that the formation of stable covalent Ti–O–C bridges (chemisorption) is imperative for establishing strong interfacial electronic coupling. Solvation models and molecular dynamics (MD) at 300 K confirmed the thermodynamic and operational robustness of the hybrid system in an aqueous electrolyte. Ultimately, this combined experimental and theoretical work conclusively demonstrates that graphene quantum dots represent an efficient, highly stable, and non-toxic alternative to classic molecular dye sensitizers. Full article

Certified seed purity is a prerequisite for sustaining the agronomic performance and commercial value of Khao Dawk Mali 105 (KDML-105), Thailand’s premium aromatic rice; however, conventional inspection methods are destructive, labour-intensive, and poorly suited to high-throughput operations. This study developed a non-destructive purity inspection system integrating five-band MSI acquired using a MicaSense RedEdge-MX sensor with three machine learning classifiers: Extra Trees (ET), Random Forest (RF), and Support Vector Machine (SVM). The system was evaluated systematically across four illumination levels (360.90–13,188.46 lx) to discriminate KDML-105 from three morphologically similar contaminating varieties: Chainat-1, RD-6, and RD-15. Two-way ANOVA confirmed that classifier type was the dominant performance determinant (η² = 0.847). Under optimal illumination (L4, 13,188.46 lx), ET achieved the highest accuracy (88.8%), recall (86.6%), and F1-score (88.4%) with a training time of 0.098 s. External validation confirmed model generalisability: KDML-105 seeds were identified with 90.6% accuracy and 95.6% recall, while Chainat-1 and RD-6 yielded accuracies of 85.1% and 90.6%, respectively; RD-15 remained challenging (62.9%; 67.4%) owing to spectral proximity to KDML-105. These findings establish MSI combined with ET classification as a viable, cost-effective approach for automated seed purity screening in certified rice production. Full article

Non-human animals are systematically excluded from public narratives, rarely recognized as sentient subjects, and instead framed within anthropocentric narratives of production and consumption; efforts to challenge this pattern and make the more-than-human world visible often encounter editorial resistance and structural constraints. This study examines how anti-speciesist journalists in Spanish written media navigate such resistance to make non-human animals legitimate subjects of news. Drawing on semi-structured interviews with nine self-identified anti-speciesist journalists, analysed through reflexive thematic analysis, the study identifies strategies organized across four non-mutually exclusive categories, namely, discursive, relational, framing, and structural, ranging from self-censorship and strategic language use to coalition-building and the creation of independent platforms. Findings reveal a key distinction between visibility strategies, which succeed in securing publication, and root-level strategies, which challenge the carnist and anthropocentric assumptions structuring newsworthiness itself. These practices reveal the pragmatism, creativity, and resilience of journalists working under structural, ideological, and normative constraints, while also highlighting the limits of anti-speciesist reporting within mainstream media. The study contributes to Critical Animal Media Studies, environmental communication, and journalism research by illuminating the intersection of ethical advocacy, professional strategy, and counter-hegemonic practice and offers recommendations for expanding coverage of the more-than-human world across ideological, editorial, and structural divides. Full article

Low-light conditions degrade remote sensing imagery by reducing contrast, distorting color, and obscuring fine terrain structures and small objects critical for Earth observation. Accurate illumination adjustment under spatially varying scene content remains challenging for existing enhancement methods, and many prior-guided approaches operate exclusively in either the spatial domain or the frequency domain. In this work, we propose a Dual-Domain Illumination Prior (DDIP), a trainable dual-domain illumination-prior module that is jointly optimized with each host backbone and exploits frequency-domain and spatial-domain illumination statistics. DDIP comprises three components: a Frequency-Domain Illumination Distribution Prior (FIDP) that performs per-color-channel amplitude calibration in Fourier space to improve global brightness; a Spatial-Domain Illumination Distribution Prior (SIDP), adapted from IDP-Net, that performs multi-scale sub-region statistical correction for local illumination adjustment; and a Selective Core Feature Fusion (SCFF) module that adaptively combines the frequency-domain output, the spatial-domain output, and the original input through an attention-based gating mechanism with dual pooling. DDIP is integrated with each host backbone while leaving its main restoration blocks unchanged. In the controlled reconstruction comparisons on iSAID-dark and the evaluated general low-light benchmarks, equipping the tested backbone networks with DDIP improves PSNR and SSIM over their corresponding baselines. Complementary LPIPS and CIELAB lightness measurements characterize perceptual similarity and lightness behavior, while a fixed-detector object-detection evaluation on the tested high-resolution iSAID-dark scenes examines the effect of the enhancement pipelines under the reported synthetic low-light conditions. The ablation studies further examine the contribution of the module components within the reported experimental settings. Full article

Seed germination and early survival are important phenotypes for plant breeding and agricultural management, yet they are still commonly assessed through labor-intensive manual stand counting. We present the Maize Seedling Detection Dataset (MSDD), a curated high-resolution red–green–blue (RGB) dataset derived from unmanned aerial vehicle (UAV) imagery collected over the 2019–2022 growing seasons. MSDD contains 3152 images and 163,921 annotated objects across three classes—single (92.47%), double (6.07%), and triple (1.45%) clusters of seedlings—and captures substantial variability in growth stage (V2–V12), illumination, soil appearance, wind, and camera viewpoint. Unlike many existing datasets, MSDD explicitly annotates clustered seedlings as double and triple classes, which are important for stand evaluation. We benchmarked YOLOv9, YOLO11, YOLOv12, and Faster-RCNN on MSDD to evaluate detection accuracy, class-specific performance, inference efficiency, and generalization across field conditions. Single-seedling detection was reliable across models, with the best mean average precision at 0.5 IoU (mAP@0.5) reaching 0.916, whereas double- and triple-seedling detection remained challenging because of class imbalance, occlusion, and annotation ambiguity. Detection was most reliable in high-contrast scenes and declined under wind, strong shadows, and bright soil backgrounds. YOLO11 provided the fastest evaluation throughput among the tested models (≈27 frames per second (fps)), while YOLOv9 achieved the strongest single-seedling detection performance. Synthetic augmentation improved class balance but did not improve generalization to naturally occurring clustered seedlings. Frames, labels, and trained models are available at Google Drive and Hugging Face. MSDD provides a public benchmark for maize seedling detection and for evaluating stand counting models under realistic field conditions. Full article

Early and reliable fire and smoke detection is essential for forest-fire warning and emergency response, especially in UAV-view and complex-background imagery, where small fire spots and diffuse smoke are easily affected by illumination variations and visually similar high-brightness or cloud- and fog-like backgrounds. To address these challenges, this paper formulates early fire and smoke recognition as a bounding-box detection task and proposes a Detail–Scale–Context Detection Network, named DSC-Det. DSC-Det is designed as a lightweight one-stage detection network and introduces three task-oriented components: a Detail–Context Downsampling Module (DCDM) for reducing information loss during early feature compression, a Dynamic Dual-Branch Fusion Module (DDFM) for adaptive multi-scale feature interaction under complex backgrounds, and a Shared-Regression Asymmetric Classification Head (SACH) for improving classification adaptation across feature layers while maintaining shared regression. Experiments on a constructed forest-fire-oriented fire and smoke dataset for UAV-view and complex-background monitoring scenes show that DSC-Det achieves 90.1% $\mathit{mAP}{@}_{0.5}$ and 66.9% $\mathit{mAP}{@}_{0.5:0.95}$, outperforming the lightweight reference detector by 2.3% and 4.4%, respectively. The results demonstrate that DSC-Det improves early forest-fire and smoke detection with controlled model complexity. Full article

Maritime object detection remains challenging because of complex sea-surface backgrounds, adverse illumination conditions, and severe class imbalance, especially when safety-critical targets such as search-and-rescue vessels are sparsely represented. To address these challenges, we propose a cascaded hybrid DINOv2-YOLOv8n detection framework for maritime scenes. Rather than relying only on supervised learning from raw RGB images, the proposed method introduces semantic priors from a frozen DINOv2 encoder and projects them into a compact representation for a YOLOv8n-based detector. To improve robustness under diverse maritime conditions, the framework uses a sea-state-aware online augmentation strategy and is trained with the standard YOLO detection objective. Experiments on the Maritime Target Data Sharing Project (MTDSP) dataset show that the proposed framework achieves strong detection performance. Specifically, it obtains an overall mAP@0.5 of 89.4% and a precision of 100% on the test set. For sparse search-and-rescue vessels and rigid offshore structures, it achieves mAP@0.5 scores of 99.5% and 96.3%, respectively. These results indicate that combining foundation-model semantic priors with a lightweight detector can improve the reliability of maritime object detection under complex sea-surface conditions. Full article

Underwater marine debris detection is important for marine environmental monitoring, robotic inspection, and debris removal. However, reliable detection remains challenging because underwater images often suffer from low illumination, color distortion, turbidity, cluttered backgrounds, and weak boundaries. These factors reduce feature reliability and hinder accurate localization, especially for small, occluded, or low-visibility debris. To address these challenges, this paper proposes UADet, an adaptive detector for marine debris detection in degraded underwater scenes. UADet integrates two complementary components: Underwater Degradation-aware Feature Modulation (UDFM) and Visibility-aware Boundary Distribution Refinement (VBDR). UDFM extracts lightweight image-level degradation cues and modulates multi-scale features to improve robustness under varying underwater conditions. VBDR incorporates object scale and an appearance-based proxy for local visual difficulty into boundary distribution learning and matching cost, providing adaptive localization supervision for small objects and objects with weak visual evidence. Experiments are conducted on TrashCan and J-Litter, and UADet is compared with representative real-time detectors, including YOLOv8s, YOLOv10s, YOLOv11s, and RT-DETR. The results show that UADet achieves the best performance on both datasets, with 72.74% mAP@0.5, 81.36% precision, and 69.36% recall on TrashCan, and 48.02% mAP@0.5, 70.13% precision, and 55.61% recall on J-Litter. Compared with the strongest baseline, UADet improves mAP@0.5 by 3.12 percentage points on TrashCan and 4.83 percentage points on J-Litter. Ablation and qualitative analyses demonstrate that UDFM and VBDR provide complementary improvements. These results indicate that modeling underwater degradation and boundary uncertainty improves the robustness and reliability of marine debris detection in challenging underwater environments. Full article

In complex and unstructured orchard environments, accurate fruit detection is essential for yield estimation and robotic harvesting in precision agriculture. However, single-model detectors often suffer from reduced robustness and high miss rates under drastic illumination changes, severe occlusions, and dense fruit overlap. To address these challenges, this study proposes a multi-model fusion framework for robust mango detection in complex orchard environments. The proposed method employs YOLOv8n, YOLOv8s, and YOLOv8m as base detectors and applies multi-scale test-time augmentation (TTA) to obtain predictions from different augmented views. After mapping the predicted bounding boxes back to the original image coordinate system, predictions corresponding to the same target across different TTA views of each base detector are matched based on the intersection over union (IoU), yielding model-specific prediction results. Weighted Box Fusion (WBF) is then applied to determine the fused bounding-box coordinates. For candidate targets jointly detected by multiple base detectors, the confidence scores provided by the individual models are combined using Noisy-OR to obtain the fused confidence score. Finally, Gaussian Soft-NMS is applied to decay the scores of overlapping candidate boxes, thereby reducing the risk of incorrectly suppressing adjacent mangoes in densely clustered scenes. Experiments on two complementary datasets under within-dataset evaluation protocols demonstrate the effectiveness of the proposed method. On the standard dataset (Data1), Recall and mAP@0.5 reach 95.52% and 98.60%, respectively. Across five repeated random holdout splits of Data2, the proposed framework increased the mean Recall from 82.79% to 84.90% and the mean mAP@0.5 from 90.27% to 91.23%. These results indicate that the proposed framework improves detection robustness and completeness compared with single-model detectors in complex orchard environments, demonstrating its potential for offline yield estimation and orchard phenotyping. Full article