Search Results (10,872)

Understory-cultivated Panax ginseng possesses high pharmacological and economic value; however, its visual quality grading predominantly relies on subjective manual assessment, constraining industrial scalability. To address challenges including fine-grained morphological variations, boundary ambiguity, and complex natural backgrounds, this study proposes Ginseng-YOLO, a lightweight and deployment-friendly object detection model for automated ginseng grade classification. The model is built on the YOLOv11n (You Only Look Once11n) framework and integrates three complementary components: (1) C2-LWA, a cross-stage local window attention module that enhances discrimination of key visual features, such as primary root contours and fibrous textures; (2) ADown, a non-parametric downsampling mechanism that substitutes convolution operations with parallel pooling, markedly reducing computational complexity; and (3) Slide Loss, a piecewise IoU-weighted loss function designed to emphasize learning from samples with ambiguous or irregular boundaries. Experimental results on a curated multi-grade ginseng dataset indicate that Ginseng-YOLO achieves a Precision of 84.9%, a Recall of 83.9%, and an mAP@50 of 88.7%, outperforming YOLOv11n and other state-of-the-art variants. The model maintains a compact footprint, with 2.0 M parameters, 5.3 GFLOPs, and 4.6 MB model size, supporting real-time deployment on edge devices. Ablation studies further confirm the synergistic contributions of the proposed modules in enhancing feature representation, architectural efficiency, and training robustness. Successful deployment on the NVIDIA Jetson Nano demonstrates practical real-time inference capability under limited computational resources. This work provides a scalable approach for intelligent grading of forest-grown ginseng and offers methodological insights for the design of lightweight models in medicinal plants and agricultural applications. Full article

Non-cooperative spatial target detection plays a vital role in enabling autonomous on-orbit servicing and maintaining space situational awareness (SSA). However, due to the limited computational resources of onboard embedded systems and the complexity of spaceborne imaging environments, where spacecraft images often contain small targets that are easily obscured by background noise and characterized by low local information entropy, many existing object detection frameworks struggle to achieve high accuracy with low computational cost. To address this challenge, we propose YOLO-GRBI, an enhanced detection network designed to balance accuracy and efficiency. A reparameterized ELAN backbone is adopted to improve feature reuse and facilitate gradient propagation. The BiFormer and C2f-iAFF modules are introduced to enhance attention to salient targets, reducing false positives and false negatives. GSConv and VoV-GSCSP modules are integrated into the neck to reduce convolution operations and computational redundancy while preserving information entropy. YOLO-GRBI employs the focal loss for classification and confidence prediction to address class imbalance. Experiments on a self-constructed spacecraft dataset show that YOLO-GRBI outperforms the baseline YOLOv8n, achieving a 4.9% increase in mAP@0.5 and a 6.0% boost in mAP@0.5:0.95, while further reducing model complexity and inference latency. Full article

Space-based on-orbit servicing missions impose stringent requirements for precise identification and localization of satellite components, while existing detection algorithms face dual challenges of insufficient accuracy and excessive computational resource consumption. This paper proposes SLD-YOLO, a lightweight satellite component detection model based on improved YOLO11, balancing accuracy and efficiency through structural optimization and lightweight design. First, we design RLNet, a lightweight backbone network that employs reparameterization mechanisms and hierarchical feature fusion strategies to reduce model complexity by 19.72% while maintaining detection accuracy. Second, we propose the CSP-HSF multi-scale feature fusion module, used in conjunction with PSConv downsampling, to effectively improve the model’s perception of multi-scale objects. Finally, we introduce SimAM, a parameter-free attention mechanism in the detection head to further improve feature representation capability. Experiments on the UESD dataset demonstrate that SLD-YOLO achieves measurable improvements compared to the baseline YOLO11s model across five satellite component detection categories: mAP50 increases by 2.22% to 87.44%, mAP50:95 improves by 1.72% to 63.25%, while computational complexity decreases by 19.72%, parameter count reduces by 25.93%, model file size compresses by 24.59%, and inference speed reaches 90.4 FPS. Validation experiments on the UESD_edition2 dataset further confirm the model’s robustness. This research provides an effective solution for target detection tasks in resource-constrained space environments, demonstrating practical engineering application value. Full article

Asphalt concrete (ascon) manufacturing facilities in South Korea are located near urban areas and emit various air pollutants, including polycyclic aromatic hydrocarbons (PAHs) such as benzo(a)pyrene (BaP), a Group 1 carcinogen. However, few measurement-based studies exist in Korea, and no domestic BaP emission factor has been established, making its effective management difficult. In this study, PAH concentrations emitted from stacks were measured using gas chromatography/mass spectrometry at 29 facilities located near densely populated areas. BaP was detected at all facilities, and emission factors were calculated based on the ascon materials and dryer fuel types. The calculated emission factors were found to be 31 to 6230 times higher than the AP-42 standards provided by the US Environmental Protection Agency. This discrepancy likely arises from differences between processes and fuel characteristics. Using the California Puff model, BaP concentrations in the near area were predicted, corresponding to as much as 30% of the US National Ambient Air Quality Standards. These findings indicate a potentially significant environmental health risk in nearby communities. The findings of this study can serve as foundational data for formulating policies and providing institutional support aimed at managing emissions from ascon manufacturing facilities in Korea. Full article

This review aims to point out the main achievements in the cutting-edge field of aptamer nanotechnology and its applications in the most frequent neuro-oncological and neurodegenerative diseases. The article discusses the properties, advantages and drawbacks of aptamers (AP), and their design and selection by various SELEX methods, as well as the synergical advantages as theranostics of the aptamer-functionalized nanoparticles (Ap-NP). The Ap-nanoconjugates properties are compared to those of Ap and unconjugated NP. Moreover, the article comparatively analyzes the aptamer-based approaches vs. antibody-drug conjugates vs. exosome-based delivery systems vs. unconjugated NP, as targeted therapies in neurodegenerative diseases and gliomas. The review presents major challenges in Ap-NP conjugates’ clinical progress (concerning the in vivo enzymatic stability, blood–brain barrier (BBB) permeability, selective intracellular uptake in the brain parenchyma and target tissues, rapid renal clearance, off-target toxicity, immunogenicity, reproductible manufacturing) and the investigated developmental strategies to solve them. Furthermore, relevant examples and comparative insights regarding preclinically tested Ap and Ap-NP conjugates are presented for targeted delivery systems loaded with chemotherapeutical drugs or genes, Ap-siRNA chimeras and immunotherapeutical aptamers, which are evaluated in glioblastomas (GBM), amyloidogenic diseases and multiple sclerosis (MS); radiotherapy enhancers in GBM; aptasensors for diagnostic and bioimaging-guided therapy in GBM, MS and amyloidopathies. The review finally points out future research directions in order to accelerate the clinical translation and the real-world impact as theranostics of the most preclinically advanced Ap-NP conjugates in major neuro-oncological and neurodegenerative disorders. Full article

Individual recognition of Hu sheep is a core requirement for precision livestock management, significantly improving breeding efficiency and fine management. However, traditional machine vision methods face challenges such as high annotation time costs, the inability to quickly annotate new sheep, and the need for manual intervention and retraining. To address these issues, this study proposes a solution that integrates automatic annotation and transfer learning, developing a sheep face recognition algorithm that adapts to complex farming environments and can quickly learn the characteristics of new Hu sheep individuals. First, through multi-view video collection and data augmentation, a dataset consisting of 82 Hu sheep and a total of 6055 images was created. Additionally, a sheep face detection and automatic annotation algorithm was designed, reducing the annotation time per image to 0.014 min compared to traditional manual annotation. Next, the YOLOv10n-CF-Lite model is proposed, which improved the recognition precision of Hu sheep faces to 92.3%, and the mAP@0.5 to 96.2%. To enhance the model’s adaptability and generalization ability for new sheep, transfer learning was applied to transfer the YOLOv10n-CF-Lite model trained on the source domain (82 Hu sheep) to the target domain (10 new Hu sheep). The recognition precision in the target domain increased from 91.2% to 94.9%, and the mAP@0.5 improved from 96.3% to 97%. Additionally, the model’s convergence speed was improved, reducing the number of training epochs required for fitting from 43 to 14. In summary, the Hu sheep face recognition algorithm proposed in this study improves annotation efficiency, recognition precision, and convergence speed through automatic annotation and transfer learning. It can quickly adapt to the characteristics of new sheep individuals, providing an efficient and reliable technical solution for the intelligent management of livestock. Full article

Accurate identification of the endometrial region is critical for the early detection of endometrial lesions. However, current detection models still face two major challenges when processing endometrial imaging data: (1) In complex and noisy environments, recognition accuracy remains limited, partly due to the insufficient exploitation of color information within the images; (2) Traditional Two-dimensional PCA-based (2DPCA-based) feature selection methods have limited capacity to capture and represent key characteristics of the endometrial region. To address these challenges, this paper proposes a novel algorithm named Feature-Level Image Fusion and Improved Swarm Intelligence Optimization Algorithm (FLFSI), which integrates a learning guided binary particle swarm optimization (BPSO) strategy with an image feature selection and reconstruction framework to enhance the detection of endometrial regions in clinical ultrasound images. Specifically, FLFSI contributes to improving feature selection accuracy and image reconstruction quality, thereby enhancing the overall performance of region recognition tasks. First, we enhance endometrial image representation by incorporating feature engineering techniques that combine structural and color information, thereby improving reconstruction quality and emphasizing critical regional features. Second, the BPSO algorithm is introduced into the feature selection stage, improving the accuracy of feature selection and its global search ability while effectively reducing the impact of redundant features. Furthermore, we refined the BPSO design to accelerate convergence and enhance optimization efficiency during the selection process. The proposed FLFSI algorithm can be integrated into mainstream detection models such as YOLO11 and YOLOv12. When applied to YOLO11, FLFSI achieves 96.6% Box mAP and 87.8% Mask mAP. With YOLOv12, it further improves the Mask mAP to 88.8%, demonstrating excellent cross-model adaptability and robust detection performance. Extensive experimental results validate the effectiveness and broad applicability of FLFSI in enhancing endometrial region detection for clinical ultrasound image analysis. Full article

Chimpanzees (Pan troglodytes) rescued from the illegal wildlife trade often suffer from chronic, traumatic injuries that require specialized and prolonged medical treatment in wildlife rehabilitation centers. We present the case report of a two-year-old male chimpanzee admitted at the Tchimpounga Chimpanzee Rehabilitation Center in the Republic of Congo with a chronic periorbital abscess, likely caused by a machete wound sustained during the poaching of his mother. Despite receiving extended antimicrobial therapy, his condition was never fully controlled and progressed to a chronic orbital infection, causing him discomfort and producing chronic purulent discharge. Enucleation was performed under general anesthesia using ketamine and medetomidine, with surgical approach adapted to the distinctive orbital anatomy of chimpanzees. During the procedure, ligation of the optic nerve and ophthalmic vessels was required due to the confined orbital apex and extensive vascularization, ensuring adequate haemostasias and procedural safety. The chimpanzee made an uneventful postoperative recovery, resuming normal feeding and social behavior within 48 h, with complete wound healing occurring within two weeks. This case report highlights the importance of prompt surgical intervention when conservative medical management fails to resolve refractory ocular infections in chimpanzees. It also emphasizes the importance of specific anesthetic protocols, refined surgical techniques and tailored postoperative care in wildlife rehabilitation centers. Documenting and sharing detailed case reports such as this contributes to the limited veterinary literature on great ape surgery and supports evidence-based clinical decision-making to improve the welfare and treatment outcomes of rescued chimpanzees. Full article

The ecological interface between grasslands and farmlands forms a critical landscape component, significantly contributing to the stability and functioning of ecosystems within the agro-pastoral transition zone of northern China. Nevertheless, the variation patterns and interactions between soil physicochemical attributes and microbial community diversity at this interface remain poorly understood. In this study, we investigated nine sites located within 50 m of the grassland–farmland boundary in the Songnen Plain, northeastern China. We assessed the soil’s physicochemical properties and the composition of bacterial and fungal communities across these sites. Results indicated a declining gradient in soil physicochemical characteristics from grassland to farmland, except for pH and total phosphorus (TP). The composition of bacterial and fungal communities differed notably in response to contrasting land-use types across the ecological interface. Soil environmental variables were closely aligned with shifts observed in bacterial and fungal assemblages. Concentrations of total nitrogen (TN), available phosphorus (AP), alkali-hydrolyzable nitrogen (AN), and available potassium (AK) exhibited inverse correlations with both bacterial and fungal populations. Alterations in microbial community composition were significantly linked to TN, TP, total potassium (TK), AN, AP, AK, and soil pH levels. Variability in soil properties, as well as microbial biomass and diversity, was evident across the grassland–cropland boundary. Long-term utilization and conversion of grassland into cultivated land altered the soil’s physicochemical environment, thereby indirectly shaping the structure of microbial communities, including both bacteria and fungi. These findings provide a valuable basis for understanding the ecological implications of land-use transitions and inform microbial-based indicators for assessing soil health in agro-pastoral ecotones. Full article

In recent years, solar energy has emerged as a pillar of sustainable development. However, maintaining panel efficiency under extreme environmental conditions remains a persistent hurdle. This study introduces an automated defect detection pipeline that leverages deep learning and computer vision to identify five standard anomaly classes: Non-Defective, Dust, Defective, Physical Damage, and Snow on photovoltaic surfaces. To build a robust foundation, a heterogeneous dataset of 8973 images was sourced from public repositories and standardized into a uniform labeling scheme. This dataset was then expanded through an aggressive augmentation strategy, including flips, rotations, zooms, and noise injections. A YOLOv11-based model was trained and fine-tuned using both fixed and adaptive learning rate schedules, achieving a mAP@0.5 of 85% and accuracy, recall, and F1-score above 95% when evaluated across diverse lighting and dust scenarios. The optimized model is integrated into an interactive dashboard that processes live camera streams, issues real-time alerts upon defect detection, and supports proactive maintenance scheduling. Comparative evaluations highlight the superiority of this approach over manual inspections and earlier YOLO versions in both precision and inference speed, making it well suited for deployment on edge devices. Automating visual inspection not only reduces labor costs and operational downtime but also enhances the longevity of solar installations. By offering a scalable solution for continuous monitoring, this work contributes to improving the reliability and cost-effectiveness of large-scale solar energy systems. Full article

Efficient and explainable vehicle damage inspection is essential due to the increasing complexity and volume of vehicular incidents. Traditional manual inspection approaches are not time-effective, prone to human error, and lead to inefficiencies in insurance claims and repair workflows. Existing deep learning methods, such as CNNs, often struggle with generalization, require large annotated datasets, and lack interpretability. This study presents a robust and interpretable deep learning framework for vehicle damage classification, integrating Vision Transformers (ViTs) and ensemble detection strategies. The proposed architecture employs a RetinaNet backbone with a ViT-enhanced detection head, implemented in PyTorch using the Detectron2 object detection technique. It is pretrained on COCO weights and fine-tuned through focal loss and aggressive augmentation techniques to improve generalization under real-world damage variability. The proposed system applies the Weighted Box Fusion (WBF) ensemble strategy to refine detection outputs from multiple models, offering improved spatial precision. To ensure interpretability and transparency, we adopt numerous explainability techniques—Grad-CAM, Grad-CAM++, and SHAP—offering semantic and visual insights into model decisions. A custom vehicle damage dataset with 4500 images has been built, consisting of approximately 60% curated images collected through targeted web scraping and crawling covering various damage types (such as bumper dents, panel scratches, and frontal impacts), along with 40% COCO dataset images to support model generalization. Comparative evaluations show that Hybrid ViT-RetinaNet achieves superior performance with an F1-score of 84.6%, mAP of 87.2%, and 22 FPS inference speed. In an ablation analysis, WBF, augmentation, transfer learning, and focal loss significantly improve performance, with focal loss increasing F1 by 6.3% for underrepresented classes and COCO pretraining boosting mAP by 8.7%. Additional architectural comparisons demonstrate that our full hybrid configuration not only maintains competitive accuracy but also achieves up to 150 FPS, making it well suited for real-time use cases. Robustness tests under challenging conditions, including real-world visual disturbances (smoke, fire, motion blur, varying lighting, and occlusions) and artificial noise (Gaussian; salt-and-pepper), confirm the model’s generalization ability. This work contributes a scalable, explainable, and high-performance solution for real-world vehicle damage diagnostics. Full article

Early traffic accident risk prediction in complex road environments poses significant challenges due to the heterogeneous nature and incomplete semantic alignment of multimodal data. To address this, we propose a novel Early Accident Risk Cross-modal Collaborative Perception Mechanism Network (EAR-CCPM-Net) that integrates hierarchical fusion modules and cross-modal attention mechanisms to enable semantic interaction between visual, motion, and textual modalities. The model is trained and evaluated on the newly constructed CAP-DATA dataset, incorporating advanced preprocessing techniques such as bilateral filtering and a rigorous MINI-Train-Test sampling protocol. Experimental results show that EAR-CCPM-Net achieves an AUC of 0.853, AP of 0.758, and improves the Time-to-Accident (TTA_0.5) from 3.927 s to 4.225 s, significantly outperforming baseline methods. These findings demonstrate that EAR-CCPM-Net effectively enhances early-stage semantic perception and prediction accuracy, providing an interpretable solution for real-world traffic risk anticipation. Full article

Marine benthic organism detection (BOD) is essential for underwater robotics and seabed resource management but suffers from motion blur, perspective distortion, and background clutter in dynamic underwater environments. To address visual feature degradation and computational constraints, we, in this paper, introduce EMR-YOLO, a deep learning based multi-scale BOD method. To handle the diverse sizes and morphologies of benthic organisms, we propose an Efficient Detection Sparse Head (EDSHead), which combines a unified attention mechanism and dynamic sparse operators to enhance spatial modeling. For robust feature extraction under resource limitations, we design a lightweight Multi-Branch Fusion Downsampling (MBFDown) module that utilizes cross-stage feature fusion and multi-branch architecture to capture rich gradient information. Additionally, a Regional Two-Level Routing Attention (RTRA) mechanism is developed to mitigate background noise and sharpen focus on target regions. The experimental results demonstrate that EMR-YOLO achieves improvements of 2.33%, 1.50%, and 4.12% in AP, AP₅₀, and AP₇₅, respectively, outperforming state-of-the-art methods while maintaining efficiency. Full article

Repeated UV exposure, air pollution, and toxins promote skin oxidative stress. ROS destroy macromolecules, changing cellular mechanisms and signaling cascades. Inflammation and injury to skin cells degrade function and accelerate aging, causing wrinkles, firmness loss, and dermatological disorders. Gymnema inodorum (GI) contains phytochemical antioxidants such polyphenols and triterpenoids that lower ROS and strengthen skin. GI extracts (GIEs) have never been examined for their effects on dermal skin fibroblasts’ oxidative stress and intracellular cytoprotective mechanisms. In this study, GIEs were prepared as a water extract (GIE0) and ethanol extracts with concentrations ranging from 20% to 95% v/v (GIE20, GIE40, GIE60, GIE80, and GIE95). These extracts were assessed for phytochemical content, antioxidant capacity, and free radical scavenging efficacy. The results were compared to a commercially available native Gymnema extract (NGE) obtained from Gymnema sylvestre. During principal component analysis (PCA), the most effective extracts were identified and subsequently evaluated for their ability to mitigate oxidative stress in fibroblasts. Cytoprotective effects of GIE and NGE against H₂O₂-induced human dermal fibroblast injury were investigated by cell viability, intracellular ROS production, and signaling pathways. GIE0, GIE80, GIE95, and NGE were the best antioxidants. By preserving ROS balance and redox homeostasis, GIE and NGE reduce fibroblast inflammation and oxidative stress-induced damage. Decreased ROS levels reduce MAPK/AP-1/NF-κB and PI3K/AKT/NF-κB signaling pathways, diminishing inflammatory cytokines. In conclusion, GIE and NGE have antioxidant and anti-inflammatory capabilities that can reduce H₂O₂-induced fibroblast oxidative stress and damage, thereby preventing skin aging and targeting cancer-associated fibroblasts. Full article

Foreign object intrusion in railway perimeter areas poses significant risks to train operation safety. To address the limitation of current visual detection technologies that overly focus on target identification while lacking quantitative risk assessment, this paper proposes a railway intrusion risk quantification method integrating track semantic segmentation and spatiotemporal features. An improved BiSeNetV2 network is employed to accurately extract track regions, while physical-constrained risk zones are constructed based on railway structure gauge standards. The lateral spatial distance of intruding objects is precisely calculated using track gauge prior knowledge. A lightweight detection architecture is designed, adopting ShuffleNetV2 as the backbone to reduce computational complexity, with an incorporated Dilated Transformer module to enhance global context awareness and sparse feature extraction, significantly improving detection accuracy for small-scale objects. The comprehensive risk assessment formula integrates object category weights, lateral risk coefficients in intrusion zones, longitudinal distance decay factors, and dynamic velocity compensation. Experimental results demonstrate that the proposed method achieves 84.9% mean average precision (mAP) on our proprietary dataset, outperforming baseline models by 3.3%. By combining lateral distance detection with multidimensional risk indicators, the method enables quantitative intrusion risk assessment and graded early warning, providing data-driven decision support for active train protection systems and substantially enhancing intelligent safety protection capabilities. Full article