Search Results (2,742)

This study aims to improve the precision of wheat spike counting and disease detection, exploring the application of deep learning in the agricultural sector. Addressing the shortcomings of traditional detection methods, we propose an advanced feature extraction strategy and a model based on the probability density attention mechanism, designed to more effectively handle feature extraction in complex backgrounds and dense areas. Through comparative experiments with various advanced models, we comprehensively evaluate the performance of our model. In the disease detection task, our model performs excellently, achieving a precision of 0.93, a recall of 0.89, an accuracy of 0.91, and an mAP of 0.90. By introducing the density loss function, we are able to effectively improve the detection accuracy when dealing with high-density regions. In the wheat spike counting task, the model similarly demonstrates a strong performance, with a precision of 0.91, a recall of 0.88, an accuracy of 0.90, and an mAP of 0.90, further validating its effectiveness. Furthermore, this paper also conducts ablation experiments on different loss functions. The results of this research provide a new method for wheat spike counting and disease detection, fully reflecting the application value of deep learning in precision agriculture. By combining the probability density attention mechanism and the density loss function, the proposed model significantly improves the detection accuracy and efficiency, offering important references for future related research. Full article

Accurate bud detection is a prerequisite for automatic tea picking and yield statistics; however, current research suffers from missed detection due to the variety of singleness and false detection under complex backgrounds. Traditional target detection models are mainly based on CNN, but CNN can only achieve the extraction of local feature information, which is a lack of advantages for the accurate identification of targets in complex environments, and Transformer can be a good solution to the problem. Therefore, based on a multi-variety tea bud dataset, this study proposes RT-DETR-Tea, an improved object detection model under the real-time detection Transformer (RT-DETR) framework. This model uses cascaded group attention to replace the multi-head self-attention (MHSA) mechanism in the attention-based intra-scale feature interaction (AIFI) module, effectively optimizing deep features and enriching the semantic information of features. The original cross-scale feature-fusion module (CCFM) mechanism is improved to establish the gather-and-distribute-Tea (GD-Tea) mechanism for multi-level feature fusion, which can effectively fuse low-level and high-level semantic information and large and small tea bud features in natural environments. The submodule of DilatedReparamBlock in UniRepLKNet was employed to improve RepC3 to achieve an efficient fusion of tea bud feature information and ensure the accuracy of the detection head. Ablation experiments show that the precision and mean average precision of the proposed RT-DETR-Tea model are 96.1% and 79.7%, respectively, which are increased by 5.2% and 2.4% compared to those of the original model, indicating the model’s effectiveness. The model also shows good detection performance on the newly constructed tea bud dataset. Compared with other detection algorithms, the improved RT-DETR-Tea model demonstrates superior tea bud detection performance, providing effective technical support for smart tea garden management and production. Full article

This study investigates the influence of electromagnetic fields on the propulsion performance of laser plasma propulsion. Based on the principle of pulsed plasma thrusters, an electromagnetic field is utilized to accelerate laser plasma, achieving enhanced propulsion performance. This approach represents a novel method for the electromagnetic enhancement of laser propulsion performance. In this paper, pulsed plasma thrusters induced by laser ablation are employed. The generated plasma is subjected to the Lorentz force under the influence of an electromagnetic field to obtain higher speed, thus increasing impulse and specific impulse. An experimental platform for laser-ablation plasma electromagnetic acceleration was constructed to explore the enhancement effect of discharge characteristics and propulsion performance. The results demonstrate that increased laser energy has little effect on discharge characteristics, while the trend of propulsion performance parameters initially rises and then declines. After coupling the electromagnetic field, the propulsion performance is significantly enhanced, with stronger electromagnetic fields yielding more pronounced effects. Full article

Solving maximum matching problems in bipartite graphs is critical in fields such as computational biology and social network analysis. This study introduces HybridGNN, a novel Graph Neural Network model designed to efficiently address complex matching problems at scale. HybridGNN leverages a combination of Graph Attention Networks (GATv2), Graph SAGE (SAGEConv), and Graph Isomorphism Networks (GIN) layers to enhance computational efficiency and model performance. Through extensive ablation experiments, we identify that while the SAGEConv layer demonstrates suboptimal performance in terms of accuracy and F₁-score, configurations incorporating GATv2 and GIN layers show significant improvements. Specifically, in six-layer GNN architectures, the combinations of GATv2 and GIN layers with ratios of 4:2 and 5:1 yield superior accuracy and F₁-score. Therefore, we name these GNN configurations HybridGNN₁ and HybridGNN₂. Additionally, techniques such as mixed precision training, gradient accumulation, and Jumping Knowledge networks are integrated to further optimize performance. Evaluations on an email communication dataset reveal that HybridGNNs outperform traditional algorithms such as the Hopcroft–Karp algorithm, the Hungarian algorithm, and the Blossom/Edmonds’ algorithm, particularly for large and complex graphs. These findings highlight HybridGNN’s robust capability to solve maximum matching problems in bipartite graphs, making it a powerful tool for analyzing large-scale and intricate graph data. Furthermore, our study aligns with the goals of the Symmetry and Asymmetry Study in Graph Theory special issue by exploring the role of symmetry in bipartite graph structures. By leveraging GNNs, we address the challenges related to symmetry and asymmetry in graph properties, thereby improving the reliability and fault tolerance of complex networks. Full article

Multi-hop reasoning provides a means for inferring indirect relationships and missing information from knowledge graphs (KGs). Reinforcement learning (RL) was recently employed for multi-hop reasoning. Although RL-based methods provide explainability, they face challenges such as sparse rewards, spurious paths, large action spaces, and long training and running times. In this study, we present a novel approach that combines KG embeddings and RL strategies for multi-hop reasoning called path-based multi-hop reasoning (PMHR). We address the issues of sparse rewards and spurious paths by incorporating a well-designed reward function that combines soft rewards with rule-based rewards. The rewards are adjusted based on the target entity and the path to it. Furthermore, we perform action filtering and utilize the vectors of entities and relations acquired through KG embeddings to initialize the environment, thereby significantly reducing the runtime. Experiments involving a comprehensive performance evaluation, efficiency analysis, ablation studies, and a case study were performed. The experimental results on benchmark datasets demonstrate the effectiveness of PMHR in improving KG reasoning accuracy while preserving interpretability. Compared to existing state-of-the-art models, PMHR achieved Hit@1 improvements of 0.63%, 2.02%, and 3.17% on the UMLS, Kinship, and NELL-995 datasets, respectively. PMHR provides not only improved reasoning accuracy and explainability but also optimized computational efficiency, thereby offering a robust solution for multi-hop reasoning. Full article

Dataset distillation has become an important technique for enhancing the efficiency of data when training machine learning models. It finds extensive applications across various fields, including computer vision (CV) and natural language processing (NLP). However, it essentially consists of a deep neural network (DNN) model, which remain susceptible to security and privacy vulnerabilities (e.g., backdoor attacks). Existing studies have primarily focused on optimizing the balance between computational efficiency and model performance, overlooking the accompanying security and privacy risks. This study presents the first backdoor attack targeting NLP models trained on distilled datasets. We introduce malicious triggers into synthetic data during the distillation phase to execute a backdoor attack on downstream models trained with these data. We employ several widely used datasets to assess how different architectures and dataset distillation techniques withstand our attack. The experimental findings reveal that the attack achieves strong performance with a high (above 0.9 and up to 1.0) attack success rate (ASR) in most cases. For backdoor attacks, high attack performance often comes at the cost of reduced model utility. Our attack maintains high ASR while maximizing the preservation of downstream model utility, as evidenced by results showing that the clean test accuracy (CTA) of the backdoored model is very close to that of the clean model. Additionally, we performed comprehensive ablation studies to identify key factors affecting attack performance. We tested our attack method against five defense strategies, including NAD, Neural Cleanse, ONION, SCPD, and RAP. The experimental results show that these defense methods are unable to reduce the attack success rate without compromising the model’s performance on normal tasks. Therefore, these methods cannot effectively defend against our attack. Full article

In this study, an improved stable multi-object simple online and real-time tracking (StableSORT) algorithm that was specifically designed for maritime environments was proposed to address challenges such as camera instability and irregular object motion. Specifically, StableSORT integrates a buffered IoU (B-IoU) and an observation-adaptive Kalman filter (OAKF) into the StrongSORT framework to improve tracking accuracy and robustness. A dataset was collected along the southern coast of Korea using a small autonomous surface vehicle to capture real-world maritime conditions. On this dataset, StableSORT achieved a 2.7% improvement in HOTA, 4.9% in AssA, and 2.6% in IDF1 compared to StrongSORT, and it significantly outperformed ByteTrack and OC-SORT by 84% and 69% in HOTA, respectively. These results underscore StableSORT’s ability to maintain identity consistency and enhance tracking performance under challenging maritime conditions. The ablation studies further validated the contributions of the B-IoU and OAKF modules in maintaining identity consistency and tracking accuracy under challenging maritime conditions. Full article

This paper introduces a novel lightweight pose estimation model, GDE-pose, which addresses the current trade-off between accuracy and computational efficiency in existing models. GDE-pose builds upon the baseline YOLO-pose model by incorporating Ghost Bottleneck, a Dynamic Feature Fusion Module (DFFM), and ECA Attention to achieve more effective feature representation and selection. The Ghost Bottleneck reduces computational complexity, DFFM enhances multi-scale feature fusion, and ECA Attention optimizes the selection of key features. GDE-pose improves pose estimation accuracy while preserving real-time performance. Experimental results demonstrate that GDE-pose achieves higher accuracy on the COCO dataset, with a substantial reduction in parameters, over 80% fewer FLOPs, and an increased inference speed of 31 FPS, underscoring its exceptional lightweight and real-time capabilities. Ablation studies confirm the independent contribution of each module to the model’s overall performance. GDE-pose’s design highlights its broad applicability in real-time pose estimation tasks. Full article

The segmentation and recognition of power lines are crucial for the UAV-based inspection of overhead power lines. To address the issues of class imbalance, low sample quantity, and long-range dependency in images, a specialized semantic segmentation network for power line segmentation called Axial-UNet++ is proposed. Firstly, to tackle the issue of long-range dependencies in images and low sample quantity, a gated axial attention mechanism is introduced to expand the receptive field and improve the capture of relative positional biases in small datasets, thereby proposing a novel feature extraction module termed axial-channel local normalization module. Secondly, to address the imbalance in training samples, a new loss function is developed by combining traditional binary cross-entropy loss with focal loss, enhancing the precision of image semantic segmentation. Lastly, ablation and comparative experiments on the PLDU and Mendeley datasets demonstrate that the proposed model achieves 54.7% IoU and 80.1% recall on the PLDU dataset, and 79.3% IoU and 93.1% recall on the Mendeley dataset, outperforming other listed models. Additionally, robustness experiments show the adaptability of the Axial-UNet++ model under extreme conditions and the augmented image dataset used in this study has been open sourced. Full article

Transition metal dichalcogenides (TMDCs), particularly molybdenum disulfide (MoS₂), have gained significant attention in the field of optoelectronics and photonics due to their unique electronic and optical properties. The integration of TMDCs with plasmonic materials allows to tailor the optical response and offers significant advantages for photonic applications. This study presents a novel approach to synthesize MoS₂-Au nanocomposites utilizing femtosecond laser ablation in liquid to achieve tunable optical properties in the near-infrared (NIR) region. By adjusting ablation and fragmentation protocols, we successfully synthesize various core–shell and core–shell–satellite nanoparticle composites, such as MoS₂/MoS_xO_y, MoS_xO_y/Au, and MoS₂/MoS_xO_y/Au. UV-visible absorption spectroscopy unveils considerable changes in the optical response of the particles depending on the fabrication regime due to structural modifications. Hybrid nanoparticles exhibit enhanced photothermal properties when subjected to NIR-I laser irradiation, demonstrating potential benefits for selective photothermal therapy. Our findings underscore that the engineered nanocomposites not only facilitate green synthesis but also pave the way for tailored therapeutic applications, highlighting their role as promising candidates in the field of nanophotonics and cancer treatment. Full article

The efficient classification of body position is crucial for monitoring infants’ motor development. It may fast-track the early detection of developmental issues related not only to the acquisition of motor milestones but also to postural stability and movement patterns. In turn, this may facilitate and enhance opportunities for early intervention that are crucial for promoting healthy growth and development. The manual classification of human body position based on video recordings is labour-intensive, leading to the adoption of Inertial Motion Unit (IMU) sensors. IMUs measure acceleration, angular velocity, and magnetic field intensity, enabling the automated classification of body position. Many research teams are currently employing supervised machine learning classifiers that utilise hand-crafted features for data segment classification. In this study, we used a longitudinal dataset of IMU recordings made in the lab in three different play activities of infants aged 4–12 months. The classification was conducted based on manually annotated video recordings. We found superior performance of the CatBoost Classifier over the Random Forest Classifier in the task of classifying five positions based on IMU sensor data from infants, yielding excellent classification accuracy of the Supine (97.7%), Sitting (93.5%), and Prone (89.9%) positions. Moreover, using data ablation experiments and analysing the SHAP (SHapley Additive exPlanations) values, the study assessed the importance of various groups of features from both the time and frequency domains. The results highlight that both accelerometer and magnetometer data, especially their statistical characteristics, are critical contributors to improving the accuracy of body position classification. Full article

Trajectory prediction plays a crucial role in autonomous driving tasks, as accurately and rapidly predicting the future trajectories of traffic participants can significantly enhance the safety and robustness of autonomous driving systems. This paper presents a novel trajectory prediction model that follows the encoder–decoder paradigm, achieving precise and rapid predictions of future vehicle trajectories by efficiently aggregating the spatiotemporal and interaction information of agents in traffic scenarios. We propose an agent–agent interaction information extraction module based on a sparse graph attention mechanism, which enables the efficient aggregation of interaction information between agents. Additionally, we introduce a non-autoregressive query generation method that accelerates the model inference speed by generating the decoding queries in parallel. Comparative experiments with the existing advanced algorithms show that our method improves the multimodal trajectory prediction metrics for the Minimum Average Displacement Error (minADE), the Minimum Final Displacement Error (minFDE), and the Miss Rate (MR) by an average of 9.1%, 11.8%, and 14.6%, respectively, while the inference time is only 33.7% of the average time taken by the other algorithms. Finally, we demonstrate the effectiveness of the various modules proposed in this paper through ablation studies. Full article

Male-typical behaviors such as aggression and mating, which reflect sexual libido in male mice, are regulated by the hypothalamus, a crucial part of the nervous system. Previous studies have demonstrated that microRNAs (miRNAs), especially miR-29, play a vital role in reproduction and the neural control of behaviors. However, it remains unclear whether miR-29 affects reproduction through the hypothalamus-mediated regulation of male-typical behaviors. Here, we constructed two mouse knockout models by ablating either the miR-29ab1 or miR-29b2c cluster. Compared to WT, the ablation of miR-29ab1 in male mice significantly reduced the incidence of aggression by 60% and the incidence of mating by 46.15%. Furthermore, the loss of miR-29ab1 in male mice led to the downregulation of androgen receptor (AR) in the ventromedial hypothalamus. Transcriptomic analysis of the hypothalamus of miR-29ab1-deficient mice revealed inflammatory activation and aberrant expression of genes associated with male-typical behaviors, including Ar, Pgr, Htr4, and Htr2c. Using bioinformatics analysis and dual-luciferase reporter assays, we identified zinc finger protein 36 (Zfp36) as a direct downstream target gene of miR-29ab1. We subsequently showed that ZFP36 colocalized with AR in GT1-7 cells. Furthermore, inhibition of Zfp36 or RelB in GT1-7 cells led to an increase in AR expression. Collectively, our results demonstrate that the miR-29ab1/Zfp36/AR axis in the hypothalamus plays a pivotal role in the regulation of aggression and mating in male mice, providing a potential therapeutic target for treating infertility caused by low libido. Full article

Laser–electrochemical hybrid machining (LECM) is promising in the processing of thin-wall parts, which avoids problems such as the weak stiffness of structures and thermal defects. However, while most studies focus on precision machining via LECM, few investigate the potential of this technique in macro-area processing. In this paper, the synergistic effects on the coupling of thermal field and electrochemical field on bulk material removal mechanisms in the LECM of additively manufactured Ti6Al4V are comprehensively analyzed experimentally and theoretically. According to the experimental results, LECM improved the material removal rate (MRR) by up to 28.6% compared to ECM. The induction of the laser increases local heating, accelerating the temperature rise of the electrolyte, eventually promoting the electrochemical reaction. The hydrogen bubble flow promotes overall heat convection between the electrode and workpiece, which facilitates the removal of the facial precipitates and increases the efficiency of electrochemical dissolution. Higher voltages and laser powers promote the formation of hydrogen bubble flow; meanwhile, they also aggravate laser energy scattering, limiting the overall machining efficiency. Additionally, laser irradiation causes the ablation and rupture of hydrogen bubbles, which weakens the bubble flow effect and ultimately decreases the material removal efficiency. This study reveals the underlying mechanisms of the joint effects of the laser field and electrical field in LECM, and the findings can provide valuable insights for the optimization of LECM parameters in industrial applications. Full article

Hepatocellular carcinoma (HCC) is a prevalent malignant tumour worldwide. Depending on the stage of the tumour and liver function, a variety of treatment options are indicated. Traditional radiotherapy and chemotherapy are ineffective against HCC; however, the U.S. Food and Drug Administration (FDA) has approved radiofrequency ablation (RFA), surgical resection, and transarterial chemoembolization (TACE) for advanced HCC. On the other hand, liver transplantation is recommended in the early stages of the disease. Tyrosine kinase inhibitors (TKIs) like lenvatinib and sorafenib, immunotherapy and anti-angiogenesis therapy, including pembrolizumab, bevacizumab, tremelimumab, durvalumab, camrelizumab, and atezolizumab, are other treatment options for advanced HCC. Moreover, to maximize outcomes for patients with HCC, the combination of immune checkpoint inhibitors (ICIs) along with targeted therapies or local ablative therapy is being investigated. This review elaborates on the current status of HCC treatment, outlining the most recent clinical study results and novel approaches. Full article