Search Results (5,052)

Accurate and real-time detection of rice shelling rate (SR) and brown rice breakage rate (BR) is crucial for intelligent hulling sorting but remains challenging because of small grain size, dense adhesion, and uneven illumination causing missed detections and blurred boundaries in traditional YOLOv8n. This paper proposes a high-precision, lightweight solution based on an enhanced YOLOv8n with improvements in network architecture, feature fusion, and attention mechanism. The backbone’s C2f module is replaced with C2f-Faster-CGLU, integrating partial convolution (PConv) local convolution and convolutional gated linear unit (CGLU) gating to reduce computational redundancy via sparse interaction and enhance small-target feature extraction. A bidirectional feature pyramid network (BiFPN) weights multiscale feature fusion to improve edge positioning accuracy of dense grains. Attention mechanism for fine-grained classification (AFGC) is embedded to focus on texture and damage details, enhancing adaptability to light fluctuations. The Detect_Rice lightweight head compresses parameters via group normalization and dynamic convolution sharing, optimizing small-target response. The improved model achieved 96.8% precision and 96.2% mAP. Combined with a quantity–mass model, SR/BR detection errors reduced to 1.11% and 1.24%, meeting national standard (GB/T 29898-2013) requirements, providing an effective real-time solution for intelligent hulling sorting. Full article

The scattering interaction between a circularly polarized Bessel pincer light-sheet beam and a chiral particle is investigated within the framework of generalized Lorenz–Mie theory (GLMT). The incident electric field distribution is rigorously derived via the vector angular spectrum decomposition method (VASDM), with subsequent determination of the beam-shape coefficients (BSCs) $p_{mn}^u$ and $q_{mn}^u$ through multipole expansion in the basis of vector spherical wave functions (VSWFs). The expansion coefficients for the scattered field ($A_{mn}^s$$B_{mn}^s$) and interior field ($A_{mn}$$B_{mn}$) are derived by imposing boundary conditions. Simulations highlight notable variations in the scattering field, near-surface field distribution, and far-field intensity, strongly influenced by the dimensionless size parameter $ka$, chirality $\kappa$, and beam parameters (beam order l and beam scaling parameter ${\alpha }_0$). These findings provide insights into the role of chirality in modulating scattering asymmetry and localization effects. The results are particularly relevant for applications in optical manipulation and super-resolution imaging in single-molecule microbiology. Full article

Apomixis-mediated fixation of heterosis could transform hybrid breeding in alfalfa (Medicago sativa), a globally important forage crop. The parthenogenesis-inducing morphogenetic regulator BABY BOOM (BBM) represents a promising candidate for enabling this advancement. Here, we identified BBM homologs from three alfalfa genomes, characterized their promoter regions, and cloned a 2082 bp MsBBM gene encoding a 694-amino acid nuclear-localized protein. Three alfalfa BBM gene promoters primarily contained light- and hormone-responsive elements. Phylogenetic and conserved domain analyses of the MsBBM protein revealed a high sequence similarity with M. truncatula BBM. Expression profiling demonstrated tissue-specific accumulation of MsBBM transcripts, with the highest expression in the roots and developing pods. Hormonal treatments differentially regulated MsBBM. Expression was upregulated by GA₃ (except at 4 h) and SA, downregulated by NAA, MeJA (both except at 8 h), and ABA (except at 4 h), while ETH treatment induced a transient expression peak at 2 h. As an AP2/ERF family transcription factor showing preferential expression in young embryos, MsBBM likely participates in reproductive development and may facilitate apomixis. These findings establish a molecular framework for exploiting MsBBM to enhance alfalfa breeding efficiency through heterosis fixation. Full article

Underwater image enhancement (UIE) technology plays a vital role in marine resource exploration, environmental monitoring, and underwater archaeology. However, due to the absorption and scattering of light in underwater environments, images often suffer from blurred details, color distortion, and low contrast, which seriously affect the usability of underwater images. To address the above limitations, a lightweight transformer-based model (LITM) is proposed for improving underwater degraded images. Firstly, our proposed method utilizes a lightweight RGB transformer enhancer (LRTE) that uses efficient channel attention blocks to capture local detail features in the RGB domain. Subsequently, a lightweight HSV transformer encoder (LHTE) is utilized to extract global brightness, color, and saturation from the hue–saturation–value (HSV) domain. Finally, we propose a multi-modal integration block (MMIB) to effectively fuse enhanced information from the RGB and HSV pathways, as well as the input image. Our proposed LITM method significantly outperforms state-of-the-art methods, achieving a peak signal-to-noise ratio (PSNR) of 26.70 and a structural similarity index (SSIM) of 0.9405 on the LSUI dataset. Furthermore, the designed method also exhibits good generality and adaptability on unpaired datasets. Full article

Owing to the intricate variability of underwater environments, images suffer from degradation including light absorption, scattering, and color distortion. However, U-Net architectures severely limit global context utilization due to fixed-receptive-field convolutions, while traditional attention mechanisms incur quadratic complexity and fail to efficiently fuse spatial–frequency features. Unlike local enhancement-focused methods, HMENet integrates a transformer sub-network for long-range dependency modeling and dual-domain attention for bidirectional spatial–frequency fusion. This design increases the receptive field while maintaining linear complexity. On UIEB and EUVP datasets, HMENet achieves PSNR/SSIM of 25.96/0.946 and 27.92/0.927, surpassing HCLR-Net by 0.97 dB/1.88 dB, respectively. Full article

Rice lodging severely affects crop growth, yield, and mechanized harvesting efficiency. The accurate detection and quantification of lodging areas are crucial for precision agriculture and timely field management. However, Unmanned Aerial Vehicle (UAV)-based lodging detection faces challenges such as complex backgrounds, variable lighting, and irregular lodging patterns. To address these issues, this study proposes SWRD–YOLO, a lightweight instance segmentation model that enhances feature extraction and fusion using advanced convolution and attention mechanisms. The model employs an optimized loss function to improve localization accuracy, achieving precise lodging area segmentation. Additionally, a grid-based lodging ratio estimation method is introduced, dividing images into fixed-size grids to calculate local lodging proportions and aggregate them for robust overall severity assessment. Evaluated on a self-built rice lodging dataset, the model achieves 94.8% precision, 88.2% recall, 93.3% mAP@0.5, and 91.4% F1 score, with real-time inference at 16.15 FPS on an embedded NVIDIA Jetson Orin NX device. Compared to the baseline YOLOv8n-seg, precision, recall, mAP@0.5, and F1 score improved by 8.2%, 16.5%, 12.8%, and 12.8%, respectively. These results confirm the model’s effectiveness and potential for deployment in intelligent crop monitoring and sustainable agriculture. Full article

This work presents the development of a functional real-time SLAM system designed to enhance the perception capabilities of an Automated Guided Vehicle (AGV) using only a 2D LiDAR sensor. The proposal aims to address recurring gaps in the literature, such as the need for low-complexity solutions that are independent of auxiliary sensors and capable of operating on embedded platforms with limited computational resources. The system integrates scan alignment techniques based on the Iterative Closest Point (ICP) algorithm. Experimental validation in a controlled environment indicated better performance using Gauss–Newton optimization and the point-to-plane metric, achieving pose estimation accuracy of 99.42%, 99.6%, and 99.99% in the position ($x$, $y$) and orientation ($\theta$) components, respectively. Subsequently, the system was adapted for operation with data from the onboard sensor, integrating a lightweight graphical interface for real-time visualization of scans, estimated pose, and the evolving map. Despite the moderate update rate, the system proved effective for robotic applications, enabling coherent localization and progressive environment mapping. The modular architecture developed allows for future extensions such as trajectory planning and control. The proposed solution provides a robust and adaptable foundation for mobile platforms, with potential applications in industrial automation, academic research, and education in mobile robotics. Full article

Mid-infrared passive spectroscopic imaging is a novel non-invasive and remote sensing method based on Planck’s law. It enables the acquisition of component-specific information from the human body by measuring naturally emitted thermal radiation in the mid-infrared region. Unlike active methods that require an external light source, our passive approach harnesses the body’s own emission, thereby enabling safe, long-term monitoring. In this study, we successfully demonstrated the simultaneous, non-invasive measurements of blood glucose and lactate levels of the human body using this method. The measurements, conducted over approximately 80 min, provided emittance data derived from mid-infrared passive spectroscopy that showed a temporal correlation with values obtained using conventional blood collection sensors. Furthermore, to evaluate localized metabolic changes, we performed k-means clustering analysis of the spectral data obtained from the upper arm. This enabled visualization of time-dependent lactate responses with spatial resolution. These results demonstrate the feasibility of multi-component monitoring without physical contact or biological sampling. The proposed technique holds promise for translation to medical diagnostics, continuous health monitoring, and sports medicine, in addition to facilitating the development of next-generation healthcare technologies. Full article

As the effective founder of Daoism’s Southern School, Bai Yuchan’s travels played a pivotal role in the sect’s early dissemination. Through a close analysis of his poems, prose, and letters, this study reconstructs the key itineraries and motivations of Bai Yuchan’s travels and examines how his itinerant practices shaped the early dissemination of the Southern School. His travels were divided into two phases: a pre-1212 period of Dao-seeking and a post-1216 phase of Dao-spreading, with the impetus for his later journeys arising from resolving internal alchemical cultivation dilemmas, which in reality, inaugurated his career of traveling to spread the Dao. Bai Yuchan established and disseminated the Southern School through sojourns and revisitations in various regions, with karmic opportunity (jiyuan 機緣) largely dictating the selection of sojourn locations during his journeys. Rooted in the Daoist philosophy of harmony, Bai Yuchan adhered to the principle of blending with the mundane while harmonizing one’s light (hunsu heguang 混俗和光) in his travels and interactions, maintaining active engagement within regional areas to foster harmonious relationships with local communities. This explains why Bai Yuchan was able to achieve the widespread dissemination of the Southern School through his itinerant activities over a short period. Full article

To address the challenges of low recognition accuracy and substantial model complexity in crop disease identification models operating in complex field environments, this study proposed a novel hybrid model named ConvTransNet-S, which integrates Convolutional Neural Networks (CNNs) and transformers for crop disease identification tasks. Unlike existing hybrid approaches, ConvTransNet-S uniquely introduces three key innovations: First, a Local Perception Unit (LPU) and Lightweight Multi-Head Self-Attention (LMHSA) modules were introduced to synergistically enhance the extraction of fine-grained plant disease details and model global dependency relationships, respectively. Second, an Inverted Residual Feed-Forward Network (IRFFN) was employed to optimize the feature propagation path, thereby enhancing the model’s robustness against interferences such as lighting variations and leaf occlusions. This novel combination of a LPU, LMHSA, and an IRFFN achieves a dynamic equilibrium between local texture perception and global context modeling—effectively resolving the trade-offs inherent in standalone CNNs or transformers. Finally, through a phased architecture design, efficient fusion of multi-scale disease features is achieved, which enhances feature discriminability while reducing model complexity. The experimental results indicated that ConvTransNet-S achieved a recognition accuracy of 98.85% on the PlantVillage public dataset. This model operates with only 25.14 million parameters, a computational load of 3.762 GFLOPs, and an inference time of 7.56 ms. Testing on a self-built in-field complex scene dataset comprising 10,441 images revealed that ConvTransNet-S achieved an accuracy of 88.53%, which represents improvements of 14.22%, 2.75%, and 0.34% over EfficientNetV2, Vision Transformer, and Swin Transformer, respectively. Furthermore, the ConvTransNet-S model achieved up to 14.22% higher disease recognition accuracy under complex background conditions while reducing the parameter count by 46.8%. This confirms that its unique multi-scale feature mechanism can effectively distinguish disease from background features, providing a novel technical approach for disease diagnosis in complex agricultural scenarios and demonstrating significant application value for intelligent agricultural management. Full article

Ghost imaging (GI) facilitates image acquisition under low-light conditions through single pixel measurements, thus holding tremendous potential across various fields such as biomedical imaging, remote sensing, defense and military applications, and 3D imaging. However, in order to reconstruct high-resolution images, GI typically requires a large number of single-pixel measurements, which imposes practical limitations on its application. Parallel ghost imaging addresses this issue by utilizing each pixel of a position-sensitive detector as a bucket detector to simultaneously perform tens of thousands of ghost imaging measurements in parallel. In this work, we explore the non-local characteristics of ghost imaging in depth, and by constructing a large speckle space, we achieve a reconstruction result in parallel ghost imaging where the field of view surpasses the limitations of the reference arm detector. Using a computational ghost imaging framework, after pre-recording the speckle patterns, we are able to complete X-ray ghost imaging at a speed of 6 min per sample, with image dimensions of 14,000 × 10,000 pixels (4.55 mm × 3.25 mm, millimeter-scale field of view) and a pixel resolution of 0.325 µm (sub-micron pixel resolution). We present this framework to enhance efficiency, extend resolution, and dramatically expand the field of view, with the aim of providing a solution for the practical implementation of ghost imaging. Full article

The genus Lysionotus belongs to the family Gesneriaceae and includes plants with both ornamental and medicinal value. However, genomic-level data on the genus remains scarce. Previous investigations of Lysionotus have predominantly centered on morphological classification, with only limited exploration of molecular phylogenetics. Comparative analysis of chloroplast genomes within the genus would provide valuable insights into the genetic variations and evolutionary patterns of Lysionotus plants. In this study, we present the analysis of 24 newly sequenced chloroplast genomes from Lysionotus-related members, including widely distributed and locally distributed species. The results showed that the 11 plastome sizes of widely distributed species ranged from 152,928 to 153,987 bp, with GC content of 37.43–37.49%; the 13 plastome sizes of locally distributed species ranged from 153,436 to 153,916 bp, with GC content of 37.43–37.48%. A total of 24 chloroplast genomes owned typical quadripartite structures, and the number of tRNA (36 tRNAs) and rRNA (4 rRNAs) were observed for all 24 genomes. However, the number of their protein-coding sequences (CDs) varied at individual levels. No contraction and expansion of IR borders, gene rearrangements, or inversions were detected. mVISTA and Pi showed inverted repeats (IR) region was more conserved than the single copy region, coding region was more conserved than the non-coding region. Additionally, the repeat sequences and codon usage bias of Lysionotus plastomes were also conserved. Our results offer a comprehensive understanding of the genetic differences among these species and shed light on their phylogenetic systematics. Full article

Collaboration between autonomous vehicles and drones can enhance the efficiency and connectivity of three-dimensional transportation systems. When satellite signals are unavailable, vehicles can achieve accurate localization by matching rich ground environmental data to digital maps, simultaneously providing the auxiliary localization information for drones. However, conventional digital maps suffer from high construction costs, easy misalignment, and low localization accuracy. Thus, this paper proposes a visual point cloud map (VPCM) construction and matching localization for autonomous vehicles. We fuse multi-source information from vehicle-mounted sensors and the regional road network to establish the geographically high-precision VPCM. In the absence of satellite signals, we segment the prior VPCM on the road network based on real-time localization results, which accelerates matching speed and reduces mismatch probability. Simultaneously, by continuously introducing matching constraints of real-time point cloud and prior VPCM through improved iterative closest point matching method, the proposed solution can effectively suppress the drift error of the odometry and output accurate fusion localization results based on pose graph optimization theory. The experiments carried out on the KITTI datasets demonstrate the effectiveness of the proposed method, which can autonomously construct the high-precision prior VPCM. The localization strategy achieves sub-meter accuracy and reduces the average error per frame by 25.84% compared to similar methods. Subsequently, this method’s reusability and localization robustness under light condition changes and environment changes are verified using the campus dataset. Compared to the similar camera-based method, the matching success rate increased by 21.15%, and the average localization error decreased by 62.39%. Full article

As a plant-specific peroxidase family, class III peroxidase (PRX) plays an important role in plant growth, development, and stress response. In this study, a preliminary functional analysis of CqPRX9L1 was conducted. Bioinformatics analysis revealed that CqPRX9L1 encodes a 349-amino acid protein belonging to the plant-peroxidase-like superfamily, featuring a transmembrane domain and cytoplasmic localization. The promoter region of CqPRX9L1 harbors various cis-acting elements associated with stress responses, hormone signaling, light regulation, and meristem-specific expression. The tissue-specific expression pattern of the CqPRX9L1 gene and its characteristics in response to different stresses were explored using subcellular localization, quantitative real-time PCR (qRT-PCR), and heterologous transformation into Arabidopsis thaliana. The results showed that CqPRX9L1, with a transmembrane structure, was localized in the cytoplasm, which encodes 349 amino acids and belongs to the plant-peroxisome-like superfamily. The promoter region contains stress-response elements, hormone-response elements, light-response elements, and meristem expression-related elements. The expression of CqPRX9L1 was relatively higher in ears and roots at the panicle stage than in stems and leaves. CqPRX9L1 showed a dynamic expression pattern of first decreasing and then increasing under abiotic stresses such as 15% PEG 6000, low temperature, and salt damage, with differences in response time and degree. CqPRX9L1 plays an important role in response to abiotic stress by affecting the activity of antioxidant enzymes such as superoxide dismutase (SOD) and peroxidase (POD), as well as the synthesis and decomposition of proline (Pro). CqPRX9L1 also affects plant bolting and flowering by regulating key flowering genes (such as FT and AP1) and gibberellin (GA)-related pathways. The results establish a foundation for revealing the functions and molecular mechanisms of the CqPRX9L1 gene. Full article

We have conducted a first-time investigation into the multiferroic properties and band gap behavior of ${\mathrm{CuFeO}}_2$ doped with Al, Ga, and In ions at the Fe site, employing a microscopic model and Green’s function formalism. The tunability of the band gap across a broad energy spectrum highlights the potential of perovskite materials for advanced applications, including photovoltaics, photodetectors, lasers, light-emitting diodes, and high-energy particle sensors. The disparity in ionic radii between the dopant and host ions introduces local lattice distortions, leading to modifications in the exchange interaction parameters. As a result, the influence of ion doping on various properties of ${\mathrm{CuFeO}}_2$ has been elucidated at microscopic level. Our findings indicate that Al doping enhances magnetization and reduces the band gap energy. In contrast, doping with Ga or In results in a decrease in magnetization and an increase in band gap energy. Additionally, it is demonstrated that ferroelectric polarization can be induced either via external magnetic fields or by Al substitution at the Fe site. The theoretical results show good qualitative agreement with experimental data, confirming the validity of the proposed model and method. Full article