Search Results (832)

In response to the challenges of detecting rice pests and diseases at different scales and the difficulties associated with deploying and running models on embedded devices with limited computational resources, this study proposes a multi-scale rice pest and disease recognition model (RGC-YOLO). Based on the YOLOv8n network, which includes an SPPF layer, the model introduces a structural reparameterization module (RepGhost) to achieve implicit feature reuse through reparameterization. GhostConv layers replace some standard convolutions, reducing the model’s computational cost and improving inference speed. A Hybrid Attention Module (CBAM) is incorporated into the backbone network to enhance the model’s ability to extract important features. The RGC-YOLO model is evaluated for accuracy and inference time on a multi-scale rice pest and disease dataset, including bacterial blight, rice blast, brown spot, and rice planthopper. Experimental results show that RGC-YOLO achieves a precision (P) of 86.2%, a recall (R) of 90.8%, and a mean average precision at Intersection over Union 0.5(mAP50) of 93.2%. In terms of model size, the parameters are reduced by 33.2%, and GFLOPs decrease by 29.27% compared to the base YOLOv8n model. Finally, the RGC-YOLO model is deployed on an embedded Jetson Nano device, where the inference time per image is reduced by 21.3% compared to the base YOLOv8n model, reaching 170 milliseconds. This study develops a multi-scale rice pest and disease recognition model, which is successfully deployed on embedded field devices, achieving high-accuracy real-time monitoring and providing valuable reference for intelligent equipment in unmanned farms. Full article

Background/Objectives: To simplify diagnosing congenital and acquired nystagmus using fundus photographs. Methods: A retrospective study included patients with congenital or childhood-acquired nystagmus examined at a hospital-based ophthalmology clinic (September 2020–September 2023) with fundus photos taken. Exclusions were for incomplete data or low-quality images. Demographics, aetiology, orthoptic measurements, and ophthalmologic and neurological exams were reviewed. Two independent physicians graded fundus photos based on amplitude (distance between “ghost” images), the number of images visible, and the direction of nystagmus. Severity was rated on a 0–3 scale using qualitative and quantitative methods. Photographic findings were compared to clinical data, and statistical analysis used Mann-Whitney tests. Results: A total of 53 eyes from 29 patients (16 females, 13 males; mean age 12.5 years, range 3–65) were studied: 25 with binocular nystagmus and 3 with monocular nystagmus. Diagnoses included congenital (n = 15), latent-manifest (n = 3), neurologically associated (n = 2), and idiopathic (n = 9). Types observed were vertical (n = 5), horizontal (n = 23), rotatory (n = 10), and multidirectional (n = 15). Visual acuity ranged from 20/20 to no light perception. Fundus photos correlated with clinical diagnoses, aiding qualitative assessment of direction and amplitude and mitigating eye movement effects for clearer retinal detail visualization. Conclusions: Fundus photography effectively captures nystagmus characteristics and retinal details, even in young children, despite continuous eye movements. Integrating fundus cameras into routine practice may enhance nystagmus diagnosis and management, improving patient outcomes. Full article

With the development of aerospace technology, the variety and complexity of spacecraft components have increased. Traditional manual and machine learning-based detection methods struggle to accurately and quickly identify these parts. Deep learning-based object detection networks require significant computational resources and high hardware requirements. This study introduces Ghost SCYLLA Intersection over Union You Only Look Once (GS-YOLO), an improved image recognition model derived from YOLOv5s, which integrates the global attention mechanism (GAM) with the Ghost module. The lightweight Ghost module substitutes the original convolutional layers, producing half of the features via convolution and the other half by symmetric linear operations. This minimizes the computing burden and model parameters by effectively acquiring superfluous feature layers. A more lightweight SimSPPF structure is created to supplant the old spatial pyramid pooling—fast (SPPF), enhancing the network speed. The GAM is included in the bottleneck architecture, improving feature extraction via channel–space interaction. The experimental results on the custom-made precision component dataset show that GS-YOLO achieves an accuracy of 96.5% with a model size of 10.8 MB. Compared to YOLOv5s, GS-YOLO improves accuracy by 1%, reduces parameters by 23%, and decreases computational requirements by 40.6%. Despite the model’s light weight, its detection accuracy has been improved. Full article

The writings of Franz Kafka open, perhaps precisely because of their temporal distance to our present, a unique window onto the nexus of power, material, and the human that constitutes AI today. Anxiety and Unbehagen [discontent] are states of mind that often grip both Kafka and his characters in an early-20th-century world increasingly dependent upon and perceived through the lens of disembodied communication and technology. But can we draw a line from Kafka’s reflections on analog media to the digital media that have come to dominate our lives in the 21st century, and whose effects are felt on a planetary scale? The short answer is “yes”. In Kafka’s analog world of technological horrors, glitches in the machinic administration of human life turn out to be not bugs, but rather features of the system; precisely the arbitrary effects that accompany the rigid implementation of rules and the slippages that occur during their merciless application enhance the power of the system as a whole. Kafka’s apparatuses and bureaucratic systems, in their powerful and toxic confluence of regularity and opacity, systematicity and arbitrariness, foreshadow the effects of AI upon our embodied existence in the 21st century. Full article

In order to enhance the precision of UAV (unmanned aerial vehicle) landings and realize the convenient and rapid deployment of the model to the mobile terminal, this study proposes a Land-YOLO lightweight UAV-guided landing algorithm based on the YOLOv8 n model. Firstly, GhostConv replaces standard convolutions in the backbone network, leveraging existing feature maps to create additional “ghost” feature maps via low-cost linear transformations, thereby lightening the network structure. Additionally, the CSP structure of the neck network is enhanced by incorporating the PartialConv structure. This integration allows for the transmission of certain channel characteristics through identity mapping, effectively reducing both the number of parameters and the computational load of the model. Finally, the bidirectional feature pyramid network (BiFPN) module is introduced, and the accuracy and average accuracy of the model recognition landing mark are improved through the bidirectional feature fusion and weighted fusion mechanism. The experimental results show that for the landing-sign data sets collected in real and virtual environments, the Land-YOLO algorithm in this paper is 1.4% higher in precision and 0.91% higher in mAP0.5 than the original YOLOv8n baseline, which can meet the detection requirements of landing signs. The model’s memory usage and floating-point operations per second (FLOPs) have been reduced by 42.8% and 32.4%, respectively. This makes it more suitable for deployment on the mobile terminal of a UAV. Full article

Small-object detection in satellite remote sensing images plays a pivotal role in the field of remote sensing. Achieving high-performance real-time detection demands not only efficient algorithms but also low-power, high-performance hardware platforms. However, most mainstream target detection methods currently rely on graphics processing units (GPUs) for acceleration, and the high power consumption of GPUs limits their use in resource-constrained platforms such as small satellites. Moreover, small-object detection faces multiple challenges: the targets occupy only a small number of pixels in the image, the background is often complex with significant noise interference, and existing detection models typically exhibit low accuracy when dealing with small targets. In addition, the large number of parameters in these models makes direct deployment on embedded devices difficult. To address these issues, we propose a hybrid overlapping acceleration architecture based on FPGA, along with a lightweight model derived from YOLOv5s that is specifically designed to enhance the detection of small objects in remote sensing images. This model incorporates a lightweight GhostBottleneckV2 module, significantly reducing both model parameters and computational complexity. Experimental results on the TIFAD thermal infrared small-object dataset show that our approach achieves an average precision (mAP) of 67.8% while consuming an average power of only 2.8 W. The robustness of the proposed model is verified by the HRSID dataset. Combining real-time performance with high energy efficiency, this architecture is particularly well suited for on-board remote sensing image processing systems, where reliable and efficient small-object detection is paramount. Full article

In view of the problems that mean that existing detection networks are not effective in detecting dynamic targets such as wildfire smoke, a lightweight dynamically enhanced transmission line channel wildfire smoke detection network LDENet is proposed. Firstly, a Dynamic Lightweight Conv Module (DLCM) is devised within the backbone network of YOLOv8 to enhance the perception of flames and smoke through dynamic convolution. Then, the Ghost Module is used to lightweight the model. DLCM reduces the number of model parameters and improves the accuracy of wildfire smoke detection. Then, the DySample upsampling operator is used in the upsampling part to make the image generation more accurate with very few parameters. Finally, in the course of the training process, the loss function is improved. EMASlideLoss is used to improve detection ability for small targets, and the Shape-IoU loss function is used to optimize the shape of wildfires and smoke. Experiments are conducted on wildfire and smoke datasets, and the final mAP50 is 86.6%, which is 1.5% higher than YOLOv8, and the number of parameters is decreased by 29.7%. The experimental findings demonstrate that LDENet is capable of effectively detecting wildfire smoke and ensuring the safety of transmission line corridors. Full article

In the textile quality control system, textile defect detection occupies a central position. In order to solve the problems of numerous model parameters, time-consuming computation, limited precision, and accuracy of tiny features of textile defects in the defect detection process, this paper proposes a textile defect detection method based on the YOLO-GCW network model. First, in order to solve the problem of detection accuracy of tiny defective targets, the CBAM (Convolutional Block Attention Module) attention mechanism was incorporated to guide the model to focus more on the spatial localization information of the defects. Meanwhile, the WIoU (Weighted Intersection over Union) loss function was adopted to enhance model training as well as to improve the detection accuracy, which can also provide a more accurate measure of match between the model-predicted bounding box and the real target to improve the detection capability of tiny defect targets. Consequently, in view of the need for performance optimization and lightweight deployment, the Ghost convolution structure was adopted to replace the traditional convolution for compressing the model parameter scale and promoting the detection speed of complex texture features in textiles. Finally, numerous experiments proved the positive performance of the presented model and demonstrated its efficiency and effectiveness in various scenes. Full article

Background/Objectives: We generated a novel recombinant Vibrio cholerae ghost (rVCG)-based subunit vaccine incorporating the A1 subunit of cholera toxin (CTA1) and a multiepitope Chlamydia trachomatis (CT) antigen (MECA) derived from five chlamydial outer membrane proteins (rVCG-MECA). The ability of this vaccine to protect against a CT transcervical challenge was evaluated. Methods: Female C57BL/6J mice were immunized thrice at two-week intervals with rVCG-MECA or rVCG-gD2 (antigen control) via the intramuscular (IM) or intranasal (IN) route. PBS-immunized mice or mice immunized with live CT served as negative and positive controls, respectively. Results: Vaccine delivery stimulated robust humoral and cell-mediated immune effectors, characterized by local mucosal and systemic CT-specific IgG, IgG2c, and IgA antibody and IFN-γ (Th1 cytokine) responses. The elicited mucosal and systemic IgG2c and IgA antibody responses persisted for 16 weeks post-immunization. Immunization with rVCG-MECA afforded protection comparable to that provided by IN immunization with live CT EBs without any side effects, irrespective of route of vaccine delivery. Conclusions: The results underline the potential of a multiepitope vaccine as a promising resource for protecting against CT genital infection and the potential of CTA1 on the VCG platform as a mucosal and systemic adjuvant for developing CT vaccines. Full article

Flow around clustered cylinders is widely encountered in engineering applications such as wind energy systems, pipeline transport, and marine engineering. To investigate the hydrodynamic performance and vortex dynamics of multiple cylinders under forced vibration at low Reynolds numbers, with a focus on understanding the interference characteristics in various configurations, this study is based on a self-developed radial basis function iso-surface ghost cell computing platform, which improves the implicit iso-surface interface representation method to track the moving boundaries of multiple cylinders, and employs a self-constructed CPU/GPU heterogeneous parallel acceleration technique for efficient numerical simulations. This study systematically investigates the interference characteristics of multiple cylinder configurations across various parameter domains, including spacing ratios, geometric arrangements, and oscillation modes. A quantitative analysis of key parameters, such as aerodynamic coefficients, dimensionless frequency characteristics, and vorticity field evolution, is performed. This study reveals that, for a dual-cylinder system, there exists a critical gap ratio between X/D = 2.5 and 3, which leads to an increase in the lift and drag coefficients of both cylinders, a reduction in the vortex shedding periodicity, and a disruption of the wake structure. For a three-cylinder system, the lift and drag coefficients of the two upstream cylinders decrease with increasing spacing. On the other hand, this increased spacing results in a rise in the drag of the downstream cylinder. In the case of a four-cylinder system, the drag coefficients of the cylinders located on either side of the flow direction are relatively high. A significant increase in the lift coefficient occurs when the spacing ratio is less than 2.0, while the drag coefficient of the downstream cylinder is minimized. The findings establish a comprehensive theoretical framework for the optimal configuration design and structural optimization of multicylinder systems, while also providing practical guidelines for engineering applications. Full article

Bleeding is a rare but serious complication of pancreatitis, significantly increasing morbidity and mortality. It can arise from various sources, including erosion of blood vessels by inflammatory processes, formation of pseudoaneurysms, and gastrointestinal bleeding. Early diagnosis and timely intervention are crucial for patient survival. Imaging modalities such as computed tomography and angiography are essential for identifying the bleeding source, where endoscopy may help in detecting and treating intraluminal hemorrhage. Management strategies for patients with extraluminal bleeding may involve angioembolization or surgical intervention, depending on the severity and location of the bleeding. While advances in diagnostic and therapeutic techniques have improved outcomes, bleeding in pancreatitis remains a challenging clinical problem requiring a multidisciplinary approach. This review aims to focus its attention specifically on the bleeding complications of pancreatitis. Full article

Due to the large number of parameters and high computational complexity of current target detection models, it is challenging to perform fast and accurate target detection in side-scan sonar images under the existing technical conditions, especially in environments with limited computational resources. Moreover, since the original waterfall map of side-scan sonar only consists of echo intensity information, which is usually of a large size, it is difficult to fuse it with other multi-source information, which limits the detection accuracy of models. To address these issues, we designed DBnet, a lightweight target detector featuring two lightweight backbone networks (PP-LCNet and GhostNet) and a streamlined neck structure for feature extraction and fusion. To solve the problem of unbalanced aspect ratios in sonar data waterfall maps, DBnet employs the SAHI algorithm with sliding-window slicing inference to improve small-target detection accuracy. Compared with the baseline model, DBnet has 33% fewer parameters and 31% fewer GFLOPs while maintaining accuracy. Tests performed on two datasets (SSUTD and SCTD) showed that the mAP values improved by 2.3% and 6.6%. Full article

Background/Objectives Bacterial ghosts (BGs), non-living empty envelopes of bacteria, are produced either through genetic engineering or chemical treatment of bacteria, retaining the shape of their parent cells. BGs are considered vaccine candidates, promising delivery systems, and vaccine adjuvants. The practical use of BGs in vaccine development for humans is limited because of concerns about the preservation of viable bacteria in BGs. Methods: To increase the efficiency of Klebsiella pneumoniae BG formation and, accordingly, to ensure maximum killing of bacteria, we exploited previously designed plasmids with the lysis gene E from bacteriophage φX174 or with holin–endolysin systems of λ or L-413C phages. Previously, this kit made it possible to generate bacterial cells of Yersinia pestis with varying degrees of hydrolysis and variable protective activity. Results: In the current study, we showed that co-expression of the holin and endolysin genes from the L-413C phage elicited more rapid and efficient K. pneumoniae lysis than lysis mediated by only single gene E or the low functioning holin–endolysin system of λ phage. The introduction of alternative lysing factors into K. pneumoniae cells instead of the E protein leads to the loss of the murein skeleton. The resulting frameless cell envelops are more reminiscent of bacterial sacs or bacterial skins than BGs. Although such structures are less naive than classical bacterial ghosts, they provide effective protection against infection by a hypervirulent strain of K. pneumoniae and can be recommended as candidate vaccines. For our vaccine candidate generated using the O1:K2 hypervirulent K. pneumoniae strain, both safety and immunogenicity aspects were evaluated. Humoral and cellular immune responses were significantly increased in mice that were intraperitoneally immunized compared with subcutaneously vaccinated animals (p < 0.05). Conclusions: Therefore, this study presents novel perspectives for future research on K. pneumoniae ghost vaccines. Full article

The growing body of research on children’s understanding of extraordinary minds has demonstrated that children believe in the persistence of mental functioning after death. However, beyond the continuity of mind, the supernatural conception of death often involves the concept of the disembodied mind, which transcends the constraints of the physical body, possessing supernatural mental capacities. The current study investigated whether children differentiate between a dead agent’s mind and ordinary minds in terms of their perceptual and information-updating capacities. In a location-change false-belief task, which involved a story of a mouse protagonist that was either eaten by an alligator or not, 4- to 6-year-old Korean children (N = 114) were asked about the mental states of the protagonist, an ordinary adult (mom), and God. The results showed (1) older children’s tendency to respond in a way that differentiated (the living) mom from the dead protagonist, (2) an increasing trend of differentiating God’s super-knowingness from ordinary minds with age, and (3) inconclusive evidence regarding children’s differential responses to the dead versus living protagonist. This study suggests that children are not predisposed to view dead agents as possessing a disembodied and supernatural mind, highlighting the importance of cultural learning in the development of such religious concepts. Full article

Seed quality testing is crucial for ensuring food security and stability. To accurately detect the germination status of corn seeds during the paper medium germination test, this study proposes a corn seed germination status detection model based on YOLO v8n (CSGD-YOLO). Initially, to alleviate the complexity encountered in conventional models, a lightweight spatial pyramid pooling fast (L-SPPF) structure is engineered to enhance the representation of features. Simultaneously, a detection module dubbed Ghost_Detection, leveraging the GhostConv architecture, is devised to boost detection efficiency while simultaneously reducing parameter counts and computational overhead. Additionally, during the downsampling process of the backbone network, a downsampling module based on receptive field attention convolution (RFAConv) is designed to boost the model’s focus on areas of interest. This study further proposes a new module named C2f-UIB-iAFF based on the faster implementation of cross-stage partial bottleneck with two convolutions (C2f), universal inverted bottleneck (UIB), and iterative attention feature fusion (iAFF) to replace the original C2f in YOLOv8, streamlining model complexity and augmenting the feature fusion prowess of the residual structure. Experiments conducted on the collected corn seed germination dataset show that CSGD-YOLO requires only 1.91 M parameters and 5.21 G floating-point operations (FLOPs). The detection precision(P), recall(R), mAP_0.5, and mAP_0.50:0.95 achieved are 89.44%, 88.82%, 92.99%, and 80.38%. Compared with the YOLO v8n, CSGD-YOLO improves performance in terms of accuracy, model size, parameter number, and floating-point operation counts by 1.39, 1.43, 1.77, and 2.95 percentage points, respectively. Therefore, CSGD-YOLO outperforms existing mainstream target detection models in detection performance and model complexity, making it suitable for detecting corn seed germination status and providing a reference for rapid germination rate detection. Full article