Ablation study visualization results. حسب Xiaozhou Feng (2918222)

"…Second, a Large Separable Kernel Attention (LSKA) mechanism is incorporated into the Spatial Pyramid Pooling-Fast (SPPF) module of YOLOv8, improving the model’s ability to perceive fine details of diseased trees and reducing interference from other elements in the forest. Finally, the MPDIoU loss function is adopted for bounding box regression, enhancing the precision of localization. …"

Experimental parameter configuration. حسب Xiaozhou Feng (2918222)

"…Second, a Large Separable Kernel Attention (LSKA) mechanism is incorporated into the Spatial Pyramid Pooling-Fast (SPPF) module of YOLOv8, improving the model’s ability to perceive fine details of diseased trees and reducing interference from other elements in the forest. Finally, the MPDIoU loss function is adopted for bounding box regression, enhancing the precision of localization. …"

FLMP-YOLOv8 identification results. حسب Xiaozhou Feng (2918222)

"…Second, a Large Separable Kernel Attention (LSKA) mechanism is incorporated into the Spatial Pyramid Pooling-Fast (SPPF) module of YOLOv8, improving the model’s ability to perceive fine details of diseased trees and reducing interference from other elements in the forest. Finally, the MPDIoU loss function is adopted for bounding box regression, enhancing the precision of localization. …"

C2f structure. حسب Xiaozhou Feng (2918222)

"…Second, a Large Separable Kernel Attention (LSKA) mechanism is incorporated into the Spatial Pyramid Pooling-Fast (SPPF) module of YOLOv8, improving the model’s ability to perceive fine details of diseased trees and reducing interference from other elements in the forest. Finally, the MPDIoU loss function is adopted for bounding box regression, enhancing the precision of localization. …"

Experimental environment configuration. حسب Xiaozhou Feng (2918222)

"…Second, a Large Separable Kernel Attention (LSKA) mechanism is incorporated into the Spatial Pyramid Pooling-Fast (SPPF) module of YOLOv8, improving the model’s ability to perceive fine details of diseased trees and reducing interference from other elements in the forest. Finally, the MPDIoU loss function is adopted for bounding box regression, enhancing the precision of localization. …"

Ablation experiment results table. حسب Xiaozhou Feng (2918222)

"…Second, a Large Separable Kernel Attention (LSKA) mechanism is incorporated into the Spatial Pyramid Pooling-Fast (SPPF) module of YOLOv8, improving the model’s ability to perceive fine details of diseased trees and reducing interference from other elements in the forest. Finally, the MPDIoU loss function is adopted for bounding box regression, enhancing the precision of localization. …"

YOLOv8 identification results. حسب Xiaozhou Feng (2918222)

"…Second, a Large Separable Kernel Attention (LSKA) mechanism is incorporated into the Spatial Pyramid Pooling-Fast (SPPF) module of YOLOv8, improving the model’s ability to perceive fine details of diseased trees and reducing interference from other elements in the forest. Finally, the MPDIoU loss function is adopted for bounding box regression, enhancing the precision of localization. …"

LSKA module structure diagram. حسب Xiaozhou Feng (2918222)

"…Second, a Large Separable Kernel Attention (LSKA) mechanism is incorporated into the Spatial Pyramid Pooling-Fast (SPPF) module of YOLOv8, improving the model’s ability to perceive fine details of diseased trees and reducing interference from other elements in the forest. Finally, the MPDIoU loss function is adopted for bounding box regression, enhancing the precision of localization. …"

Comparison of mAP curves in ablation experiments. حسب Xiaozhou Feng (2918222)

"…Second, a Large Separable Kernel Attention (LSKA) mechanism is incorporated into the Spatial Pyramid Pooling-Fast (SPPF) module of YOLOv8, improving the model’s ability to perceive fine details of diseased trees and reducing interference from other elements in the forest. Finally, the MPDIoU loss function is adopted for bounding box regression, enhancing the precision of localization. …"

FarsterBlock structure. حسب Xiaozhou Feng (2918222)

"…Second, a Large Separable Kernel Attention (LSKA) mechanism is incorporated into the Spatial Pyramid Pooling-Fast (SPPF) module of YOLOv8, improving the model’s ability to perceive fine details of diseased trees and reducing interference from other elements in the forest. Finally, the MPDIoU loss function is adopted for bounding box regression, enhancing the precision of localization. …"

Sample augmentation and annotation illustration. حسب Xiaozhou Feng (2918222)

"…Second, a Large Separable Kernel Attention (LSKA) mechanism is incorporated into the Spatial Pyramid Pooling-Fast (SPPF) module of YOLOv8, improving the model’s ability to perceive fine details of diseased trees and reducing interference from other elements in the forest. Finally, the MPDIoU loss function is adopted for bounding box regression, enhancing the precision of localization. …"

YOLOv8 model architecture diagram. حسب Xiaozhou Feng (2918222)

"…Second, a Large Separable Kernel Attention (LSKA) mechanism is incorporated into the Spatial Pyramid Pooling-Fast (SPPF) module of YOLOv8, improving the model’s ability to perceive fine details of diseased trees and reducing interference from other elements in the forest. Finally, the MPDIoU loss function is adopted for bounding box regression, enhancing the precision of localization. …"

FLMP-YOLOv8 architecture diagram. حسب Xiaozhou Feng (2918222)

"…Second, a Large Separable Kernel Attention (LSKA) mechanism is incorporated into the Spatial Pyramid Pooling-Fast (SPPF) module of YOLOv8, improving the model’s ability to perceive fine details of diseased trees and reducing interference from other elements in the forest. Finally, the MPDIoU loss function is adopted for bounding box regression, enhancing the precision of localization. …"

Image 4_Multidisciplinary analysis of the prognosis and biological function of NUBPL in gastric cancer.tif حسب Luqian Liu (10814985)

"…</p>Materials and method<p>The gastric cancer transcriptomic data were retrieved from TCGA database, and disulfidptosis-related genes were identified through literature search. …"

Image 3_Multidisciplinary analysis of the prognosis and biological function of NUBPL in gastric cancer.tif حسب Luqian Liu (10814985)

"…</p>Materials and method<p>The gastric cancer transcriptomic data were retrieved from TCGA database, and disulfidptosis-related genes were identified through literature search. …"

Image 5_Multidisciplinary analysis of the prognosis and biological function of NUBPL in gastric cancer.tif حسب Luqian Liu (10814985)

"…</p>Materials and method<p>The gastric cancer transcriptomic data were retrieved from TCGA database, and disulfidptosis-related genes were identified through literature search. …"

Image 6_Multidisciplinary analysis of the prognosis and biological function of NUBPL in gastric cancer.tif حسب Luqian Liu (10814985)

"…</p>Materials and method<p>The gastric cancer transcriptomic data were retrieved from TCGA database, and disulfidptosis-related genes were identified through literature search. …"

Image 1_Multidisciplinary analysis of the prognosis and biological function of NUBPL in gastric cancer.tif حسب Luqian Liu (10814985)

"…</p>Materials and method<p>The gastric cancer transcriptomic data were retrieved from TCGA database, and disulfidptosis-related genes were identified through literature search. …"

Image 2_Multidisciplinary analysis of the prognosis and biological function of NUBPL in gastric cancer.tif حسب Luqian Liu (10814985)

"…</p>Materials and method<p>The gastric cancer transcriptomic data were retrieved from TCGA database, and disulfidptosis-related genes were identified through literature search. …"

Video 1_Development and control of a robotic assistant walking aid for fall risk reduction.mp4 حسب Marcel Naderer (22434454)

"…To enable effective and reliable control in the real system, actuator dynamics are characterized through an optimization-based system identification approach, resulting in transfer function models with over 98% accuracy. Based on these models, PID controllers are optimally tuned using an optimization algorithm to ensure fast and accurate corrective action. …"