mAP0.5 Curves of various models. by Yaojun Zhang (389482)

“…Results on a specialized dataset reveal that YOLOv8n-BWG outperforms YOLOv8n by increasing the mean Average Precision (mAP) by 4.2%, boosting recognition speed by 21.3% per second, and decreasing both the number of floating-point operations (FLOPs) by 28.9% and model size by 26.3%. …”

Network loss function change diagram. by Yaojun Zhang (389482)

“…Results on a specialized dataset reveal that YOLOv8n-BWG outperforms YOLOv8n by increasing the mean Average Precision (mAP) by 4.2%, boosting recognition speed by 21.3% per second, and decreasing both the number of floating-point operations (FLOPs) by 28.9% and model size by 26.3%. …”

Comparative diagrams of different indicators. by Yaojun Zhang (389482)

“…Results on a specialized dataset reveal that YOLOv8n-BWG outperforms YOLOv8n by increasing the mean Average Precision (mAP) by 4.2%, boosting recognition speed by 21.3% per second, and decreasing both the number of floating-point operations (FLOPs) by 28.9% and model size by 26.3%. …”

YOLOv8n structure diagram. by Yaojun Zhang (389482)

“…Results on a specialized dataset reveal that YOLOv8n-BWG outperforms YOLOv8n by increasing the mean Average Precision (mAP) by 4.2%, boosting recognition speed by 21.3% per second, and decreasing both the number of floating-point operations (FLOPs) by 28.9% and model size by 26.3%. …”

Geometric model of the binocular system. by Yaojun Zhang (389482)

“…Results on a specialized dataset reveal that YOLOv8n-BWG outperforms YOLOv8n by increasing the mean Average Precision (mAP) by 4.2%, boosting recognition speed by 21.3% per second, and decreasing both the number of floating-point operations (FLOPs) by 28.9% and model size by 26.3%. …”

Enhanced dataset sample images. by Yaojun Zhang (389482)

“…Results on a specialized dataset reveal that YOLOv8n-BWG outperforms YOLOv8n by increasing the mean Average Precision (mAP) by 4.2%, boosting recognition speed by 21.3% per second, and decreasing both the number of floating-point operations (FLOPs) by 28.9% and model size by 26.3%. …”

Knockdown of <i>fhplk1</i> disrupts growth and cell proliferation in juvenile <i>Fasciola hepatica in vitro.</i> by Paul McCusker (634425)

“…<b>(C)</b> Mean # EdU⁺ nuclei ±SEM significantly decreased after four weeks of <i>fhplk1</i> dsRNA treatments in juvenile <i>F. hepatica</i> (n ≥ 15 for each treatment; Mann-Whitney U test). …”

Effect of depuration period on the retention of NPs in <i>C</i>. <i>hippurus</i> following trophic transfer exposure for various durations. by Preyojon Dey (20308215)

Variation of shear stress during the pullout of a single fiber with respect to the shear strain [41]. by Ping Gao (172038)

Value ranges of three representative points. by Bin Zhao (276445)

“…<div><p>Capturing congestion propagation among different facilities at intersections in dynamic stochastic traffic environments poses significant challenges, particularly under oversaturated conditions. …”

Signalized intersection in Kunshan. by Bin Zhao (276445)

“…<div><p>Capturing congestion propagation among different facilities at intersections in dynamic stochastic traffic environments poses significant challenges, particularly under oversaturated conditions. …”

Dynamic system state in demand scenarios 2. by Bin Zhao (276445)

“…<div><p>Capturing congestion propagation among different facilities at intersections in dynamic stochastic traffic environments poses significant challenges, particularly under oversaturated conditions. …”

Survey data of the intersection. by Bin Zhao (276445)

“…<div><p>Capturing congestion propagation among different facilities at intersections in dynamic stochastic traffic environments poses significant challenges, particularly under oversaturated conditions. …”

The main notations used in this paper. by Bin Zhao (276445)

“…<div><p>Capturing congestion propagation among different facilities at intersections in dynamic stochastic traffic environments poses significant challenges, particularly under oversaturated conditions. …”

Feedback elimination for feedback queue. by Bin Zhao (276445)

“…<div><p>Capturing congestion propagation among different facilities at intersections in dynamic stochastic traffic environments poses significant challenges, particularly under oversaturated conditions. …”

A typical cross signalized intersection. by Bin Zhao (276445)

“…<div><p>Capturing congestion propagation among different facilities at intersections in dynamic stochastic traffic environments poses significant challenges, particularly under oversaturated conditions. …”

Four signal stages for the intersection. by Bin Zhao (276445)

“…<div><p>Capturing congestion propagation among different facilities at intersections in dynamic stochastic traffic environments poses significant challenges, particularly under oversaturated conditions. …”

Dynamic system state in demand scenarios 3. by Bin Zhao (276445)

“…<div><p>Capturing congestion propagation among different facilities at intersections in dynamic stochastic traffic environments poses significant challenges, particularly under oversaturated conditions. …”

Dynamic system state in demand scenarios 1. by Bin Zhao (276445)

“…<div><p>Capturing congestion propagation among different facilities at intersections in dynamic stochastic traffic environments poses significant challenges, particularly under oversaturated conditions. …”

Characteristics comparison of related literature. by Bin Zhao (276445)

“…<div><p>Capturing congestion propagation among different facilities at intersections in dynamic stochastic traffic environments poses significant challenges, particularly under oversaturated conditions. …”