Expression analysis of color-related genes in the hepatopancreas and shell tissues of cherry shrimp (<i>N</i>. <i>davidi</i> var. red) fed with vehicle, astaxanthin alone and in co... by Wei-Wei Hou (3405947)

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Correlations between feed additives and color-related genes. by Wei-Wei Hou (3405947)

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Two-dimensional quadrant analysis of cherry shrimp (<i>N</i>. <i>davidi</i> var. red) bodycolor parameters (<i>L*</i> and <i>a*</i> values) in different feed treatment groups on da... by Wei-Wei Hou (3405947)

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Appearance and body color of <i>N</i>. <i>davidi</i> var. red. by Wei-Wei Hou (3405947)

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Color data collection points on the body of ornamental shrimp. by Wei-Wei Hou (3405947)

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Changes in the color parameters of cherry shrimp (<i>N</i>. <i>davidi</i> var. red) in different feed treatment groups^a over a 56-day period (mean ± SD, <i>n</i> = 20). by Wei-Wei Hou (3405947)

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MGPC module. by Bo Tong (2138632)

“…Experimental results indicate that at a pruning level of 1.5, mAP@0.5 and mAP@0.5:0.95 improved by 3.9% and 4.6%, respectively, while computational load decreased by 21% and parameter count dropped by 53%. …”

Comparative experiment. by Bo Tong (2138632)

“…Experimental results indicate that at a pruning level of 1.5, mAP@0.5 and mAP@0.5:0.95 improved by 3.9% and 4.6%, respectively, while computational load decreased by 21% and parameter count dropped by 53%. …”

Pruning experiment. by Bo Tong (2138632)

“…Experimental results indicate that at a pruning level of 1.5, mAP@0.5 and mAP@0.5:0.95 improved by 3.9% and 4.6%, respectively, while computational load decreased by 21% and parameter count dropped by 53%. …”

Parameter setting table. by Bo Tong (2138632)

“…Experimental results indicate that at a pruning level of 1.5, mAP@0.5 and mAP@0.5:0.95 improved by 3.9% and 4.6%, respectively, while computational load decreased by 21% and parameter count dropped by 53%. …”

DTADH module. by Bo Tong (2138632)

“…Experimental results indicate that at a pruning level of 1.5, mAP@0.5 and mAP@0.5:0.95 improved by 3.9% and 4.6%, respectively, while computational load decreased by 21% and parameter count dropped by 53%. …”

Ablation experiment. by Bo Tong (2138632)

“…Experimental results indicate that at a pruning level of 1.5, mAP@0.5 and mAP@0.5:0.95 improved by 3.9% and 4.6%, respectively, while computational load decreased by 21% and parameter count dropped by 53%. …”

Multi scale detection. by Bo Tong (2138632)

“…Experimental results indicate that at a pruning level of 1.5, mAP@0.5 and mAP@0.5:0.95 improved by 3.9% and 4.6%, respectively, while computational load decreased by 21% and parameter count dropped by 53%. …”

MFDPN module. by Bo Tong (2138632)

“…Experimental results indicate that at a pruning level of 1.5, mAP@0.5 and mAP@0.5:0.95 improved by 3.9% and 4.6%, respectively, while computational load decreased by 21% and parameter count dropped by 53%. …”

Detection effect of different sizes. by Bo Tong (2138632)

“…Experimental results indicate that at a pruning level of 1.5, mAP@0.5 and mAP@0.5:0.95 improved by 3.9% and 4.6%, respectively, while computational load decreased by 21% and parameter count dropped by 53%. …”

Radar chart comparing indicators. by Bo Tong (2138632)

“…Experimental results indicate that at a pruning level of 1.5, mAP@0.5 and mAP@0.5:0.95 improved by 3.9% and 4.6%, respectively, while computational load decreased by 21% and parameter count dropped by 53%. …”

MFD-YOLO structure. by Bo Tong (2138632)

“…Experimental results indicate that at a pruning level of 1.5, mAP@0.5 and mAP@0.5:0.95 improved by 3.9% and 4.6%, respectively, while computational load decreased by 21% and parameter count dropped by 53%. …”

Detection results of each category. by Bo Tong (2138632)

“…Experimental results indicate that at a pruning level of 1.5, mAP@0.5 and mAP@0.5:0.95 improved by 3.9% and 4.6%, respectively, while computational load decreased by 21% and parameter count dropped by 53%. …”

Predictive Significance of Glycosyltransferase-Related lncRNAs in Endometrial Cancer: A Comprehensive Analysis and Experimental Validation by Xiaoyu Shen (4715430)

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Cohesion characteristics and statistical significance analysis of different ingredients across IDDSI levels^b'*'. by Muxi Chen (9294270)

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