Spectral slope analysis. حسب Anika T. Löwe (19924021)

Analysis of differential microbiome and classification prediction model between case and control groups. حسب Chuan Zhang (187157)

"…<p><b>(A)</b> Linear discriminant analysis [LDA; (log10)>2] and (B) effect size (LEfSe) analysis revealed significant differences (P < 0.01) in the microbiota of the case (orange, negative score) and control groups (blue, positive score) groups. …"

SLC response kinematics at 28 dpf. حسب Morgan Barnes (7876373)

"…The angle and duration of the C1 bend is not significantly altered at this time point. (C-D) C2 is the second C-bend performed. …"

FFD – main effect, original fibrosis burden. حسب Åshild Telle (22794721)

Algorithms for designing continuous curricula A: Decision tree showing the continuous version of ADP which includes actions that “grow” and “shrink” the increments between continuo... حسب William L. Tong (22238845)

Deep reinforcement learning agents trained using a curriculum solve navigation tasks with delayed rewards. حسب William L. Tong (22238845)

Phylosymbiosis between primates and their gut mycobiomes. حسب Emily P. Van Syoc (22169869)

"…<b>(C)</b> Pairwise Bray–Curtis distances between individual samples for within-host (intraspecific) and between-host (interspecific) comparisons are shown for each host species, ordered by decreasing median of intraspecific distances. Asterisks denote statistical significance (<i>T</i> test <i><i>P</i></i> < 0.05). …"

Simulated centering ILD (cILD) patterns. حسب Carlos Jurado (20996552)

Sample size in each occupational subgroup. حسب Maryam Rafiee (10368504)

"…Furthermore, an increase of one point in work ability score leads to a decrease in WMSDs in the neck, wrists/hands, low back and hips/thighs regions by 13.5%, 8%, 11.5%, and 9%, respectively.…"

Correlation between different study variables. حسب Maryam Rafiee (10368504)

"…Furthermore, an increase of one point in work ability score leads to a decrease in WMSDs in the neck, wrists/hands, low back and hips/thighs regions by 13.5%, 8%, 11.5%, and 9%, respectively.…"

Study flow diagram. حسب Maryam Rafiee (10368504)

"…Furthermore, an increase of one point in work ability score leads to a decrease in WMSDs in the neck, wrists/hands, low back and hips/thighs regions by 13.5%, 8%, 11.5%, and 9%, respectively.…"

Final data used in the study. حسب Maryam Rafiee (10368504)

"…Furthermore, an increase of one point in work ability score leads to a decrease in WMSDs in the neck, wrists/hands, low back and hips/thighs regions by 13.5%, 8%, 11.5%, and 9%, respectively.…"

Demographic characteristics of participants. حسب Maryam Rafiee (10368504)

"…Furthermore, an increase of one point in work ability score leads to a decrease in WMSDs in the neck, wrists/hands, low back and hips/thighs regions by 13.5%, 8%, 11.5%, and 9%, respectively.…"

Dataset visualization diagram. حسب 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%. …"

Dataset sample images. حسب 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%. …"

Performance comparison of different models. حسب 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%. …"

C2f and BC2f module structure diagrams. حسب 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 detection results diagram. حسب 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-BWG model structure diagram. حسب 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%. …"

BiFormer structure diagram. حسب 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%. …"