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%. …”

Enhanced intrinsic excitability of spinal Calcrl⁺ neurons in chronic itch. by Huifeng Jiao (11537806)

“…Data are represented as mean ± SEM (n = 5 mice per group). ns: not significant, *p < 0.05, **p < 0.01. …”

General participant characteristics. by Josephine Grandt (21512356)

“…The hepatic transcriptome changed significantly during a standardized meal and these changes were blunted in patients with NAFLD and cirrhosis. …”

Investigating the Influence of Transition Metal Substitution in Lithium Argyrodites on Structure, Transport, and Solid-State Battery Performance by Johannes Hartel (16316022)

“…This leads to blocking of Li⁺ transport pathways within the Li⁺ cages causing a decrease of ionic conductivities along with increasing activation energies for Li⁺ transport. …”

DoLP characterization of clustering results. by Dolores Bernenko (16528426)

5.HT_1A binding difference between known cause of death (KCOD) controls and SIDS. by Hannah C. Kinney (10280975)

“…When SIDS is compared to all KCOD controls, the HG, NTS, and MAO show a significant mean difference in 5-HT_1A receptor binding. …”

Post-meal glycemic curve of T2DM patients without or with raw cinnamon. by Fernanda Duarte Moreira (19825891)

“…<p>(A) Mean blood glucose (mg/dL) and (B) mean Δ blood glucose (difference between post-meal blood glucose and fasting blood glucose) of T2DM men throughout 120 minutes after eating a standardized meal alone (Control) or after prior ingestion of 3 g of raw cinnamon in capsules (3gCaps), 6 g of raw cinnamon in capsules (6gCaps), 3 g of raw cinnamon powder dissolved in water (3gPowder), or 6 g of raw cinnamon powder (6gPowder). …”

Heat map showing correlations between comet assay parameters, and warm and cold ischemia times. by Miroslava Jandová (22553132)

“…<p>Red values are significant at p < 0.05. Negative values mean that there is a “the more, the less” relationship between the quantities, i.e., as one quantity increases, the other quantity decreases. …”

Original data. by Kenji Sato (657309)

“…Additionally, a statistically significant improvement was observed in the Japanese Zarit Caregiver Burden Interview score at 6 months from baseline (p < 0.001).…”

Home-based rehabilitation program. by Kenji Sato (657309)

“…Additionally, a statistically significant improvement was observed in the Japanese Zarit Caregiver Burden Interview score at 6 months from baseline (p < 0.001).…”

Baseline characteristics of study participants. by Kenji Sato (657309)

“…Additionally, a statistically significant improvement was observed in the Japanese Zarit Caregiver Burden Interview score at 6 months from baseline (p < 0.001).…”

Assembly error angle of a single spline. by Zhe Hu (787283)

“…Then, the LSTM model is trained using this sliding window approach, achieving a root mean square error(RMSE) of 0.03 on the test set. …”

Annual distribution of IG-ralated AE reports. by Shaozhi Liu (13720340)

“…In pregnant women, hypoglycaemia (IC025 = 4.25) was detected as a strong signal, hypoglycaemia neonatal (IC025 = 2.96) as a medium signal, while ketoacidosis (IC025 = 0.76), decreased insulin requirement (IC025 = 0.24), and underweight (IC025 = 0.09) were identified as weak signals. …”

Proportion of AEs by SOCs in pregnant women. by Shaozhi Liu (13720340)

“…In pregnant women, hypoglycaemia (IC025 = 4.25) was detected as a strong signal, hypoglycaemia neonatal (IC025 = 2.96) as a medium signal, while ketoacidosis (IC025 = 0.76), decreased insulin requirement (IC025 = 0.24), and underweight (IC025 = 0.09) were identified as weak signals. …”

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

“…Numerical experiments using actual survey data from Kunshan City yield several noteworthy findings: (1) An optimal moderate-sized time step exists for rolling optimization to minimize either the average delay time or total costs; specifically, an excessively small time step may increase vehicle average delay time or total costs; (2) The percentage of delay reduction achieved by our method, compared to Synchro software, reaches a maximum of approximately 70% when traffic demand is moderate and the initial state is low; and (3) The percentage reduction in average delay or total costs compared to Synchro initially increases and then decreases with rising traffic intensity.…”