Systematic All-Hydrocarbon Stapling Analysis for Cecropin A Generates a Potent and Stable Antimicrobial Peptide by Yejiao Shi (6391688)

“…Compared to cecropin A, its increased helicity and hydrophobicity as well as the decreased net charge also enabled its improved stability and biocompatibility, facilitating its enhanced antibacterial and anti-inflammatory efficacy for the effective treatment of mice with peritonitis sepsis. …”

Comparison of cats in the petting groups who contacted the researcher’s hand vs. those who did not. by Allison Andrukonis (11846404)

Amino acid metabolic pathways are influenced by the NC1 POM cycle over expression. by Bonnie A. McNeil (22331601)

“…Box and whisker plots of selected metabolites that displayed significant increase or decrease in the NC1 POM cycle producing strain (NC), red boxes, or empty vector producing strain (YC) green boxes. …”

Direct Observation of Liquid–Liquid Phase Separation and Core–Shell Morphology of PM_2.5 Collected from Three Northeast Asian Cities and Implications for N₂O<s... by Mijung Song (13134633)

“…As the shell becomes more viscous, the diffusivity of N₂O₅ decreases, thereby lowering the N₂O₅ uptake coefficient by 1–3 orders of magnitude and significantly restricting N₂O₅ uptake. …”

Direct Observation of Liquid–Liquid Phase Separation and Core–Shell Morphology of PM_2.5 Collected from Three Northeast Asian Cities and Implications for N₂O<s... by Mijung Song (13134633)

“…As the shell becomes more viscous, the diffusivity of N₂O₅ decreases, thereby lowering the N₂O₅ uptake coefficient by 1–3 orders of magnitude and significantly restricting N₂O₅ uptake. …”

Direct Observation of Liquid–Liquid Phase Separation and Core–Shell Morphology of PM_2.5 Collected from Three Northeast Asian Cities and Implications for N₂O<s... by Mijung Song (13134633)

“…As the shell becomes more viscous, the diffusivity of N₂O₅ decreases, thereby lowering the N₂O₅ uptake coefficient by 1–3 orders of magnitude and significantly restricting N₂O₅ uptake. …”

Direct Observation of Liquid–Liquid Phase Separation and Core–Shell Morphology of PM_2.5 Collected from Three Northeast Asian Cities and Implications for N₂O<s... by Mijung Song (13134633)

“…As the shell becomes more viscous, the diffusivity of N₂O₅ decreases, thereby lowering the N₂O₅ uptake coefficient by 1–3 orders of magnitude and significantly restricting N₂O₅ uptake. …”

Direct Observation of Liquid–Liquid Phase Separation and Core–Shell Morphology of PM_2.5 Collected from Three Northeast Asian Cities and Implications for N₂O<s... by Mijung Song (13134633)

“…As the shell becomes more viscous, the diffusivity of N₂O₅ decreases, thereby lowering the N₂O₅ uptake coefficient by 1–3 orders of magnitude and significantly restricting N₂O₅ uptake. …”

Direct Observation of Liquid–Liquid Phase Separation and Core–Shell Morphology of PM_2.5 Collected from Three Northeast Asian Cities and Implications for N₂O<s... by Mijung Song (13134633)

“…As the shell becomes more viscous, the diffusivity of N₂O₅ decreases, thereby lowering the N₂O₅ uptake coefficient by 1–3 orders of magnitude and significantly restricting N₂O₅ uptake. …”

n-fold cross-validation and predictor for the δ³⁴S random forest regression model: A) Observed and predicted δ³⁴S isotope values. by Borja González-Rabanal (22138847)

Prediction of transition readiness. by Sharon Barak (4803966)

“…In most transition domains, help needed did not decrease with age and was not affected by function. …”

Dataset visualization 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%. …”

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%. …”

Performance comparison of different 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%. …”

C2f and BC2f module structure diagrams. 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 detection results 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%. …”

YOLOv8n-BWG model 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%. …”

BiFormer 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%. …”

YOLOv8n-BWG detection results 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%. …”

GSConv module 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%. …”