Marginal effect of HTD on income and SWB. by Stephan Dietrich (6979985)

“…We take advantage of 40 years of variation in daily land surface temperature data, finding that one additional exceptionally hot day significantly lowers wellbeing, by roughly 0.5% on average. …”

Estimation procedure flow-chart. by Stephan Dietrich (6979985)

“…We take advantage of 40 years of variation in daily land surface temperature data, finding that one additional exceptionally hot day significantly lowers wellbeing, by roughly 0.5% on average. …”

Structural model outline. by Stephan Dietrich (6979985)

“…We take advantage of 40 years of variation in daily land surface temperature data, finding that one additional exceptionally hot day significantly lowers wellbeing, by roughly 0.5% on average. …”

SEM images of cobalt-based catalysts supported on Al₂O₃ at different magnifications. by Ning Zuo (17295415)

OVX educes bone density of femur. by Mingzhu Chen (6370013)

“…Compared to Sham rats, OVX tats had a significant decrease in bone mass and impaired bone micro structure: (L) BMD, (M) BV, (N) BV/TV, (O) Tb.N,(P) Tb.Sp, and (Q) Tb.Th. …”

Fos does not interact with Jun but homodimerizes post axotomy. by Gibarni Mahata (19486154)

<b> </b> Energy efficiency and gas volume comparison. by Ning Zuo (17295415)

“…CO₂ yield was higher in the control group at lower temperatures, while the integrated system consistently produced more biochar and biogas. …”

Model validation of kinetic parameters. by Ning Zuo (17295415)

“…CO₂ yield was higher in the control group at lower temperatures, while the integrated system consistently produced more biochar and biogas. …”

A Hydrate-Bearing Sediment Gas Replacement Mechanical Behavior Regulation Mechanism and Slope Stability Analysis by Lei Huang (35191)

“…The Ψ value on the <i>v</i>-ln <i>p</i>′ plane is consistently negative, and gas replacement results in higher ln <i>p</i>′ values and lower <i>v</i> at the failure state point. As saturation increases, the Γ value of the critical state line decreases, while the λ value increases. (3) For slope simulations, increased hydrate saturation significantly raises the safety factor for gentler slopes, while the reinforcing effect of gas replacement is weaker for steeper slopes with higher saturation.…”

A Hydrate-Bearing Sediment Gas Replacement Mechanical Behavior Regulation Mechanism and Slope Stability Analysis by Lei Huang (35191)

“…The Ψ value on the <i>v</i>-ln <i>p</i>′ plane is consistently negative, and gas replacement results in higher ln <i>p</i>′ values and lower <i>v</i> at the failure state point. As saturation increases, the Γ value of the critical state line decreases, while the λ value increases. (3) For slope simulations, increased hydrate saturation significantly raises the safety factor for gentler slopes, while the reinforcing effect of gas replacement is weaker for steeper slopes with higher saturation.…”

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%. …”

Performance comparison of three loss functions. 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%. …”