Robustness optimization strategy. by Huiyuan Zheng (12889196)

“…Nevertheless, a 40% reduction in roll motion is achieved (3.36° in Condition 4 vs. 5.60° in Condition 1), along with a 24.5% reduction in yaw motion (39.22° in Condition 4 vs. 51.94° in Condition 1). (3) In irregular wave simulations, the ballast tanks effectively reduce the heave amplitude by up to 8.34% in sea state level 4 and 6.06% in sea state level 8, thereby enhancing its wave-following performance in the heave degree of freedom. (4) A CNN_BiLSTM_Attention algorithm is developed using hydrodynamic analysis generated datasets to predict the pitch motion time series of the platform under different ballast water conditions and sea states, while the model has a superior prediction performance (R² = 0.9658, RMSE = 0.5343, MAE = 0.3188, representing a 4.82% increase in R² and 30.31% reduction in RMSE compared to the original model). …”

Mooring system. by Huiyuan Zheng (12889196)

“…Nevertheless, a 40% reduction in roll motion is achieved (3.36° in Condition 4 vs. 5.60° in Condition 1), along with a 24.5% reduction in yaw motion (39.22° in Condition 4 vs. 51.94° in Condition 1). (3) In irregular wave simulations, the ballast tanks effectively reduce the heave amplitude by up to 8.34% in sea state level 4 and 6.06% in sea state level 8, thereby enhancing its wave-following performance in the heave degree of freedom. (4) A CNN_BiLSTM_Attention algorithm is developed using hydrodynamic analysis generated datasets to predict the pitch motion time series of the platform under different ballast water conditions and sea states, while the model has a superior prediction performance (R² = 0.9658, RMSE = 0.5343, MAE = 0.3188, representing a 4.82% increase in R² and 30.31% reduction in RMSE compared to the original model). …”

The “Guo Hai Shi 1” offshore test platform. by Huiyuan Zheng (12889196)

“…Nevertheless, a 40% reduction in roll motion is achieved (3.36° in Condition 4 vs. 5.60° in Condition 1), along with a 24.5% reduction in yaw motion (39.22° in Condition 4 vs. 51.94° in Condition 1). (3) In irregular wave simulations, the ballast tanks effectively reduce the heave amplitude by up to 8.34% in sea state level 4 and 6.06% in sea state level 8, thereby enhancing its wave-following performance in the heave degree of freedom. (4) A CNN_BiLSTM_Attention algorithm is developed using hydrodynamic analysis generated datasets to predict the pitch motion time series of the platform under different ballast water conditions and sea states, while the model has a superior prediction performance (R² = 0.9658, RMSE = 0.5343, MAE = 0.3188, representing a 4.82% increase in R² and 30.31% reduction in RMSE compared to the original model). …”

Difference comparison. by Huiyuan Zheng (12889196)

“…Nevertheless, a 40% reduction in roll motion is achieved (3.36° in Condition 4 vs. 5.60° in Condition 1), along with a 24.5% reduction in yaw motion (39.22° in Condition 4 vs. 51.94° in Condition 1). (3) In irregular wave simulations, the ballast tanks effectively reduce the heave amplitude by up to 8.34% in sea state level 4 and 6.06% in sea state level 8, thereby enhancing its wave-following performance in the heave degree of freedom. (4) A CNN_BiLSTM_Attention algorithm is developed using hydrodynamic analysis generated datasets to predict the pitch motion time series of the platform under different ballast water conditions and sea states, while the model has a superior prediction performance (R² = 0.9658, RMSE = 0.5343, MAE = 0.3188, representing a 4.82% increase in R² and 30.31% reduction in RMSE compared to the original model). …”

Comparison of model draft and actual draft. by Huiyuan Zheng (12889196)

“…Nevertheless, a 40% reduction in roll motion is achieved (3.36° in Condition 4 vs. 5.60° in Condition 1), along with a 24.5% reduction in yaw motion (39.22° in Condition 4 vs. 51.94° in Condition 1). (3) In irregular wave simulations, the ballast tanks effectively reduce the heave amplitude by up to 8.34% in sea state level 4 and 6.06% in sea state level 8, thereby enhancing its wave-following performance in the heave degree of freedom. (4) A CNN_BiLSTM_Attention algorithm is developed using hydrodynamic analysis generated datasets to predict the pitch motion time series of the platform under different ballast water conditions and sea states, while the model has a superior prediction performance (R² = 0.9658, RMSE = 0.5343, MAE = 0.3188, representing a 4.82% increase in R² and 30.31% reduction in RMSE compared to the original model). …”

Resultant moment diagram. by Huiyuan Zheng (12889196)

“…Nevertheless, a 40% reduction in roll motion is achieved (3.36° in Condition 4 vs. 5.60° in Condition 1), along with a 24.5% reduction in yaw motion (39.22° in Condition 4 vs. 51.94° in Condition 1). (3) In irregular wave simulations, the ballast tanks effectively reduce the heave amplitude by up to 8.34% in sea state level 4 and 6.06% in sea state level 8, thereby enhancing its wave-following performance in the heave degree of freedom. (4) A CNN_BiLSTM_Attention algorithm is developed using hydrodynamic analysis generated datasets to predict the pitch motion time series of the platform under different ballast water conditions and sea states, while the model has a superior prediction performance (R² = 0.9658, RMSE = 0.5343, MAE = 0.3188, representing a 4.82% increase in R² and 30.31% reduction in RMSE compared to the original model). …”

Regular wave condition combination. by Huiyuan Zheng (12889196)

“…Nevertheless, a 40% reduction in roll motion is achieved (3.36° in Condition 4 vs. 5.60° in Condition 1), along with a 24.5% reduction in yaw motion (39.22° in Condition 4 vs. 51.94° in Condition 1). (3) In irregular wave simulations, the ballast tanks effectively reduce the heave amplitude by up to 8.34% in sea state level 4 and 6.06% in sea state level 8, thereby enhancing its wave-following performance in the heave degree of freedom. (4) A CNN_BiLSTM_Attention algorithm is developed using hydrodynamic analysis generated datasets to predict the pitch motion time series of the platform under different ballast water conditions and sea states, while the model has a superior prediction performance (R² = 0.9658, RMSE = 0.5343, MAE = 0.3188, representing a 4.82% increase in R² and 30.31% reduction in RMSE compared to the original model). …”

Ballast tank water volume setting. by Huiyuan Zheng (12889196)

“…Nevertheless, a 40% reduction in roll motion is achieved (3.36° in Condition 4 vs. 5.60° in Condition 1), along with a 24.5% reduction in yaw motion (39.22° in Condition 4 vs. 51.94° in Condition 1). (3) In irregular wave simulations, the ballast tanks effectively reduce the heave amplitude by up to 8.34% in sea state level 4 and 6.06% in sea state level 8, thereby enhancing its wave-following performance in the heave degree of freedom. (4) A CNN_BiLSTM_Attention algorithm is developed using hydrodynamic analysis generated datasets to predict the pitch motion time series of the platform under different ballast water conditions and sea states, while the model has a superior prediction performance (R² = 0.9658, RMSE = 0.5343, MAE = 0.3188, representing a 4.82% increase in R² and 30.31% reduction in RMSE compared to the original model). …”

Step length asymmetry and step width during split-belt adaptation. by Samantha Jeffcoat (22783930)

PfIleRS-SANC456 delta <i>CC.</i> by Curtis Chepsiror (21368103)

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

“…Unchanged maximal rise slope <b>(G)</b>; Reduced half-width <b>(H)</b>; Decreased AHP amplitude <b>(I)</b>; Increased number of spikes upon 100 pA depolarization <b>(J)</b>. …”

Reconciliation services product value analysis. by Yu Zhang (12946)

“…The study reveals several key findings. (1) In the three study areas, afforestation significantly impacts the regulation of service product values. …”

Means of sampling data in the Shibing study area. by Yu Zhang (12946)

“…The study reveals several key findings. (1) In the three study areas, afforestation significantly impacts the regulation of service product values. …”

Technology roadmap. by Yu Zhang (12946)

“…The study reveals several key findings. (1) In the three study areas, afforestation significantly impacts the regulation of service product values. …”

Zunhua study area factor rotation results. by Yu Zhang (12946)

“…The study reveals several key findings. (1) In the three study areas, afforestation significantly impacts the regulation of service product values. …”

Value of ecological products in the study area. by Yu Zhang (12946)

“…The study reveals several key findings. (1) In the three study areas, afforestation significantly impacts the regulation of service product values. …”

Means of sampling data in the Zunhua study area. by Yu Zhang (12946)

“…The study reveals several key findings. (1) In the three study areas, afforestation significantly impacts the regulation of service product values. …”

Effect of the Surface Peak–Valley Features on Droplet Impact Dynamics under Leidenfrost Temperature by Yunlong Jiao (6672764)

“…Specifically, the Leidenfrost temperature on micropit surfaces increases with greater micropit area occupancy, while it decreases on micropillar surfaces under similar conditions, which is mainly attributed to the differential impact of area occupancy on droplet heat transfer efficiency. …”

Effect of the Surface Peak–Valley Features on Droplet Impact Dynamics under Leidenfrost Temperature by Yunlong Jiao (6672764)

“…Specifically, the Leidenfrost temperature on micropit surfaces increases with greater micropit area occupancy, while it decreases on micropillar surfaces under similar conditions, which is mainly attributed to the differential impact of area occupancy on droplet heat transfer efficiency. …”

S5 File - by Xiangling Li (2308666)

“…</p><p>Results</p><p>CY and DEX significantly reduced the immune function in DNFB-induced sensitized mice, as indicated by decreased thymus and spleen indices, MLN enlargement, intestinal sIgA content, and ear swelling degree. …”