The Pareto-optimal fronts on the random network with 10 added edges and its corresponding solutions. by Ruochen Zhang (3434996)

The Pareto-optimal fronts on the regular network with 10 added edges and its corresponding solutions. by Ruochen Zhang (3434996)

Image 1_A novel inversion method of slope rock mechanical parameters using differential evolution gray wolf algorithm to optimize support vector regression.tif by Tingkai Hou (21014762)

Image 2_A novel inversion method of slope rock mechanical parameters using differential evolution gray wolf algorithm to optimize support vector regression.tif by Tingkai Hou (21014762)

Illustration of the representation. by Ruochen Zhang (3434996)

The topologies of edging solution with <i>k</i> = 10 on Zachary’s Karate Club network. by Ruochen Zhang (3434996)

Hip versus ankle joints torque for different methods. by Hedyeh Jafari (9667127)

Phase portraits comparison of the mentioned methods for the ankle joint. by Hedyeh Jafari (9667127)

Phase portraits comparison of the mentioned methods for the hip joint. by Hedyeh Jafari (9667127)

DataSheet1_An optimized three-dimensional time-space domain staggered-grid finite-difference method.docx by Wei Liu (20030)

Parameter space exploration in comparison to exploitation for the selection of samples during Bayesian Optimization. by John E. Fleming (8533956)

DataSheet_1_CT-Based Radiomics Analysis for Preoperative Diagnosis of Pancreatic Mucinous Cystic Neoplasm and Atypical Serous Cystadenomas.pdf by Tiansong Xie (10959561)

Study workflow overview. by Janne J. Luppi (17804327)

“…According to this algorithm, connections of relatively active regions are damaged (dashed lines) with priority compared to the less active (solid lines). …”

Presentation_1_Finite Element-Based Personalized Simulation of Duodenal Hydrogel Spacer: Spacer Location Dependent Duodenal Sparing and a Decision Support System for Spacer-Enabled... by Hamed Hooshangnejad (12282518)

Human expert versus automated processing: Running dataset. by Keenon Werling (17512156)

Image_4_Development and validation of an ensemble machine-learning model for predicting early mortality among patients with bone metastases of hepatocellular carcinoma.tif by Ze Long (8568447)

Image_7_Development and validation of an ensemble machine-learning model for predicting early mortality among patients with bone metastases of hepatocellular carcinoma.tif by Ze Long (8568447)

Table_1_Development and validation of an ensemble machine-learning model for predicting early mortality among patients with bone metastases of hepatocellular carcinoma.docx by Ze Long (8568447)

Image_5_Development and validation of an ensemble machine-learning model for predicting early mortality among patients with bone metastases of hepatocellular carcinoma.tif by Ze Long (8568447)

Image_6_Development and validation of an ensemble machine-learning model for predicting early mortality among patients with bone metastases of hepatocellular carcinoma.tif by Ze Long (8568447)