Spatial characterization and cell-type-specific genes in mouse brain tissue. by Huamei Li (8815955)

Order parameters versus temperature for amylase tasks: by Carlos A. Gomez-Uribe (21492788)

“…Markers come from BADASS runs, and lines are fits using Eqs 5–7 in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1013119#pcbi.1013119.s001" target="_blank">S1 Text</a>. These were obtained from cooling then heating runs of our algorithm for the amylase task: on the left using the ESM2 mutant marginal score, and on the right using the machine learning model that predicts fitness for stain removal and dp3 function. …”

Simulation conditions. by Tuan Anh Nguyen (121944)

“…Previous studies often applied only one or several traditional algorithms to control the performance of EPAS systems and ignored the influence of external disturbances. …”

System models. by Tuan Anh Nguyen (121944)

“…Previous studies often applied only one or several traditional algorithms to control the performance of EPAS systems and ignored the influence of external disturbances. …”

Proposed control scheme. by Tuan Anh Nguyen (121944)

“…Previous studies often applied only one or several traditional algorithms to control the performance of EPAS systems and ignored the influence of external disturbances. …”

Ideal assisted torque. by Tuan Anh Nguyen (121944)

“…Previous studies often applied only one or several traditional algorithms to control the performance of EPAS systems and ignored the influence of external disturbances. …”

Table 1_Static and temporal dynamic changes in brain activity in patients with post-stroke balance dysfunction: a pilot resting state fMRI.docx by Zhiqing Tang (12895406)

Supervised Predictive Modeling of High-dimensional Data with Group l0-norm Constrained Neural Networks by Zhihuang Yang (22530437)

Supplementary file 1_Computational methods and artificial intelligence-based modeling of magnesium alloys: a systematic review of machine learning, deep learning, and data-driven d... by Hanxuan Wang (6589901)

Fig 12 - by Nisha Ann Viswan (20371285)

Multistart (InitScram) method. by Nisha Ann Viswan (20371285)

Investigation of the bioactivity and ADMET properties of compounds targeting the breast cancer receptor ERα by Jinhui Cao (22505423)

Grid search process. by Lin Qi (128224)

“…During the prediction phase, the model performs excellently across different sample types in the test set, achieving a precision (P) of 84%, a recall (R) of 86%, and an F1 score of 85%. Through the model’s interpretability analysis, we find that quality, functionality, and price are key features affecting perceived risk for electronic products. …”

Research framework. by Lin Qi (128224)

“…During the prediction phase, the model performs excellently across different sample types in the test set, achieving a precision (P) of 84%, a recall (R) of 86%, and an F1 score of 85%. Through the model’s interpretability analysis, we find that quality, functionality, and price are key features affecting perceived risk for electronic products. …”

Parameter configuration for TextCNN model. by Lin Qi (128224)

“…During the prediction phase, the model performs excellently across different sample types in the test set, achieving a precision (P) of 84%, a recall (R) of 86%, and an F1 score of 85%. Through the model’s interpretability analysis, we find that quality, functionality, and price are key features affecting perceived risk for electronic products. …”

Model performance on the validation set. by Lin Qi (128224)

“…During the prediction phase, the model performs excellently across different sample types in the test set, achieving a precision (P) of 84%, a recall (R) of 86%, and an F1 score of 85%. Through the model’s interpretability analysis, we find that quality, functionality, and price are key features affecting perceived risk for electronic products. …”

Performance comparison of different models. by Lin Qi (128224)

“…During the prediction phase, the model performs excellently across different sample types in the test set, achieving a precision (P) of 84%, a recall (R) of 86%, and an F1 score of 85%. Through the model’s interpretability analysis, we find that quality, functionality, and price are key features affecting perceived risk for electronic products. …”

Performance evaluation of models on test dataset. by Lin Qi (128224)

“…During the prediction phase, the model performs excellently across different sample types in the test set, achieving a precision (P) of 84%, a recall (R) of 86%, and an F1 score of 85%. Through the model’s interpretability analysis, we find that quality, functionality, and price are key features affecting perceived risk for electronic products. …”

Topic classification based on KeyBERT-TextCNN. by Lin Qi (128224)

“…During the prediction phase, the model performs excellently across different sample types in the test set, achieving a precision (P) of 84%, a recall (R) of 86%, and an F1 score of 85%. Through the model’s interpretability analysis, we find that quality, functionality, and price are key features affecting perceived risk for electronic products. …”

Summary of perceived risk dimensions. by Lin Qi (128224)

“…During the prediction phase, the model performs excellently across different sample types in the test set, achieving a precision (P) of 84%, a recall (R) of 86%, and an F1 score of 85%. Through the model’s interpretability analysis, we find that quality, functionality, and price are key features affecting perceived risk for electronic products. …”