Data Sheet 8_Successful prediction of LC8 binding to intrinsically disordered proteins sheds light on AlphaFold’s black box.csv by Douglas R. Walker (3664039)

Data Sheet 4_Successful prediction of LC8 binding to intrinsically disordered proteins sheds light on AlphaFold’s black box.csv by Douglas R. Walker (3664039)

Data Sheet 9_Successful prediction of LC8 binding to intrinsically disordered proteins sheds light on AlphaFold’s black box.csv by Douglas R. Walker (3664039)

Expression analysis of lncRNAs between the MCAO and sham groups. by Meimei Xu (744867)

Supplementary file 1_A workflow for extracting ungulate trails in wetlands using 3D point clouds obtained from airborne laser scanning.docx by Jinhu Wang (2631772)

“…The (near-)terrain points are then segmented using an iterative filtering algorithm, and digital terrain models are generated with a user-defined resolution. …”

The code for sample size calculation. by Ying Zhou (25031)

“…We collected basic clinical data and multimodal ultrasound data from these patients as predictive features, with clinical pregnancy as the predictive label, for model training. …”

Predictive modelling of peroxisome proliferator-activated receptor gamma (PPARγ) IC50 inhibition by emerging pollutants using light gradient boosting machine by A. Awomuti (20926958)

“…The predictive model, based on the light-gradient boosting machine (LightGBM) algorithm, was trained on a dataset of 1804 molecules showed <i>r</i>² values of 0.82 and 0.59, Mean Absolute Error (MAE) of 0.38 and 0.58, and Root Mean Square Error (RMSE) of 0.54 and 0.76 for the training and test sets, respectively. …”

Preprocessed Spatial Dynamic Wind Power Forecasting (SDWPF) Dataset from Baidu KDD Cup 2022 Challenge by Yuan Shi (22779782)

Breakdown of respondents. by Qunita Brown (19751520)

“…High quality data from Africa will afford diversity to global data sets, reducing bias in algorithms built for artificial intelligence technologies in healthcare. …”

SC25-lsCOMP by Yafan Huang (21798488)

Integrating drought warning water level with analytical hedging for reservoir water supply operation by Wenhua Wan (8051543)

“…</p><p dir="ltr">2. R codes for the HR-based DP algorithm, the processes deriving seasonal DWWL, and the statistical performance of HR with DWWL during typical drought years.…”

Linear mixed-effect model results. by Shirong Chen (22127046)

“…Additionally, we found three distinct preparatory reading patterns: <i><i>Fast Surface-level Preparatory Reading, Systematic Deep-level Preparatory Reading,</i></i> and <i><i>Extended Iterative Preparatory Reading,</i></i> each reflecting a distinct combination of cognitive investment and reading speed. …”

Visualizations of three clusters. by Shirong Chen (22127046)

“…Additionally, we found three distinct preparatory reading patterns: <i><i>Fast Surface-level Preparatory Reading, Systematic Deep-level Preparatory Reading,</i></i> and <i><i>Extended Iterative Preparatory Reading,</i></i> each reflecting a distinct combination of cognitive investment and reading speed. …”

Summary of three preparatory reading clusters. by Shirong Chen (22127046)

“…Additionally, we found three distinct preparatory reading patterns: <i><i>Fast Surface-level Preparatory Reading, Systematic Deep-level Preparatory Reading,</i></i> and <i><i>Extended Iterative Preparatory Reading,</i></i> each reflecting a distinct combination of cognitive investment and reading speed. …”

Ablation Experiment_. by Cheng-jie Chen (22272090)

“…<div><p>Under the influence of complex factors such as lighting, color distortion, and suspended solids, there is a problem of losing edge feature information and blurring edges in product appearance design images. …”

Sample of product appearance design images. by Cheng-jie Chen (22272090)

“…<div><p>Under the influence of complex factors such as lighting, color distortion, and suspended solids, there is a problem of losing edge feature information and blurring edges in product appearance design images. …”

LSTM model’s equations. by Songsong Wang (8088293)

“…The findings indicate that the LSTM model, when integrated with the watershed-internal KG and LLM, can effectively incorporate critical elements influencing water level changes, the accuracy of the LLM-KG-LSTM model is enhanced by 3% compared to the standard LSTM model, and the LSTM series outperforms both RNN and GRU models, Our method will guide future research from the perspective of focusing on forecasting algorithms to the perspective of focusing on the relationship between multi-dimensional disaster data and algorithm parallelism.…”

Parameter’s interpretation. by Songsong Wang (8088293)

“…The findings indicate that the LSTM model, when integrated with the watershed-internal KG and LLM, can effectively incorporate critical elements influencing water level changes, the accuracy of the LLM-KG-LSTM model is enhanced by 3% compared to the standard LSTM model, and the LSTM series outperforms both RNN and GRU models, Our method will guide future research from the perspective of focusing on forecasting algorithms to the perspective of focusing on the relationship between multi-dimensional disaster data and algorithm parallelism.…”

The models’ training parameters. by Songsong Wang (8088293)

“…The findings indicate that the LSTM model, when integrated with the watershed-internal KG and LLM, can effectively incorporate critical elements influencing water level changes, the accuracy of the LLM-KG-LSTM model is enhanced by 3% compared to the standard LSTM model, and the LSTM series outperforms both RNN and GRU models, Our method will guide future research from the perspective of focusing on forecasting algorithms to the perspective of focusing on the relationship between multi-dimensional disaster data and algorithm parallelism.…”

Model’s measure methods. by Songsong Wang (8088293)

“…The findings indicate that the LSTM model, when integrated with the watershed-internal KG and LLM, can effectively incorporate critical elements influencing water level changes, the accuracy of the LLM-KG-LSTM model is enhanced by 3% compared to the standard LSTM model, and the LSTM series outperforms both RNN and GRU models, Our method will guide future research from the perspective of focusing on forecasting algorithms to the perspective of focusing on the relationship between multi-dimensional disaster data and algorithm parallelism.…”