(a) Star_block module. by Dongshan Jiang (21866339)

“…Therefore, our proposed algorithm has great potential in the field of drought resistance identification and germplasm selection of mung bean.…”

CSCA-YOLOv8 network structure. by Dongshan Jiang (21866339)

“…Therefore, our proposed algorithm has great potential in the field of drought resistance identification and germplasm selection of mung bean.…”

Hyperparameters settings. by Dongshan Jiang (21866339)

“…Therefore, our proposed algorithm has great potential in the field of drought resistance identification and germplasm selection of mung bean.…”

Experimental results of various loss functions. by Dongshan Jiang (21866339)

“…Therefore, our proposed algorithm has great potential in the field of drought resistance identification and germplasm selection of mung bean.…”

Table 1_Risk prediction for gastrointestinal bleeding in pediatric Henoch-Schönlein purpura using an interpretable transformer model.doc by Gahao Chen (21688843)

“…GI complications were stratified into three severity tiers: 1) no complications, 2) abdominal pain without bleeding), and 3) documented rectal bleeding or hemorrhage, based on standardized diagnostic criteria. Five machine learning algorithms (Random Forest, XGBoost, LightGBM, CatBoost, and TabPFN-V2) were optimized through nested cross-validation. …”

Bayesian Calibration of the 40K Decay Scheme and its implications for 40K-based geochronology by John Carter (14649908)

“…</p><p dir="ltr"><br></p><p dir="ltr">BayesCal Comp (Jupyter notebook) - Jupyter notebook containing the entire code for the MCMC algorithm described in the "Bayesian Calibration of the 40K Decay Scheme and its implications for 40K-based geochronology" manuscript. …”

Supporting Information: New perspectives on European wildcat (<i>Felis silvestris, </i>Schreber 1777) habitat suitability in Britain: Integrating fossil records to improve baseline... by Mollie Mills (15796295)

“…</p><p dir="ltr">We modelled environmental suitability for the European wildcat (Felis silvestris) in Britain under current climate conditions using modern, historic and Holocene fossil data. We used the maximum entropy algorithm (Maxent) implemented through the <i>R</i> package <i>maxnet.…”

Unveiling optimal SDG pathways: an innovative automated recommendation approach integrating graph pruning, intent graph, and attention mechanism by Qiang Wang (32383)

“…However, traditional recommendation algorithms struggle with two key challenges: spatial heterogeneity and sparse historical interaction records between regions and SDPs. …”

Table 1_Machine learning insights into early mortality risks for small cell lung cancer patients post-chemotherapy.docx by Min Liang (363007)

“…Additionally, the model was integrated into a web-based platform to improve accessibility.</p>Conclusion<p>This study introduces a machine learning model alongside a web-based support system as critical resources for healthcare professionals, facilitating personalized clinical decision-making and enhancing treatment strategies for SCLC patients post-chemotherapy.…”

Supplementary file 1_Almond yield prediction at orchard scale using satellite-derived biophysical traits and crop evapotranspiration combined with machine learning.pdf by Manuel Quintanilla-Albornoz (22653386)

“…In this study, remote sensing-based evapotranspiration estimates were evaluated for predicting almond yield at the orchard scale using machine learning (ML) algorithms. …”

Data Sheet 2_Bi-modal ultrasound radiomics and habitat analysis enhanced the pre-operative prediction of axillary lymph node burden in patients with early-stage breast cancer.xlsx by Jing Xu (15337)

“…The machine learning models based on SWE radiomics with habitat analysis could enhance the ability of prediction lymph node burden in patients with early-stage breast cancer, which could be a promising approach to make clinical decisions.…”

Data Sheet 1_Bi-modal ultrasound radiomics and habitat analysis enhanced the pre-operative prediction of axillary lymph node burden in patients with early-stage breast cancer.xlsx by Jing Xu (15337)

“…The machine learning models based on SWE radiomics with habitat analysis could enhance the ability of prediction lymph node burden in patients with early-stage breast cancer, which could be a promising approach to make clinical decisions.…”

An optional formula for calculating crustal thickness using Sr/Y ratio and its application to the southeastern margin of the Central Asian Orogenic Belt by Xu Ma (45624)

“…Global lower crust-derived magmatic rock (younger than 23 Ma) data from diverse geological settings were selected and filtered using key geochemical criteria, alternating and iteratively using K-means clustering algorithm and improved Thompson tau method to exclude outliers. …”

STURM-Flood: a curated dataset for deep learning-based flood extent mapping leveraging Sentinel-1 and Sentinel-2 imagery by Nicla Notarangelo (20684425)

“…Timely assessment and mapping of inundations are crucial for preventive and emergency measures, driving the demand for curated global geospatial data to implement novel algorithms. This study introduces the STURM-Flood dataset, a high-quality, open-access, and DL-ready resource for flood extent mapping using Sentinel-1 and Sentinel-2 satellite imagery, combined with ground-truth data from the Copernicus Emergency Management Service. …”

Supplementary Material for: Investigating the value of umbilical artery eucapnic pH as a biomarker for adverse neonatal outcomes: A systematic review by figshare admin karger (2628495)

“…Introduction: Metabolic acidosis at birth is associated with severe neonatal outcomes, making umbilical artery gas analysis crucial for management. …”

VIIDA and InViDe: computational approaches for generating and evaluating inclusive image paragraphs for the visually impaired by Daniel L. Fernandes (20416673)

“…<p>Existing image description methods when used as Assistive Technologies often fall short in meeting the needs of blind or low vision (BLV) individuals. …”

Spatial patterns of the CAPs estimated from both splits are reproducible and strongly correlated with at least one of the basis CAPs. by Kangjoo Lee (11648345)

“…Results were obtained from the split 1 data (top) and the split 2 data (bottom). Each <i>r</i> value is color-coded using a sorting algorithm to label the corresponding EC using the maximum spatial correlation with BCs. …”

Table 1_Genome-wide identification and expression analysis of phytochrome gene family in Aikang58 wheat (Triticum aestivum L.).xlsx by Zhu Yang (756364)

“…In this study, we employed bioinformatics methods to identify eight TaAkPHY genes in the Aikang58 wheat variety. …”

Table 12_Genome-wide identification and expression analysis of phytochrome gene family in Aikang58 wheat (Triticum aestivum L.).xlsx by Zhu Yang (756364)

“…In this study, we employed bioinformatics methods to identify eight TaAkPHY genes in the Aikang58 wheat variety. …”

Table 8_Genome-wide identification and expression analysis of phytochrome gene family in Aikang58 wheat (Triticum aestivum L.).xlsx by Zhu Yang (756364)

“…In this study, we employed bioinformatics methods to identify eight TaAkPHY genes in the Aikang58 wheat variety. …”