The direction of the magnetic force [1]. by Vahid Goodarzimehr (21343124)

“…The source codes of the MOSRS algorithm are publicly available at <a href="https://nimakhodadadi.com/algorithms-+-codes" target="_blank">https://nimakhodadadi.com/algorithms-+-codes</a>.…”

Friedman tests for the CEC 2009 problems. by Vahid Goodarzimehr (21343124)

“…The source codes of the MOSRS algorithm are publicly available at <a href="https://nimakhodadadi.com/algorithms-+-codes" target="_blank">https://nimakhodadadi.com/algorithms-+-codes</a>.…”

Reactive Molecular Simulation and Microscopic Origins in the Reaction Kinetics of Binary Polymerization by Xinwei Chen (255125)

“…A microscopic expression of the hybrid function is derived based on the general collision reaction model and reactive algorithm in molecular dynamics (MD) simulation, in which both the reaction energy barrier and intermolecular interaction play pivotal roles. …”

Image 1_MS4A7 based metabolic gene signature as a prognostic predictor in lung adenocarcinoma.jpeg by Yan Jiang (12139)

“…</p>Methods<p>A prognostic signature for LUAD was developed using the LASSO-Cox regression algorithm with RNA-seq data from 500 LUAD patients in The Cancer Genome Atlas database. …”

Image 3_MS4A7 based metabolic gene signature as a prognostic predictor in lung adenocarcinoma.jpeg by Yan Jiang (12139)

“…</p>Methods<p>A prognostic signature for LUAD was developed using the LASSO-Cox regression algorithm with RNA-seq data from 500 LUAD patients in The Cancer Genome Atlas database. …”

Image 10_MS4A7 based metabolic gene signature as a prognostic predictor in lung adenocarcinoma.jpeg by Yan Jiang (12139)

“…</p>Methods<p>A prognostic signature for LUAD was developed using the LASSO-Cox regression algorithm with RNA-seq data from 500 LUAD patients in The Cancer Genome Atlas database. …”

Image 4_MS4A7 based metabolic gene signature as a prognostic predictor in lung adenocarcinoma.jpeg by Yan Jiang (12139)

“…</p>Methods<p>A prognostic signature for LUAD was developed using the LASSO-Cox regression algorithm with RNA-seq data from 500 LUAD patients in The Cancer Genome Atlas database. …”

Image 8_MS4A7 based metabolic gene signature as a prognostic predictor in lung adenocarcinoma.jpeg by Yan Jiang (12139)

“…</p>Methods<p>A prognostic signature for LUAD was developed using the LASSO-Cox regression algorithm with RNA-seq data from 500 LUAD patients in The Cancer Genome Atlas database. …”

Image 2_MS4A7 based metabolic gene signature as a prognostic predictor in lung adenocarcinoma.jpeg by Yan Jiang (12139)

“…</p>Methods<p>A prognostic signature for LUAD was developed using the LASSO-Cox regression algorithm with RNA-seq data from 500 LUAD patients in The Cancer Genome Atlas database. …”

Image 9_MS4A7 based metabolic gene signature as a prognostic predictor in lung adenocarcinoma.jpeg by Yan Jiang (12139)

“…</p>Methods<p>A prognostic signature for LUAD was developed using the LASSO-Cox regression algorithm with RNA-seq data from 500 LUAD patients in The Cancer Genome Atlas database. …”

Image 6_MS4A7 based metabolic gene signature as a prognostic predictor in lung adenocarcinoma.jpeg by Yan Jiang (12139)

“…</p>Methods<p>A prognostic signature for LUAD was developed using the LASSO-Cox regression algorithm with RNA-seq data from 500 LUAD patients in The Cancer Genome Atlas database. …”

Image 7_MS4A7 based metabolic gene signature as a prognostic predictor in lung adenocarcinoma.jpeg by Yan Jiang (12139)

“…</p>Methods<p>A prognostic signature for LUAD was developed using the LASSO-Cox regression algorithm with RNA-seq data from 500 LUAD patients in The Cancer Genome Atlas database. …”

Image 5_MS4A7 based metabolic gene signature as a prognostic predictor in lung adenocarcinoma.jpeg by Yan Jiang (12139)

“…</p>Methods<p>A prognostic signature for LUAD was developed using the LASSO-Cox regression algorithm with RNA-seq data from 500 LUAD patients in The Cancer Genome Atlas database. …”

(A) The PPI network was mapped using the STRING visualization tool. by Zhikai Su (21144119)

A full feature list. by Ido Neuman (20390138)

“…<div><p>Background</p><p><i>Acinetobacter baumanni</i> infection is a leading cause of morbidity and mortality in the Intensive Care Unit (ICU). …”

Hippocampal and cortical activity reflect early hyperexcitability in an Alzheimer's mouse model by Marina Diachenko (19739092)

“…</p><p dir="ltr">All data are available upon request. The standalone Python implementation of the fE/I algorithm is available under a CC-BY-NC-SA license at <a href="https://github.com/arthur-ervin/crosci" target="_blank">https://github.com/arthur-ervin/crosci</a>. …”

Data associated with "Functional specialisation of multisensory temporal integration in the mouse superior colliculus" by Gaia Bianchini (7570553)

“…<p dir="ltr">Dataset from Bianchini et al., "Functional specialisation of multisensory temporal integration in the mouse superior colliculus" available at https://www.biorxiv.org/content/10.1101/2025.02.11.637674v3.…”

Image 7_Segmentation of tobacco shred point cloud and 3-D measurement based on improved PointNet++ network with DTC algorithm.png by Yihang Wang (4731429)

“…This model combines the weighted cross-entropy loss function to enhance the classification effect, the cosine annealing algorithm to optimize the training process, and the improved k-nearest neighbors multi-scale grouping method to enhance the model’s ability to segment the point cloud with complex morphology. …”

Image 2_Segmentation of tobacco shred point cloud and 3-D measurement based on improved PointNet++ network with DTC algorithm.png by Yihang Wang (4731429)

“…This model combines the weighted cross-entropy loss function to enhance the classification effect, the cosine annealing algorithm to optimize the training process, and the improved k-nearest neighbors multi-scale grouping method to enhance the model’s ability to segment the point cloud with complex morphology. …”

Table 1_Segmentation of tobacco shred point cloud and 3-D measurement based on improved PointNet++ network with DTC algorithm.docx by Yihang Wang (4731429)

“…This model combines the weighted cross-entropy loss function to enhance the classification effect, the cosine annealing algorithm to optimize the training process, and the improved k-nearest neighbors multi-scale grouping method to enhance the model’s ability to segment the point cloud with complex morphology. …”