Enhancements to SarcGraph’s z-disc segmentation pipeline. by Saeed Mohammadzadeh (13492285)

Here we compare the performance of the original and modified SarcGraph software for detecting sarcomeres. by Saeed Mohammadzadeh (13492285)

<b>Supplementary material for "Modified nonlocal strain gradient theory for static bending, free vibration and buckling analysis of functionally graded piezoelectric nanoplates"</b... by Phạm Văn Vinh (19543726)

Data Sheet 3_Pain - related methylation driver genes affect the prognosis of pancreatic cancer patients by altering immune function and perineural infiltration.docx by Chencheng Zhang (6877691)

“…</p>Methods<p>Integrating multi-omics data from TCGA-PAAD (Pancreatic adenocarcinoma), we identified methylation driver genes (MDGs) using the MethylMix algorithm. By intersecting MDGs with pain-related gene sets and conducting multi-step regression modeling, we established a five-gene prognostic signature (PSMB8/COL17A1/BICC1/CTRC/TRIP13). …”

Data Sheet 2_Pain - related methylation driver genes affect the prognosis of pancreatic cancer patients by altering immune function and perineural infiltration.zip by Chencheng Zhang (6877691)

“…</p>Methods<p>Integrating multi-omics data from TCGA-PAAD (Pancreatic adenocarcinoma), we identified methylation driver genes (MDGs) using the MethylMix algorithm. By intersecting MDGs with pain-related gene sets and conducting multi-step regression modeling, we established a five-gene prognostic signature (PSMB8/COL17A1/BICC1/CTRC/TRIP13). …”

Instances and detailed results of the paper <i>Stochastic scheduling on a restricted batching machine</i> by Yasmin Rios Solis (3909469)

“…This function is particularly relevant in manufacturing environments where these machines are present, as meeting due dates is crucial on these bottleneck machines. …”

Optimizing Kernel Transformations to Handle Binary Class Imbalanced Dataset Classification by Vaibhavi Patel (19843282)

“…Traditional machine learning algorithms often struggle with imbalanced datasets, as they tend to bias their classification functions toward the majority class, resulting in suboptimal performance for minority classes. …”

Monthly averages of ED2 model simulations initialised with airborne lidar structure, Jan 1981-Dec 2018, Brazilian Amazon by Marcos Longo (1928929)

“…The output results correspond to the last 38 years of simulation (one full cycle of meteorological drivers), in which ED2 simulations used static stand structure (i.e., the forest structure was held constant). …”

Table 2_A machine learning-derived immune-related prognostic model identifies PLXNA3 as a functional risk gene in colorectal cancer.xlsx by Hanzhang Lyu (22163404)

“…</p>Results<p>Plexin-A3 (PLXNA3) emerged as a top risk gene within the ensemble model, which achieved strong predictive performance, surpassing conventional clinical indicators. …”

Image 3_A machine learning-derived immune-related prognostic model identifies PLXNA3 as a functional risk gene in colorectal cancer.tif by Hanzhang Lyu (22163404)

“…</p>Results<p>Plexin-A3 (PLXNA3) emerged as a top risk gene within the ensemble model, which achieved strong predictive performance, surpassing conventional clinical indicators. …”

Image 2_A machine learning-derived immune-related prognostic model identifies PLXNA3 as a functional risk gene in colorectal cancer.tif by Hanzhang Lyu (22163404)

“…</p>Results<p>Plexin-A3 (PLXNA3) emerged as a top risk gene within the ensemble model, which achieved strong predictive performance, surpassing conventional clinical indicators. …”

Image 7_A machine learning-derived immune-related prognostic model identifies PLXNA3 as a functional risk gene in colorectal cancer.tif by Hanzhang Lyu (22163404)

“…</p>Results<p>Plexin-A3 (PLXNA3) emerged as a top risk gene within the ensemble model, which achieved strong predictive performance, surpassing conventional clinical indicators. …”

Image 6_A machine learning-derived immune-related prognostic model identifies PLXNA3 as a functional risk gene in colorectal cancer.tif by Hanzhang Lyu (22163404)

“…</p>Results<p>Plexin-A3 (PLXNA3) emerged as a top risk gene within the ensemble model, which achieved strong predictive performance, surpassing conventional clinical indicators. …”

Image 5_A machine learning-derived immune-related prognostic model identifies PLXNA3 as a functional risk gene in colorectal cancer.tif by Hanzhang Lyu (22163404)

“…</p>Results<p>Plexin-A3 (PLXNA3) emerged as a top risk gene within the ensemble model, which achieved strong predictive performance, surpassing conventional clinical indicators. …”

Image 4_A machine learning-derived immune-related prognostic model identifies PLXNA3 as a functional risk gene in colorectal cancer.tif by Hanzhang Lyu (22163404)

“…</p>Results<p>Plexin-A3 (PLXNA3) emerged as a top risk gene within the ensemble model, which achieved strong predictive performance, surpassing conventional clinical indicators. …”

A. Illustration of the PredNet Architecture [57]. by Dirk Gütlin (22566778)

Data repository for paper: "Variational approach to open quantum systems with long-range competing interactions" by Dawid Hryniuk (11488600)

Groundwater suitability for the selected crops. by Ahmed S. Abuzaid (5088119)

“…Using GIS tools, raster layers were created, assigned scores (fuzzy membership functions) according to crop requirements, and complied in accordance with the weighted sum algorithm to produce final crop suitability maps. …”

Land suitability for the selected crops. by Ahmed S. Abuzaid (5088119)

“…Using GIS tools, raster layers were created, assigned scores (fuzzy membership functions) according to crop requirements, and complied in accordance with the weighted sum algorithm to produce final crop suitability maps. …”

Overall suitability maps for the selected crops. by Ahmed S. Abuzaid (5088119)

“…Using GIS tools, raster layers were created, assigned scores (fuzzy membership functions) according to crop requirements, and complied in accordance with the weighted sum algorithm to produce final crop suitability maps. …”