Stability of cancer clustering results. حسب Teemu J. Rintala (19333003)

Size of the smallest cluster. حسب Teemu J. Rintala (19333003)

Cancer subtype agreement. حسب Teemu J. Rintala (19333003)

Cancer subtype agreement for all numbers of clusters. حسب Teemu J. Rintala (19333003)

Stability of cancer clustering results with different pathway sets. حسب Teemu J. Rintala (19333003)

Stability of cancer clustering results with different feature filters. حسب Teemu J. Rintala (19333003)

Cancer subtype agreement with different pathway sets. حسب Teemu J. Rintala (19333003)

Algoritmo de clasificación de expresiones de odio por tipos en español (Algorithm for classifying hate expressions by type in Spanish) حسب Daniel Pérez Palau (11097348)

"…</li></ul><p dir="ltr"><b>File Structure</b></p><p dir="ltr">The code generates and saves:</p><ul><li>Weights of the trained model (.h5)</li><li>Configured tokenizer</li><li>Training history in CSV</li><li>Requirements file</li></ul><p dir="ltr"><b>Important Notes</b></p><ul><li>The model excludes category 2 during training</li><li>Implements transfer learning from a pre-trained model for binary hate detection</li><li>Includes early stopping callbacks to prevent overfitting</li><li>Uses class weighting to handle category imbalances</li></ul><p dir="ltr">The process of creating this algorithm is explained in the technical report located at: Blanco-Valencia, X., De Gregorio-Vicente, O., Ruiz Iniesta, A., & Said-Hung, E. (2025). …"

The process of optimizing RPGA by Q-Learning. حسب Yicheng Liu (2179626)

"…A case study of a bidirectional disruption during the 08:00–10:00 on the section of Xi’an Metro Line 2 demonstrates that: (1) The proposed model exhibits stronger robustness under demand uncertainty, achieving a reduction of 3 dispatched vehicles and a cost saving of 9,439 RMB by moderately increasing passenger costs by 850 RMB and extending bridging time; (2) The RPGA algorithm outperforms Non-dominated Sorting Genetic Algorithm II (NSGA-II), Reinforcement Learning-based NSGA-II (RLNSGA-II), and Multi-objective Particle Swarm Optimization Algorithm (MOPSO) in hypervolume (HV), generational distance (GD), and non-dominated ratio (NDR); (3) Increasing the rated passenger capacity within a certain range can reduce average passenger delays but correspondingly raises transportation costs. …"

Xi’an metro line 2 disruption stations. حسب Yicheng Liu (2179626)

"…A case study of a bidirectional disruption during the 08:00–10:00 on the section of Xi’an Metro Line 2 demonstrates that: (1) The proposed model exhibits stronger robustness under demand uncertainty, achieving a reduction of 3 dispatched vehicles and a cost saving of 9,439 RMB by moderately increasing passenger costs by 850 RMB and extending bridging time; (2) The RPGA algorithm outperforms Non-dominated Sorting Genetic Algorithm II (NSGA-II), Reinforcement Learning-based NSGA-II (RLNSGA-II), and Multi-objective Particle Swarm Optimization Algorithm (MOPSO) in hypervolume (HV), generational distance (GD), and non-dominated ratio (NDR); (3) Increasing the rated passenger capacity within a certain range can reduce average passenger delays but correspondingly raises transportation costs. …"

Methodology block diagram. حسب Gahao Chen (21688843)

"…Six machine learning algorithms - Random Forest (RF), AdaBoost, Light Gradient Boosting Machine (LightGBM), eXtreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), and Tabular Prior-data Fitted Network version 2.0 (TabPFN-V2) - were implemented with five-fold cross-validation to optimize model hyperparameters. …"

May 1st −7st Metro Line 2 OD Statistics Table. حسب Yicheng Liu (2179626)

"…A case study of a bidirectional disruption during the 08:00–10:00 on the section of Xi’an Metro Line 2 demonstrates that: (1) The proposed model exhibits stronger robustness under demand uncertainty, achieving a reduction of 3 dispatched vehicles and a cost saving of 9,439 RMB by moderately increasing passenger costs by 850 RMB and extending bridging time; (2) The RPGA algorithm outperforms Non-dominated Sorting Genetic Algorithm II (NSGA-II), Reinforcement Learning-based NSGA-II (RLNSGA-II), and Multi-objective Particle Swarm Optimization Algorithm (MOPSO) in hypervolume (HV), generational distance (GD), and non-dominated ratio (NDR); (3) Increasing the rated passenger capacity within a certain range can reduce average passenger delays but correspondingly raises transportation costs. …"

Validation of image processing steps and metric results using images of a microfluidic device. حسب Gavrielle R. Untracht (11820311)

tUbenet Foundation Model Weights حسب Natalie Holroyd (7099391)

Table 1_Pixel-wise navigation line extraction of cross-growth-stage seedlings in complex sugarcane fields and extension to corn and rice.docx حسب Hongwei Li (17402)

"…In response to such challenges, we proposed a generalizable navigation line extraction algorithm using classical image processing technologies. …"

Table 2_Pixel-wise navigation line extraction of cross-growth-stage seedlings in complex sugarcane fields and extension to corn and rice.docx حسب Hongwei Li (17402)

"…In response to such challenges, we proposed a generalizable navigation line extraction algorithm using classical image processing technologies. …"

Image 1_Pixel-wise navigation line extraction of cross-growth-stage seedlings in complex sugarcane fields and extension to corn and rice.tif حسب Hongwei Li (17402)

"…In response to such challenges, we proposed a generalizable navigation line extraction algorithm using classical image processing technologies. …"

Image 3_Pixel-wise navigation line extraction of cross-growth-stage seedlings in complex sugarcane fields and extension to corn and rice.tif حسب Hongwei Li (17402)

"…In response to such challenges, we proposed a generalizable navigation line extraction algorithm using classical image processing technologies. …"

Image 2_Pixel-wise navigation line extraction of cross-growth-stage seedlings in complex sugarcane fields and extension to corn and rice.tif حسب Hongwei Li (17402)

"…In response to such challenges, we proposed a generalizable navigation line extraction algorithm using classical image processing technologies. …"

Data Sheet 4_Identification of a signature gene set for oxaliplatin sensitivity prediction in colorectal cancer.pdf حسب Xiaopeng Zhan (4170574)

"…Subsequent PFS analysis narrowed this set to four key genes (AXDND1, BAMBI, MAPK8IP2, and BMP7) that were significantly associated with patient survival following oxaliplatin-based therapy. External validation confirmed that different combinations of these four genes consistently and robustly predicted oxaliplatin sensitivity. …"