The top panels show the effect of different factors on choosing the optimal sequence of action. by Can Eren Sezener (6449291)

Experiment 4: Evaluating modularity-based clustering methods on tree-of-cliques networks. by Yasamin Tabatabaee (18095491)

Decision tree*. by Gabrielle Bonnet (11614471)

Data_Sheet_1_Crystal Structure Prediction of Binary Alloys via Deep Potential.PDF by Haidi Wang (9694004)

Predicting Thermal Decomposition Temperature of Binary Imidazolium Ionic Liquid Mixtures from Molecular Structures by Hongpeng He (348094)

“…Both in silico design and data analysis descriptors and norm index were employed to encode the structural characteristics of binary IL mixtures. …”

Data_Sheet_1_Posiform planting: generating QUBO instances for benchmarking.pdf by Georg Hahn (12530469)

Optimal <i>Q</i> analysis for chromosome 10. by Dolores Bernenko (16528426)

Calibration plots for all models on the Derivation cohort. by Agni Orfanoudaki (8878097)

Secure MANET routing with blockchain-enhanced latent encoder coupled GANs and BEPO optimization by Sandeep Jagonda Patil (22048337)

“…The performance of the proposed LEGAN-BEPO-BCMANET technique attains 29.786%, 19.25%, 22.93%, 27.21%, 31.02%, 26.91%, and 25.61% greater throughput, compared to existing methods like Blockchain-based BATMAN protocol utilizing MANET with an ensemble algorithm (BATMAN-MANET), Block chain-based trusted distributed routing scheme with optimized dropout ensemble extreme learning neural network in MANET (DEELNN-MANET), A secured trusted routing utilizing structure of a new directed acyclic graph-blockchain in MANET internet of things environment (DAG-MANET), An Optimized Link State Routing Protocol with Blockchain Framework for Efficient Video-Packet Transmission and Security over MANET (OLSRP-MANET), Auto-metric Graph Neural Network based Blockchain Technology for Protected Dynamic Optimum Routing in MANET (AGNN-MANET) and Data security-based routing in MANETs under key management process (DSR-MANET) respectively.…”

Analysis and design of algorithms for the manufacturing process of integrated circuits by Sonia Fleytas (16856403)

The data processing and analysis pipeline. by Xin Zhang (35492)

Algoritmo de clasificación de expresiones de odio por tipos en español (Algorithm for classifying hate expressions by type in Spanish) by 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). …”

Most cost-effective option* when TCZ is available. by Gabrielle Bonnet (11614471)

Firing patterns of a-thorny cells encode more information about the statistics of presynaptic inputs. by Daniele Linaro (227790)

Solubility Prediction of Different Forms of Pharmaceuticals in Single and Mixed Solvents Using Symmetric Electrolyte Nonrandom Two-Liquid Segment Activity Coefficient Model by Getachew S. Molla (6416744)

“…The methodology incorporates key features of the symmetric eNRTL-SAC model structure to reduce the number of parameters and uses a hybrid of global search algorithms for parameter estimation. …”

Supplementary Material for: Penalized Logistic Regression Analysis for Genetic Association Studies of Binary Phenotypes by Yu Y. (3096192)

“…Our estimate of m is the maximizer of a marginal likelihood obtained by integrating the latent log-ORs out of the joint distribution of the parameters and observed data. We consider two approximate approaches to maximizing the marginal likelihood: (i) a Monte Carlo EM algorithm (MCEM) and (ii) a Laplace approximation (LA) to each integral, followed by derivative-free optimization of the approximation. …”

Optimal Timings Codebook.xlsx by Emma Spillane (11249121)

Surround Suppression mechanism. by Elnaz Nemati (21402730)

Optimal Time Intervals of Breech Births Dataset by Emma Spillane (11249121)

Scatter plots for UTS prediction for heat treated and non-heat treated samples. by Vijaykumar S. Jatti (19251057)