Python code for a rule-based NLP model for mapping circular economy indicators to SDGs حسب Zahir Barahmand (18008947)

"…The package includes:</p><ul><li>The complete Python codebase implementing the classification algorithm</li><li>A detailed manual outlining model features, requirements, and usage instructions</li><li>Sample input CSV files and corresponding processed output files to demonstrate functionality</li><li>Keyword dictionaries for all 17 SDGs, distinguishing strong and weak matches</li></ul><p dir="ltr">These materials enable full reproducibility of the study, facilitate adaptation for related research, and offer transparency in the methodological framework.…"

Simulation of multiple agents minimizing the dissonance function following homophily حسب Mohsen Raoufi (18775639)

Discovery of Protein Modifications Using Differential Tandem Mass Spectrometry Proteomics حسب Paolo Cifani (1575613)

"…Termed SAMPEI for spectral alignment-based modified peptide identification, this open-source algorithm is designed for the discovery of functional protein and peptide signaling modifications, without prior knowledge of their identities. …"

Discovery of Protein Modifications Using Differential Tandem Mass Spectrometry Proteomics حسب Paolo Cifani (1575613)

"…Termed SAMPEI for spectral alignment-based modified peptide identification, this open-source algorithm is designed for the discovery of functional protein and peptide signaling modifications, without prior knowledge of their identities. …"

The training results of WA-YOLO and YOLOv8 on the custom blasting site dataset are presented, where (a) shows the mAP_0.5 comparison, and (b) illustrates the box loss tra... حسب LinNa Li (5665885)

Design and implementation of DelaySSA. The input data for DelaySSA are reaction information, initial states, parameters and delays. Combining these into the simulation function, th... حسب Ziyan Jin (21021976)

Performance of the three algorithms. حسب Juanjuan Lin (2096830)

"…<div><p>Disruptive events cause decreased functionality of transportation infrastructures and enormous financial losses. …"

Modeling with uniform <i>σ</i> values. حسب Giacomo Janson (8138517)

Effect of <i>|Δd</i>_<i>n</i><i>|</i> perturbation on 3D modeling. حسب Giacomo Janson (8138517)

State Function-Based Correction: A Simple and Efficient Free-Energy Correction Algorithm for Large-Scale Relative Binding Free-Energy Calculations حسب Runduo Liu (10756379)

"…We present an efficient and straightforward State Function-based Correction (SFC) algorithm, which leverages the state function property of free energy without requiring cycle identification. …"

Revisiting the “satisfaction of spatial restraints” approach of MODELLER for protein homology modeling حسب Giacomo Janson (8138517)

"…This program implements the “modeling by satisfaction of spatial restraints” strategy and its core algorithm has not been altered significantly since the early 1990s. …"

Example of use of the slightly off-axis strategies. حسب Raul Castañeda (13932971)

Example of the fully-compensated phase reconstruction package for off-axis DHM holograms recorded in telecentric configuration. حسب Raul Castañeda (13932971)

Rosenbrock function losses for . حسب Shikun Chen (14625352)

"…This approach bridges the gap between model accuracy and optimization efficiency, offering a practical solution for optimizing non-differentiable machine learning models that can be extended to other tree-based ensemble algorithms. The method has been successfully applied to real-world steel alloy optimization, where it achieved superior performance while maintaining all metallurgical composition constraints.…"

Rosenbrock function losses for . حسب Shikun Chen (14625352)

"…This approach bridges the gap between model accuracy and optimization efficiency, offering a practical solution for optimizing non-differentiable machine learning models that can be extended to other tree-based ensemble algorithms. The method has been successfully applied to real-world steel alloy optimization, where it achieved superior performance while maintaining all metallurgical composition constraints.…"

Levy function losses for . حسب Shikun Chen (14625352)

"…This approach bridges the gap between model accuracy and optimization efficiency, offering a practical solution for optimizing non-differentiable machine learning models that can be extended to other tree-based ensemble algorithms. The method has been successfully applied to real-world steel alloy optimization, where it achieved superior performance while maintaining all metallurgical composition constraints.…"

Rastrigin function losses for . حسب Shikun Chen (14625352)

"…This approach bridges the gap between model accuracy and optimization efficiency, offering a practical solution for optimizing non-differentiable machine learning models that can be extended to other tree-based ensemble algorithms. The method has been successfully applied to real-world steel alloy optimization, where it achieved superior performance while maintaining all metallurgical composition constraints.…"

Levy function losses for . حسب Shikun Chen (14625352)

"…This approach bridges the gap between model accuracy and optimization efficiency, offering a practical solution for optimizing non-differentiable machine learning models that can be extended to other tree-based ensemble algorithms. The method has been successfully applied to real-world steel alloy optimization, where it achieved superior performance while maintaining all metallurgical composition constraints.…"

Rastrigin function losses for . حسب Shikun Chen (14625352)

"…This approach bridges the gap between model accuracy and optimization efficiency, offering a practical solution for optimizing non-differentiable machine learning models that can be extended to other tree-based ensemble algorithms. The method has been successfully applied to real-world steel alloy optimization, where it achieved superior performance while maintaining all metallurgical composition constraints.…"

Levy function losses for . حسب Shikun Chen (14625352)

"…This approach bridges the gap between model accuracy and optimization efficiency, offering a practical solution for optimizing non-differentiable machine learning models that can be extended to other tree-based ensemble algorithms. The method has been successfully applied to real-world steel alloy optimization, where it achieved superior performance while maintaining all metallurgical composition constraints.…"