Performance comparison with other papers. حسب Hang Zhao (143592)

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Action potential of sample points in model 2. حسب Hang Zhao (143592)

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Kurtograms of different signals. حسب Hang Zhao (143592)

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Model establishment of the human heart. حسب Hang Zhao (143592)

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Action potential of sample points in model 0. حسب Hang Zhao (143592)

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Deep attention network architecture for arrhythmia classification. حسب Hang Zhao (143592)

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Bayesian optimization results of key hyperparameters. حسب Hang Zhao (143592)

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DataSheet1_Fast and precise dose estimation for very high energy electron radiotherapy with graph neural networks.PDF حسب Lorenzo Arsini (15083796)

<b>FHUFT — Fractal Harmonic Unified Framework Technology Registration Package (v2025.11.02h, Cryptographic Provenance)</b> حسب Oleksandr Zhovnirovych (22501424)

Supplementary file 1_Insight elements of mathematical problem solving in generally gifted and mathematical experts: ERP amplitudes in PO electrodes.pdf حسب Ilana Waisman (20997701)

Multimaterial Metamaterial Inverse Design via Machine Learning for Tailorable and Reusable Energy Absorption حسب Xuyang Li (11431426)

"…Our method integrates multimaterial compatibility (TPU/resin/NiTi/Al alloy) with topology-morphing body-centered cubic (BCC) lattices, where nodal coordinates, beam diameters, and material parameters are co-optimized. We delve into studying the effects of material parameters, nodal coordinates, and beam diameter variations on the structural compressive performances by conducting over 20,000 simulation experiments on randomly generated BCC lattice structures using a finite element analysis. …"

Multimaterial Metamaterial Inverse Design via Machine Learning for Tailorable and Reusable Energy Absorption حسب Xuyang Li (11431426)

"…Our method integrates multimaterial compatibility (TPU/resin/NiTi/Al alloy) with topology-morphing body-centered cubic (BCC) lattices, where nodal coordinates, beam diameters, and material parameters are co-optimized. We delve into studying the effects of material parameters, nodal coordinates, and beam diameter variations on the structural compressive performances by conducting over 20,000 simulation experiments on randomly generated BCC lattice structures using a finite element analysis. …"

Multimaterial Metamaterial Inverse Design via Machine Learning for Tailorable and Reusable Energy Absorption حسب Xuyang Li (11431426)

"…Our method integrates multimaterial compatibility (TPU/resin/NiTi/Al alloy) with topology-morphing body-centered cubic (BCC) lattices, where nodal coordinates, beam diameters, and material parameters are co-optimized. We delve into studying the effects of material parameters, nodal coordinates, and beam diameter variations on the structural compressive performances by conducting over 20,000 simulation experiments on randomly generated BCC lattice structures using a finite element analysis. …"

Multimaterial Metamaterial Inverse Design via Machine Learning for Tailorable and Reusable Energy Absorption حسب Xuyang Li (11431426)

"…Our method integrates multimaterial compatibility (TPU/resin/NiTi/Al alloy) with topology-morphing body-centered cubic (BCC) lattices, where nodal coordinates, beam diameters, and material parameters are co-optimized. We delve into studying the effects of material parameters, nodal coordinates, and beam diameter variations on the structural compressive performances by conducting over 20,000 simulation experiments on randomly generated BCC lattice structures using a finite element analysis. …"

Multimaterial Metamaterial Inverse Design via Machine Learning for Tailorable and Reusable Energy Absorption حسب Xuyang Li (11431426)

"…Our method integrates multimaterial compatibility (TPU/resin/NiTi/Al alloy) with topology-morphing body-centered cubic (BCC) lattices, where nodal coordinates, beam diameters, and material parameters are co-optimized. We delve into studying the effects of material parameters, nodal coordinates, and beam diameter variations on the structural compressive performances by conducting over 20,000 simulation experiments on randomly generated BCC lattice structures using a finite element analysis. …"

Multimaterial Metamaterial Inverse Design via Machine Learning for Tailorable and Reusable Energy Absorption حسب Xuyang Li (11431426)

"…Our method integrates multimaterial compatibility (TPU/resin/NiTi/Al alloy) with topology-morphing body-centered cubic (BCC) lattices, where nodal coordinates, beam diameters, and material parameters are co-optimized. We delve into studying the effects of material parameters, nodal coordinates, and beam diameter variations on the structural compressive performances by conducting over 20,000 simulation experiments on randomly generated BCC lattice structures using a finite element analysis. …"

Multimaterial Metamaterial Inverse Design via Machine Learning for Tailorable and Reusable Energy Absorption حسب Xuyang Li (11431426)

"…Our method integrates multimaterial compatibility (TPU/resin/NiTi/Al alloy) with topology-morphing body-centered cubic (BCC) lattices, where nodal coordinates, beam diameters, and material parameters are co-optimized. We delve into studying the effects of material parameters, nodal coordinates, and beam diameter variations on the structural compressive performances by conducting over 20,000 simulation experiments on randomly generated BCC lattice structures using a finite element analysis. …"

Table 1_WCSGNet: a graph neural network approach using weighted cell-specific networks for cell-type annotation in scRNA-seq.xlsx حسب Yi-Ran Wang (5938265)

"…We introduce WCSGNet, a graph neural network-based algorithm for automatic cell-type annotation that leverages Weighted Cell-Specific Networks (WCSNs). …"

Image 1_WCSGNet: a graph neural network approach using weighted cell-specific networks for cell-type annotation in scRNA-seq.tif حسب Yi-Ran Wang (5938265)

"…We introduce WCSGNet, a graph neural network-based algorithm for automatic cell-type annotation that leverages Weighted Cell-Specific Networks (WCSNs). …"

Table 2_WCSGNet: a graph neural network approach using weighted cell-specific networks for cell-type annotation in scRNA-seq.docx حسب Yi-Ran Wang (5938265)

"…We introduce WCSGNet, a graph neural network-based algorithm for automatic cell-type annotation that leverages Weighted Cell-Specific Networks (WCSNs). …"