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

“…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. Subsequently, the metamaterials with the specific platform stress values (from 0.015 to 4.05 MPa) and specific energy absorptions (from 0.049 to 23.377 J/g) can be inversely designed with the aid of the artificial neural networks and genetic algorithms to pinpoint optimized parameters from a 181-dimensional space. …”

Mean and root mean square errors of DOA estimate. by Chuanxi Xing (20141665)

“…Furthermore, the estimation of the DOA can be accurately carried out under low signal-to-noise ratio conditions. This method effectively utilizes the degrees of freedom provided by the virtual array, reducing noise interference, and exhibiting better performance in terms of positioning accuracy and algorithm stability.…”

Schematic diagram of maritime array arming. by Chuanxi Xing (20141665)

“…Furthermore, the estimation of the DOA can be accurately carried out under low signal-to-noise ratio conditions. This method effectively utilizes the degrees of freedom provided by the virtual array, reducing noise interference, and exhibiting better performance in terms of positioning accuracy and algorithm stability.…”

Variation of RMSE with the number of snapshots. by Chuanxi Xing (20141665)

“…Furthermore, the estimation of the DOA can be accurately carried out under low signal-to-noise ratio conditions. This method effectively utilizes the degrees of freedom provided by the virtual array, reducing noise interference, and exhibiting better performance in terms of positioning accuracy and algorithm stability.…”

Velocity of sound profile. by Chuanxi Xing (20141665)

“…Furthermore, the estimation of the DOA can be accurately carried out under low signal-to-noise ratio conditions. This method effectively utilizes the degrees of freedom provided by the virtual array, reducing noise interference, and exhibiting better performance in terms of positioning accuracy and algorithm stability.…”

Variation of RMSE with input SNR. by Chuanxi Xing (20141665)

“…Furthermore, the estimation of the DOA can be accurately carried out under low signal-to-noise ratio conditions. This method effectively utilizes the degrees of freedom provided by the virtual array, reducing noise interference, and exhibiting better performance in terms of positioning accuracy and algorithm stability.…”

Coprime array. by Chuanxi Xing (20141665)

“…Furthermore, the estimation of the DOA can be accurately carried out under low signal-to-noise ratio conditions. This method effectively utilizes the degrees of freedom provided by the virtual array, reducing noise interference, and exhibiting better performance in terms of positioning accuracy and algorithm stability.…”

Variation of RMSE with different grid spacing. by Chuanxi Xing (20141665)

“…Furthermore, the estimation of the DOA can be accurately carried out under low signal-to-noise ratio conditions. This method effectively utilizes the degrees of freedom provided by the virtual array, reducing noise interference, and exhibiting better performance in terms of positioning accuracy and algorithm stability.…”

Spatial power spectrum of compact sound source. by Chuanxi Xing (20141665)

“…Furthermore, the estimation of the DOA can be accurately carried out under low signal-to-noise ratio conditions. This method effectively utilizes the degrees of freedom provided by the virtual array, reducing noise interference, and exhibiting better performance in terms of positioning accuracy and algorithm stability.…”

XAFS-spectra of the reference substances by Sebastian Praetz (6576515)

CBAM module architecture. by Buyu Wang (17889178)

Uniform Manifold Approximation and Projection (UMAP)-based sorting enhances detection of low-firing rate neurons and boosts mutual information. by Daniel Suárez-Barrera (22676654)

Description of data sources. by Gowtham A. Rao (8261928)

“…<div><p>Objective</p><p>This paper introduces a novel framework for evaluating phenotype algorithms (PAs) using the open-source tool, Cohort Diagnostics.…”

Comparison of Uniform Manifold Approximation and Projection (UMAP)-based and feature-based spike sorting on synthetic data. by Daniel Suárez-Barrera (22676654)

Data and codes for the paper "Machine-Learning-Enabled Spatial Pattern Mining: Evaluating the Impact of Imperfect Inputs". by Zhili Li (18836803)

High-Throughput Mass Spectral Library Searching of Small Molecules in R with NIST MSPepSearch by Andrey Samokhin (20282728)

“…Despite the availability of numerous library search algorithms, those developed by NIST and implemented in MS Search remain predominant, partly because commercial databases (e.g., NIST, Wiley) are distributed in proprietary formats inaccessible to custom code. …”

SpeLL: An Agent for Natural Language-Driven Intelligent Spectral Modeling by Jiashun Fu (20888176)

“…The core strength of SpeLL lies in its dual RAG pathways. The Code RAG provides specialized code knowledge for spectral data analysis, enabling the LLM to generate robust and domain-specific analytical scripts that address the implementation and optimization of algorithms. …”

XGBoost regressor for estimating community-level education percentile rank in China by Yanji Zhang (22363905)

Mitochondrial toxic prediction of marine alga toxins using a predictive model based on feature coupling and ensemble learning algorithms by Guangyin Jia (21075537)

“…By comparing 8 machine learning algorithms and using a weighted soft voting method to integrate the two optimal algorithms, we established 108 prediction models and identified the best ensemble learning model MACCS_LK for screening and defining its application domain. …”

Homophilic vs. by Caesar Tawfeeq (21087272)