Results 171 to 180 of about 5,701,734 (271)

Optimization of the Production of Rubber Compounds Using Mathematical Models

Advanced Engineering Materials, EarlyView.
Rubber compounds were mixed in a batch internal mixer, and symbolic regression was used to derive mathematical models linking recipe and process parameters to ram path, torque, and mixing quality (incorporation, dispersion, distribution). Subsequent optimization with evolutionary algorithms identified operating conditions that reduce specific energy ...
Anke Bardehle, Andreas Limper, Ulrich Giese, Carlos Lucke, Mario Beiner, Christoph Gögelein, Lisa Leuchtenberger‐Engel, Christian Hopmann   +7 more
wiley   +1 more source

SCAGC-UNet: Graph Convolutional Network with Spatial and Channel Attention for Medical Image Segmentation. [PDF]

J Imaging
Hu X, Liu X, Jiang J, Hao Z, Qiao L.
europepmc   +1 more source

Modular Critical Element Recycling Platform Using a Nanoporous Additively Manufactured Gyroid

Advanced Engineering Materials, EarlyView.
A modular recycling platform integrates 3D‐printed nanoporous gyroid structures to enable efficient critical element recovery. This system utilizes a hierarchical architecture, combining macroscopic channels with polymerization‐induced nanoscale porosity. By systematically tuning structural wall thickness and resin formulation, the platform achieves an
Xiangyu Gao, Keldy Mason, Mert Gülçür, Rachel Paddock‐Lamb, Prince Alvin Fofanah, Zachariah A. Page, Michael Cullinan   +6 more
wiley   +1 more source

A multi-modal profiling workflow of transcriptomics and biomineralization demonstrated on a tooth-on-chip model. [PDF]

MethodsX
Huang C, Tijhof C, Ji W, Yang F, Walboomers XF.   +4 more
europepmc   +1 more source

Inverse Identification of Energy‐Dependent Laser Absorptivity in NiTi Laser Powder‐Bed Fusion via Calibrated Melt Pool Simulation

Advanced Engineering Materials, EarlyView.
A combined experimental–computational framework identifies energy‐dependent laser absorptivity for NiTi in laser powder‐bed fusion, applicable to conduction and transition modes. Single‐track experiments and thermofluid smoothed particle hydrodynamics simulations are coupled through inverse analysis of melt pool geometry.
Mohamadreza Afrasiabi, Hadi Moutablaleh, Ignatius A. Setiawan, Mehrshad Mehrpouya   +3 more
wiley   +1 more source

Quantized Plasmon Modes for Metallic Nanoparticles of Arbitrary Shape with a Generic Dielectric Function. [PDF]

J Phys Chem Lett
Romanelli M, Gil G, Corni S.
europepmc   +1 more source

Machine Learning‐Supported Analysis for Predicting and Visualizing Nonlinear Relationships Between Material Properties in Electroplated Chromium Layers

Advanced Engineering Materials, EarlyView.
This study applies machine learning regression to predict chromium layer thickness in decorative trivalent chromium electroplating, using 441 experiments from laboratory‐scale (1L) and pilot‐scale (14L) setups. Tree‐based models, particularly CatBoost, outperformed linear regression by capturing nonlinear parameter interactions (R2$R^2$ up to 0.77 ...
Christoph Baumer, Christopher Mai, Luca Eisentraut, Andreas Bund, Ricardo Buettner   +4 more
wiley   +1 more source

Topology-aware hybrid graph-transformer network for Alzheimer's disease diagnosis from structural magnetic resonance imaging. [PDF]

Front Comput Neurosci
Gazem NA, Latif S, Ghaban W, Qasem SN.
europepmc   +1 more source

Influence of Scan Strategies in Electron Beam Powder Bed Fusion on Solidification, Microstructure, and High‐Temperature Compressive Properties of γ′‐Strengthened Inconel 738LC

Advanced Engineering Materials, EarlyView.
Experiments and thermophysical simulations were conducted to investigate the electron beam powder bed fusion electron beam (PBF‐EB/M) process for the γ′‐strengthened nickel‐based superalloy Inconel 738LC. The results demonstrate the impact of process‐induced microstructural variations on high‐temperature mechanical behavior, providing a basis for ...
Jan Niklas Petenati, Karthik Kumar Banku, Prasanth Bondi, Ivan Zhirnov, Yangyiwei Yang, Gordon Schmidt, Jürgen Christen, Georg Hasemann, Manja Krüger, Bai‐Xiang Xu, Bilal Gökce, Silja‐Katharina Rittinghaus   +11 more
wiley   +1 more source

HLR-UFS: Hessian-based Unsupervised Feature Selection using Low-Rank Approximation. [PDF]

PLoS One
Zhang J, Lin W, Liu Y, Tu S, Peng M.
europepmc   +1 more source