Data comparison of different models for <i>D</i>_<i>5-75</i>.

This study aims to predict the significant duration (D5-75, D5-95) of seismic motion by employing machine learning algorithms. Based on three parameters (moment magnitude, fault distance, and average shear wave velocity), two additional parameters(fault top depth and epicenter mechanism parameters) were introduced in this study. The XGBoost algorithm is utilized for characteristic parameter optimization analysis to obtain the optimal combination of four parameters. We compare the prediction results of four machine learning algorithms (random forest, XGBoost, BP neural network, and SVM) and develop a new method of significant duration prediction by constructing two fusion models (stacking and weighted averaging). The fusion model demonstrates an improvement in prediction accuracy and generalization ability of the significant duration when compared to single algorithm models based on evaluation indicators and residual values. The accuracy and rationality of the fusion model are validated through comparison with existing research.</div

Comparison of <i>D</i>_5-95 prediction results of fusion model and single model.

Comparison of D5-95 prediction results of fusion model and single model.</p

BP neural network structure diagram.

This study aims to predict the significant duration (D5-75, D5-95) of seismic motion by employing machine learning algorithms. Based on three parameters (moment magnitude, fault distance, and average shear wave velocity), two additional parameters(fault top depth and epicenter mechanism parameters) were introduced in this study. The XGBoost algorithm is utilized for characteristic parameter optimization analysis to obtain the optimal combination of four parameters. We compare the prediction results of four machine learning algorithms (random forest, XGBoost, BP neural network, and SVM) and develop a new method of significant duration prediction by constructing two fusion models (stacking and weighted averaging). The fusion model demonstrates an improvement in prediction accuracy and generalization ability of the significant duration when compared to single algorithm models based on evaluation indicators and residual values. The accuracy and rationality of the fusion model are validated through comparison with existing research.</div

Compared with the existing prediction equation.

This study aims to predict the significant duration (D5-75, D5-95) of seismic motion by employing machine learning algorithms. Based on three parameters (moment magnitude, fault distance, and average shear wave velocity), two additional parameters(fault top depth and epicenter mechanism parameters) were introduced in this study. The XGBoost algorithm is utilized for characteristic parameter optimization analysis to obtain the optimal combination of four parameters. We compare the prediction results of four machine learning algorithms (random forest, XGBoost, BP neural network, and SVM) and develop a new method of significant duration prediction by constructing two fusion models (stacking and weighted averaging). The fusion model demonstrates an improvement in prediction accuracy and generalization ability of the significant duration when compared to single algorithm models based on evaluation indicators and residual values. The accuracy and rationality of the fusion model are validated through comparison with existing research.</div

Residual graph of <i>D</i>_5-95 predicted by SVM.

This study aims to predict the significant duration (D5-75, D5-95) of seismic motion by employing machine learning algorithms. Based on three parameters (moment magnitude, fault distance, and average shear wave velocity), two additional parameters(fault top depth and epicenter mechanism parameters) were introduced in this study. The XGBoost algorithm is utilized for characteristic parameter optimization analysis to obtain the optimal combination of four parameters. We compare the prediction results of four machine learning algorithms (random forest, XGBoost, BP neural network, and SVM) and develop a new method of significant duration prediction by constructing two fusion models (stacking and weighted averaging). The fusion model demonstrates an improvement in prediction accuracy and generalization ability of the significant duration when compared to single algorithm models based on evaluation indicators and residual values. The accuracy and rationality of the fusion model are validated through comparison with existing research.</div

Significant duration distribution with <i>M</i>_w and <i>R</i>_rup.

Significant duration distribution with Mw and Rrup.</p

MSE variation with iterations number for <i>D</i>_<i>5-75</i>.

This study aims to predict the significant duration (D5-75, D5-95) of seismic motion by employing machine learning algorithms. Based on three parameters (moment magnitude, fault distance, and average shear wave velocity), two additional parameters(fault top depth and epicenter mechanism parameters) were introduced in this study. The XGBoost algorithm is utilized for characteristic parameter optimization analysis to obtain the optimal combination of four parameters. We compare the prediction results of four machine learning algorithms (random forest, XGBoost, BP neural network, and SVM) and develop a new method of significant duration prediction by constructing two fusion models (stacking and weighted averaging). The fusion model demonstrates an improvement in prediction accuracy and generalization ability of the significant duration when compared to single algorithm models based on evaluation indicators and residual values. The accuracy and rationality of the fusion model are validated through comparison with existing research.</div

Comparison of different parameter combinations of significant duration (test set).

Comparison of different parameter combinations of significant duration (test set).</p

Aptamer Fiber Anchored on the Edge of a Protein Pattern: A Template for Nanowire Fabrication

How to lay down nanowires at designated positions is a challenge that undermines the development of nanowire-based devices. We demonstrate that aptamer fibers, which are formed by the self-assembly of multiple aptamers, anchor specifically on the edge of protein patterns. This edge-anchoring effect originates from the biospecific recognition between the aptamer and its target protein. The fractal- shaped aptamer fibers are 1−6 nm high and can be tens of micrometers long. Once these edge-bound fibers have formed, they can serve as scaffolds for further assembly processes. We used these aptamer fibers as templates to fabricate palladium and streptavidin nanowires, which anchored on the pattern edges and never cross over or collapse over each other. The aptamer fiber scaffold provides a solution for fabricating and interfacing nanowires to existing surface patterns

The Boundary Molecules in a Lysozyme Pattern Exhibit Preferential Antibody Binding

Lysozyme was immobilized on a prefabricated carboxylic acid terminated chemical template, forming a tightly packed, one monolayer thick lysozyme pattern. Polyclonal anti-lysozyme antibodies can bind to the immobilized lysozyme pattern. Atomic force microscope (AFM) observation reveals that the antibodies bind to the lysozyme molecules on the pattern edge before they bind to the lysozyme molecules in the pattern interior. Better spatial accessibility and flexibility of the lysozyme molecules on the pattern edge are used to explain the observed antibody binding preference. The topographies of the lysozyme pattern also affect the antibody binding. The antibodies bind to the edge lysozyme from the top if the lysozyme pattern is half-buried in a 10 Å deep channel, whereas the antibodies bind to the edge lysozyme from the side if the lysozyme pattern is immobilized on a protruding terrace. The observed “edge effect” suggests that, for the same protein coverage, reducing the protein pattern feature to the nanoscale will improve the overall binding activity of the immobilized protein toward the antibody