Activating the Mn Single Atomic Center for an Efficient Actual Active Site of the Oxygen Reduction Reaction by Spin-State Regulation.

The ligand engineering for single-atom catalysts (SACs) is considered a cutting-edge strategy to tailor their electrocatalytic activity. However, the fundamental reasons underlying the reaction mechanism and the contemplation for which the actual active site for the catalytic reaction depends on the pyrrolic and pyridinic N ligand structure remain to be fully understood. Herein, we first reveal the relationship between the oxygen reduction reaction (ORR) activity and the N ligand structure for the manganese (Mn) single atomic site by the precisely regulated pyrrolic and pyridinic N coordination environment.

Rapid Post-Earthquake Structural Damage Assessment Using Convolutional Neural Networks and Transfer Learning.

The adoption of artificial intelligence in post-earthquake inspections and reconnaissance has received considerable attention in recent years, owing to its exponential increase in computation capabilities and inherent potential in addressing disadvantages associated with manual inspections. Herein, we present the effectiveness of automated deep learning in enhancing the assessment of damage caused by the 2017 Pohang earthquake. Six classical pre-trained convolutional neural network (CNN) models are implemented through transfer learning (TL) on a small dataset, comprising 1780 manually labeled images of structural damage.

An Automated Image-Based Multivariant Concrete Defect Recognition Using a Convolutional Neural Network with an Integrated Pooling Module.

Buildings and infrastructure in congested metropolitan areas are continuously deteriorating. Various structural flaws such as surface cracks, spalling, delamination, and other defects are found, and keep on progressing. Traditionally, the assessment and inspection is conducted by humans; however, due to human physiology, the assessment limits the accuracy of image evaluation, making it more subjective rather than objective.

Multivariate Analysis of Concrete Image Using Thermography and Edge Detection.

With the growing demand for structural health monitoring system applications, data imaging is an ideal method for performing regular routine maintenance inspections. Image analysis can provide invaluable information about the health conditions of a structure's existing infrastructure by recording and analyzing exterior damages. Therefore, it is desirable to have an automated approach that reports defects on images reliably and robustly.

Aerodynamic Characteristics and Lateral Displacements of a Set of Two Buildings in a Linked Tall Building System.

This study evaluates the aerodynamic characteristics and lateral displacements of two staggered buildings in a linked-building (LB) system. Particle image velocimetry and pressure measurements are employed, and the lateral displacement is evaluated using a 3-dimensional analytical model. When the gap distance between two non-linked buildings is small, the wind flows in a narrow jet, and a strong suction is generated on the inner surfaces of the two buildings, leading to a large cross-wind-induced response.

Wind-Induced Pressure Prediction on Tall Buildings Using Generative Adversarial Imputation Network.

Wind tunnel testing techniques are the main research tools for evaluating the wind loadings of buildings. They are significant in designing structurally safe and comfortable buildings. The wind tunnel pressure measurement technique using pressure sensors is significant for assessing the cladding pressures of buildings.