No abstract

Shot peening is a surface cold working process used to enhance the fatigue life of metallic parts or members. Coverage is a major parameter of shot peening process, which is defined as the percentage of the sum of peened area over the total area on the surface of the specimen. In this paper, a new method is proposed to estimate the full coverage for simulation of the shot peening process. A dynamic plastic model of the shot peening process with the aid of Matlab TM code is also presented using the finite element method (FEM). The numerical results show that the coverage, shot velocity and radius significantly affect the residual stress distribution of the target material, AISI 4340 steel. In addition, a double-shot peening process is also studied in simulation to consider its effect on compressive residual stress distribution.

The feeling of touch is an essential human sensation. Four types of mechanoreceptors (i.e., FA-I, SA-I, FA-II, and SA-II) in human skin signalize physical properties, such as shape, size, and texture, of an object that is touched and transmit the signal to the brain. Previous studies attempted to investigate the mechanical properties of skin microstructure and their effect on mechanoreceptors by using finite element modeling. However, very few studies have focused on the three-dimensional microstructure of dermal papillae, and this is related to that of FA-I receptors. A gap exists between conventional 2D models of dermal papillae and the natural configuration, which corresponds to a complex and uneven structure with depth. In this study, the three-dimensional microstructure of dermal papillae is modeled, and the differences between two-dimensional and three-dimensional aspects of dermal papillae on the strain energy density at receptor positions are examined. The three-dimensional microstructure has a focalizing effect and a localizing effect. Results also reveal the potential usefulness of these effects for tactile sensor design, and this may improve edge discrimination.

Humans and now computers can derive subjective valuations from sensory events although such transformation process is largely a black box. In this study, we elucidate unknown neural mechanisms by comparing representations of humans and convolutional neural networks (CNNs). We optimized CNNs to predict aesthetic valuations of paintings and examined the relationship between the CNN representations and brain activity by using multivoxel pattern analysis. The activity in the primary visual cortex was similar to computations in shallow CNN layers, while that in the higher association cortex was similar to computations in deeper layers, being consistent with the principal gradient that connects unimodal to transmodal brain regions. As a result, representations of the hidden layers of CNNs can be understood and visualized by the correspondence with brain activity. These relations can provide parallels between artificial intelligence and neuroscience.

The non-stationarity of resting-state brain activity has received increasing attention in recent years. Functional connectivity (FC) analysis with short sliding windows and coactivation pattern (CAP) analysis are two widely used methods for assessing the non- stationary characteristics of brain activity observed with functional magnetic resonance imaging (fMRI). However, whether these techniques adequately capture non-stationarity needs to be verified. In this study, we found that the results of CAP analysis were similar for real fMRI data and simulated stationary data with matching covariance structures and spectral contents. We also found that, for both the real and simulated data, CAPs were clustered into spatially heterogeneous modules. Moreover, for each of the modules in the real data, a spatially similar module was found in the simulated data. The present results suggest that care needs to be taken when interpreting observations drawn from CAP analysis as it does not necessarily reflect non-stationarity or a mixture of states in resting brain activity.