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Self-assembled systems are normally composed of incompressible building blocks, which constrain their space filling efficiency. Yu et al. show programmable, densely packed clusters using thermally expandable soft microparticles, whereby the self-assembling process is realized via a jamming transition.

This study evaluates the mechanical and electrical properties of copper-clad aluminum (CCA) wire prepared with a total cross-section reduction of 89% through a multistage cold drawing process and subjected to annealing at various temperatures. In addition to the CCA wire, individual samples of oxygen-free copper and aluminum, drawn with a cross-sectional reduction of 50%, were annealed under the same temperature conditions to enable a comparative analysis. Tensile tests for strength and elongation measurements were conducted, while electrical conductivity was assessed through resistivity tests. SEM and EDS analyses were performed to examine the diffusion thickness and the composition of intermetallic compounds generated at the Al/Cu interface of CCA wire. The tensile strength of the CCA wire decreased and its elongation increased up to 250 °C, after which were maintained. As the annealing temperature increased, intermetallic compound layers of Al2Cu, AlCu, and Al4Cu9 were formed at the Al/Cu interface of the CCA wire, and their thickness increased. Electrical conductivity reaches a maximum at 200 °C and then continuously decreases, showing a negative linear correlation with an increase in the diffusion layer thickness of intermetallic compounds. The study confirmed that cold-drawn CCA wire achieves stable mechanical properties and maximum electrical conductivity at the optimal annealing temperature.

The present study was conducted to evaluate the analytical precision of finite element analysis models of wind turbine bearings. In the finite element analysis models, balls were modeled as finite element meshes as a solid model or replaced by nonlinear springs as two kinds of spring models. In addition, test bench modeling was performed to calculate the displacement following the application of a turnover moment by means of a global model analysis and to calculate the contact stress by means of a sub-model analysis. The comparison of the results of the finite element analyses with the results of the bearing bench test showed that the analytical precision was 17% in the single-spring model, 9% in the Daidie spring model, and 3% in the finite element mesh ball model, indicating that the finite element mesh ball model exhibited the highest precision.

Recently, federated learning (FL) has been receiving great attention as an effective machine learning method to avoid the security issue in raw data collection, as well as to distribute the computing load to edge devices. However, even though wireless communication is an essential component for implementing FL in edge networks, there have been few works that analyze the effect of wireless networks on FL. In this paper, we investigate FL in small-cell networks where multiple base stations (BSs) and users are located according to a homogeneous Poisson point process (PPP) with different densities. We comprehensively analyze the effects of geographic node deployment on the model aggregation in FL on the basis of stochastic geometry-based analysis. We derive the closed-form expressions of coverage probability with tractable approximations and discuss the minimum required BS density for achieving a target model aggregation rate in small-cell networks. Our analysis and simulation results provide insightful information for understanding the behaviors of FL in small-cell networks; these can be exploited as a guideline for designing the network facilitating wireless FL.