Experiments on micro- and nano-mechanical systems (M/NEMS) have shown that their behavior under bending loads departs in many cases from the classical predictions using Euler-Bernoulli theory and Hooke’s law. This anomalous response has usually been seen as a dependence of the material properties on the size of the structure, in particular thickness. A theoretical model that allows for quantitative understanding and prediction of this size effect is important for the design of M/NEMS. In this paper, we summarize and analyze the five theories that can be found in the literature: Grain Boundary Theory (GBT), Surface Stress Theory (SST), Residual Stress Theory (RST), Couple Stress Theory (CST) and Surface Elasticity Theory (SET). By comparing these theories with experimental data we propose a simplified model combination of CST and SET that properly fits all considered cases, therefore delivering a simple (two parameters) model that can be used to predict the mechanical properties at the nanoscale.

Application of the stent for treatment of the internal carotid artery (ICA) aneurysms has been extensively increased in recent decades. In the present work, stent-induced deformations of the parent vessel of ICA aneurysms are fully investigated. This study tries to visualize blood stream and calculated hemodynamic factors inside the four ICA aneurysms after deformations of parent vessel. For the simulation of the non-Newtonian blood stream, computational fluid dynamic is applied with one-way Fluid–Solid interaction (FSI) approach. Four ICA aneurysms with different ostium sizes and neck vessel angle are selected for this investigation. Wall shear stress on wall of aneurysm is analyzed in two angles of deformation due to application of the stent. Blood flow investigation shows that the deformation of the aneurysm limited blood entrance to the sac region and this decreases the blood velocity and consequently oscillatory shear index (OSI) on the sac wall. It is also observed that the stent-induced deformation is more effective on those cases with extraordinary OSI values on aneurysm wall.

This paper explores the use of Deep Reinforcement Learning (DRL) to improve the aerodynamic stability of compressible flow around a vibrating cylinder. In uncontrolled conditions, the cylinder experiences a drag coefficient of 1.35 and an oscillatory lift coefficient with an amplitude of 0.35. By applying a classic Deep Q-Network (DQN), the lift oscillation amplitude is significantly reduced to ±0.025, marking an improvement of over 100%. The study further investigates the effects of episode count, neural network architecture, and DQN variants on performance, demonstrating the robustness of the approach. While changes to the neural network structure within the classic DQN yield limited improvements in reducing lift oscillations, both the classic and dueling DQN variants effectively control lift oscillations. Notably, the dueling DQN provides greater stability, reducing lift oscillation amplitude to as low as ±0.001. The paper also examines the effect of varying jet positions, offering valuable insights into the adaptability and generalization of the proposed DRL-based control strategy.