We demonstrate that a quiet state and large-amplitude self-sustained oscillations can coexist in a carbon nanotube subject to time-independent drive. A feature of the bistability is that it would be hysteresis free in the absence of noise, and the oscillatory state would not be seen. It is revealed by random switching between the stable states, which we observe in the time domain. We attribute the switching to fluctuations in the system and show that it displays Poisson statistics. We propose a minimalistic model that relates the emergence of the bistability to a nonmonotonic variation of nonlinear friction with the vibration amplitude. This new type of dynamical regime and the means to reveal it are generic and are of interest for various mesoscopic vibrational systems.

Analysing the contact area between head-related products and the corresponding craniofacial profile is necessary to make such products more comfortable. The study of soft tissue biomechanical experiments provides a reliable perspective for understanding such contact areas and improving the comfort. In order to obtain more accurate and visualized craniofacial biological information that can be used to guide product design, CT data of the head, face, and neck of 50 Chinese aged 18-35 years were obtained in this paper. For each subject, an individual thickness map is calculated by segmentation of the soft tissue layer and wall-thickness calculation via Mimics. The individual maps superimposed on the outer surface area of the head, face, and
neck are brought into correspondence using a non-rigid iterative closest point technique. From the correspondence an average head, face, and neck geometry and soft tissue thickness map was calculated. Statistics of the overall soft tissue thickness of the head, face, and neck is extracted, and an accurate soft tissue thickness map of the Chinese head, face, and neck is generated. This study not only lays the groundwork for future simulation experiments on head-related product design, but it also has significant implications for the fields of facial reconstruction in China.

Markerless estimation of 3D Kinematics has the great potential to clinically diagnose and monitor movement disorders without referrals to expensive motion capture labs; however, current approaches are limited by performing multiple de-coupled steps to estimate the kinematics of a person from videos. Most current techniques work in a multi-step approach by first detecting the pose of the body and then fitting a musculoskeletal model to the data for accurate kinematic estimation. Errors in training data of the pose detection algorithms, model scaling, as well the requirement of multiple cameras limit the use of these techniques in a clinical setting. Our goal is to pave the way toward fast, easily applicable and accurate 3D kinematic estimation . To this end, we propose a novel approach for direct 3D human kinematic estimation D3KE from videos using deep neural networks. Our experiments demonstrate that the proposed end-to-end training is robust and outperforms 2D and 3D markerless motion capture based kinematic estimation pipelines in terms of joint angles error by a large margin (35% from 5.44 to 3.54 degrees). We show that D3KE is superior to the multi-step approach and can run at video framerate speeds. This technology shows the potential for clinical analysis from mobile devices in the future.