We have investigated the structures of copper nanowires encapsulated in carbon nanotubes using a structural optimization process applied to the steepest descent method. The results showed that the stable morphology of the cylindrical ultrathin copper nanowires in carbon nanotubes is multishell packs consisting of coaxial cylindrical shells. As the diameter of carbon nanotubes increased, the encapsulated copper nanowires have the face-centered-cubic structure as the bulk. The circular rolling of a triangular network can explain the structures of ultrathin multishell copper nanowires encapsulated in carbon nanotubes.

We propose a nonvolatile memory based on carbon nanotubes (CNTs) serving as the key building blocks for molecular-scale computers and investigate the dynamic operations of a double-walled CNT memory element by classical molecular dynamics simulations. The localized potential energy wells achieved from both the interwall van der Waals energy and CNT-metal binding energy make the bistability of the CNT positions and the electrostatic attractive forces induced by the voltage differences lead to the reversibility of this CNT memory. The material for the electrodes should be carefully chosen to achieve the nonvolatility of this memory. The kinetic energy of the CNT shuttle experiences several rebounds induced by the collisions of the CNT onto the metal electrodes, and this is critically important to the performance of such an electrostatically telescoping CNT memory because the collision time is sufficiently long to cause a delay of the state transition. (Some figures in this article are in colour only in the electronic version)

Detecting anomalies in the Brake Operating Unit (BOU) braking system of metro trains is very important for trains’ reliability and safety. However, current periodic maintenance and inspection cannot detect anomalies at an early stage. In addition, constructing a stable and accurate anomaly detection system is a very challenging task. Hence, in this work, we propose a method for detecting anomalies of BOU on metro vehicles using a one-class long short-term memory (LSTM) autoencoder. First, we extracted brake cylinder (BC) pressure data from the BOU data since one of the anomaly cases of metro trains is that BC pressure relief time is delayed by 4 s. After that, extracted BC pressure data is split into subsequences which are fed into our proposed one-class LSTM autoencoder which consists of two LSTM blocks (encoder and decoder). The one-class LSTM autoencoder is trained using training data which only consists of normal subsequences. To detect anomalies from test data that contain abnormal subsequences, the mean absolute error (MAE) for each subsequence is calculated. When the error is larger than a predefined threshold which was set to the maximum value of MAE in the training (normal) dataset, we can declare that example an anomaly. We conducted the experiments with the BOU data of metro trains in Korea. Experimental results show that our proposed method can detect anomalies of the BOU data well.