Transition Metal Selenide-Based Anodes for Advanced Sodium-Ion Batteries: Electronic Structure Manipulation and Heterojunction Construction Aspect.

In recent years, sodium-ion batteries (SIBs) have gained a foothold in specific applications related to lithium-ion batteries, thanks to continuous breakthroughs and innovations in materials by researchers. Commercial graphite anodes suffer from small interlayer spacing (0.334 nm), limited specific capacity (200 mAh g), and low discharge voltage (<0.

Design and Fabrication of an Integrated Hollow Concave Cilium MEMS Cardiac Sound Sensor.

In light of a need for low-frequency, high sensitivity and broadband cardiac murmur signal detection, the present work puts forward an integrated MEMS-based heart sound sensor with a hollow concave ciliary micro-structure. The advantages of a hollow MEMS structure, in contrast to planar ciliated micro-structures, are that it reduces the ciliated mass and enhances the operating bandwidth. Meanwhile, the area of acoustic-wave reception is enlarged by the concave architecture, thereby enhancing the sensitivity at low frequencies.

Design and Realization of MEMS Heart Sound Sensor with Concave, Racket-Shaped Cilium.

The biomedical acoustic signal plays an important role in clinical non-invasive diagnosis. In view of the deficiencies in early diagnosis of cardiovascular diseases, acoustic properties of and heart sounds are utilized. In this paper, we propose an integrated concave cilium MEMS heart sound sensor.

How do training experience and geographical origin of a runner affect running biomechanics?

Background: Several studies compared African runners with runners from other places with difference ethnicities to identify biomechanical factors that may contribute to their extraordinary running performance. However, most studies only assessed runners at the elite level. Whether the performance difference was a result of nature or nurture remains unclear.

Classification of runners' performance levels with concurrent prediction of biomechanical parameters using data from inertial measurement units.

Identification of runner's performance level is critical to coaching, performance enhancement and injury prevention. Machine learning techniques have been developed to measure biomechanical parameters with body-worn inertial measurement unit (IMU) sensors. However, a robust method to classify runners is still unavailable.