Large stroke radially oriented MXene composite fiber tensile artificial muscles.

Actuation is normally dramatically enhanced by introducing so much yarn fiber twist that the fiber becomes fully coiled. In contrast, we found that usefully high muscle strokes and contractile work capacities can be obtained for non-twisted MXene (TiCT) fibers comprising MXene nanosheets that are stacked in the fiber direction. The MXene fiber artificial muscles are called MFAMs.

Block-Based Privacy-Preserving Healthcare Data Ranked Retrieval in Encrypted Cloud File Systems.

The Internet of Medical Things (IoMT) is an important application of the Internet of Things in health care. In IoMT, efficiency and user privacy are crucial for cloud storage and retrieval of healthcare data documents. Existing schemes, however, often suffer from inefficient retrieval and increased risk of privacy disclosure when dealing with massive data.

Privacy-Preserving in Healthcare Blockchain Systems Based on Lightweight Message Sharing.

Electronic medical records (EMRs) are extremely important for patients' treatment, doctors' diagnoses, and medical technology development. In recent years, the distributed healthcare blockchain system has been researched for solving the information isolated island problem in centralized healthcare service systems. However, there still exists a series of important problems such as the patients' sensitive information security, cross-institutional data sharing, medical quality, and efficiency.

A Method of Data Aggregation for Wearable Sensor Systems.

Data aggregation has been considered as an effective way to decrease the data to be transferred in sensor networks. Particularly for wearable sensor systems, smaller battery has less energy, which makes energy conservation in data transmission more important. Nevertheless, wearable sensor systems usually have features like frequently dynamic changes of topologies and data over a large range, of which current aggregating methods can't adapt to the demand.

Random and directed walk-based top-(k) queries in wireless sensor networks.

In wireless sensor networks, filter-based top-  query approaches are the state-of-the-art solutions and have been extensively researched in the literature, however, they are very sensitive to the network parameters, including the size of the network, dynamics of the sensors' readings and declines in the overall range of all the readings. In this work, a random walk-based top-  query approach called RWTQ and a directed walk-based top-  query approach called DWTQ are proposed. At the beginning of a top-  query, one or several tokens are sent to the specific node(s) in the network by the base station.

Double cluster heads model for secure and accurate data fusion in wireless sensor networks.

Secure and accurate data fusion is an important issue in wireless sensor networks (WSNs) and has been extensively researched in the literature. In this paper, by combining clustering techniques, reputation and trust systems, and data fusion algorithms, we propose a novel cluster-based data fusion model called Double Cluster Heads Model (DCHM) for secure and accurate data fusion in WSNs. Different from traditional clustering models in WSNs, two cluster heads are selected after clustering for each cluster based on the reputation and trust system and they perform data fusion independently of each other.

An integrated model for robust multisensor data fusion.

This paper presents an integrated model aimed at obtaining robust and reliable results in decision level multisensor data fusion applications. The proposed model is based on the connection of Dempster-Shafer evidence theory and an extreme learning machine. It includes three main improvement aspects: a mass constructing algorithm to build reasonable basic belief assignments (BBAs); an evidence synthesis method to get a comprehensive BBA for an information source from several mass functions or experts; and a new way to make high-precision decisions based on an extreme learning machine (ELM).