In patients receiving fibrinolysis for ST-elevation myocardial infarction, treatment with enoxaparin throughout the index hospitalization is superior to treatment with unfractionated heparin for 48 hours but is associated with an increase in major bleeding episodes. These findings should be interpreted in the context of net clinical benefit. (ClinicalTrials.gov number, NCT00077792.).

Thanks to the rapid development in mobile vehicles and wireless technologies, the Internet of Vehicles (IoV) has become an attractive application that can provide a large number of mobile services for drivers. Vehicles can be informed of the mobile position, direction, speed, and other real-time information of nearby vehicles to avoid traffic jams and accidents. However, the environments of IoV could be dangerous in the absence of security protections. Due to the openness and self-organization of IoV, there are enormous malicious attackers. To guarantee the safety of mobile services, we propose an effective decentralized authentication mechanism for IoV on the basis of the consensus algorithm of blockchain technology. The simulation under the veins framework is carried out to verify the feasibility of the scheme in reducing the selfish behavior and malicious attacks in IoV.INDEX TERMS Blockchain, Internet of Vehicles, security and privacy, consensus algorithm.

Distributed Ledger Technologies (DLTs) and blockchain systems have received enormous academic, government, and commercial interest in recent years. This article surveys the integration of DLTs within another life-changing technology, the Internet of Things (IoT). IoT-based applications, such as smart home, smart transport, supply chain, smart healthcare, and smart energy, promise to boost the efficiency of existing infrastructures and change every facet of our daily life. This article looks into the challenges faced by such applications and reviews a comprehensive selection of existing DLT solutions to those challenges. We also identify issues for future research, including DLT security and scalability, multi-DLT applications, and survival of DLT in the post-quantum world.

A novel method of using the spiking neural networks (SNNs) and the electroencephalograph (EEG) processing techniques to recognize emotion states is proposed in this paper. Three algorithms including discrete wavelet transform (DWT), variance and fast Fourier transform (FFT) are employed to extract the EEG signals, which are further taken by the SNN for the emotion classification. Two datasets, i.e., DEAP and SEED, are used to validate the proposed method. For the former dataset, the emotional states include arousal, valence, dominance and liking where each state is denoted as either high or low status. For the latter dataset, the emotional states are divided into three categories (negative, positive and neutral). Experimental results show that by using the variance data processing technique and SNN, the emotion states of arousal, valence, dominance and liking can be classified with accuracies of 74%, 78%, 80% and 86.27% for the DEAP dataset, and an overall accuracy is 96.67% for the SEED dataset, which outperform the FFT and DWT processing methods. In the meantime, this work achieves a better emotion classification performance than the benchmarking approaches, and also demonstrates the advantages of using SNN for the emotion state classifications.