Blockchain, a specific distributed database which maintains a list of data records against tampering and corruption, has aroused wide interests and become a hot topic in the real world. Nevertheless, the increasingly heavy storage consumption brought by the full-replication data storage mechanism, becomes a bottleneck to the system scalability. To address this problem, a reliable storage scheme named BFT-Store [1], integrating erasure coding with Byzantine Fault Tolerance (BFT), was proposed recently. While, three critical problems are still left open: (i) The complex re-initialization process of the blockchain when the number of nodes varies; (ii) The high computational overload of downloading data; (iii) The massive communication on the network.This paper proposes a better trade-off for blockchain storage scheme termed PartitionChain which addresses the above three problems, maintaining the merits of BFT-Store. Firstly, our scheme allows the original nodes to merely update a single aggregate signature (e.g., 320 bits) when the number of nodes varies. Using aggregate signatures as the proof of the encoded data not only saves the storage costs but also gets rid of the trusted third party. Secondly, the computational complexity of retrieving data by decoding, compared to BFT-Store, is greatly reduced by about 2 18 times on each node. Thirdly, the amount of transmitted data for recovering each block is reduced from O(n) (assuming n is the number of nodes) to O(1), by partitioning each block into smaller pieces and applying Reed-Solomon coding to each block. Furthermore, this paper also introduces a reputation ranking system where the malicious behaviors of the nodes can be detected and marked, enabling PartitionChain to check the credits of each node termly and expel the nodes with misbehavior to the specific extent. Comparing with BFT-Store, our scheme allows blockchain system to suit dynamic network with higher efficiency and scalability.

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Consistent with CDC recommendations and government mandates during COVID, masks are commonly required/suggested in public gathering places like workplaces and schools. Considering the implications for human factors research, the extent to which mask-wearing impacts cognitive abilities and/or workload assessment from cardiac indicators are unknown. This study investigated these effects by engaging subjects in cognitively-loading tasks (math or memory) while not wearing masks, and additionally by wearing one of three types of masks (surgical, N95, and cloth). Results suggest that mask-wearing does not have significant impact on task performance data (accuracy, total question answered) or cardiac indices (heart rate). This study can foster a deeper understanding of the influence of mask-wearing on actual and perceived cognitive workload, and how mask-wearing may mitigate common workload assessment methods. These findings are valuable for informing human subjects research during COVID, and in identifying implications of mask-wearing on domains that impose cognitive work demands.

The high storage costs brought by the full-replication storage strategy adopted in most existing blockchain systems have become the main bottleneck to system scalability. To address the above, we propose an asynchronous committee-based blockchain storage strategy named lightweight BFT (LBFT), which can be applied to more diverse scenarios with better system performance. It is the first blockchain storage scheme that is designed on the conception of the zero-trust model, achieving higher-level security and fending off internal, as well as external attackers. In addition, it makes the following progress on system performance on the premise of maintaining the merits of the blockchain: (1) decreases communication complexity by involving only a part of the nodes in each decoding round; (2) enhances the robustness of the scheme regardless of the time assumption of the network; (3) improves the computational efficiency in the encoding and decoding process; and (4) reduces the storage costs and improves system scalability. In addition, we implemented experiments on LBFT and two other existing blockchain-based storage strategies, and the experimental results showed that LBFT indeed has significant improvements in system performance.