A Secure and Efficient Audit Mechanism for Dynamic Shared Data in Cloud Storage

With popularization of cloud services, multiple users easily share and update their data through cloud storage. For data integrity and consistency in the cloud storage, the audit mechanisms were proposed. However, existing approaches have some security vulnerabilities and require a lot of computational overheads. This paper proposes a secure and efficient audit mechanism for dynamic shared data in cloud storage. The proposed scheme prevents a malicious cloud service provider from deceiving an auditor. Moreover, it devises a new index table management method and reduces the auditing cost by employing less complex operations. We prove the resistance against some attacks and show less computation cost and shorter time for auditing when compared with conventional approaches. The results present that the proposed scheme is secure and efficient for cloud storage services managing dynamic shared data

Computational Discovery for Crafting Multi-dimensional and Multi-functional Metal-Organic Framework Composites

Rational design of multiple material components to create composite materials for synergistic enhancement is a crucial strategy in materials science. The combination of two-dimensional (2D) and three-dimensional (3D) metal-organic frameworks (MOFs) has great potential for creating multi-dimensional and multi-functional composites, expanding the material space for various applications. In this study, we developed a novel screening algorithm to construct 2D-MOF@3D-MOF composite structures using the intrinsic geometrical information of each MOF. Our algorithm was designed to prioritize synthesizability and identified several pairs of 2D-MOFs and 3D-MOFs. The screening results revealed that Ni-HHTP@UiO-66, a previously synthesized composite material, was among the potential candidates. Furthermore, the 2D-MOF@3D-MOF composite candidate that passed our algorithm exhibited superior mechanical strength compared to the mismatched composite. Our research advances the field of MOF by providing a practical screening algorithm for identifying suitable 2D-MOF@3D-MOF composite candidates and paves the way for the discovery of new materials with enhanced properties

Weighted Consensus Protocols Design based on Network Centrality for Multi-agent Systems with Sampled-data

International audienceThis paper aims at constructing and analyzing an efficient framework for the leader-following consensus protocol in multi-agent systems (MASs). We propose two novel consensus protocols weighted by calculating the betweenness and eigenvector centralities for agent and link which are determined by the interconnection structure of MASs. The concepts of centrality were introduced in the field of social science. Ultimately, the use of the proposed protocols can be described with regard to not only the number of each agent's neighbors, which was utilized in the existing works, but also more information about agents through considering two such centralities. By utilizing the Lyapunov method and some mathematical techniques, the leader-following guaranteed cost consensus conditions for MASs with the proposed protocols and sampled-data will be established in terms of linear matrix inequalities (LMIs). Based on the result of consensus criteria, two new protocol design methods which utilize the betweenness and eigenvector centralities will be proposed. Finally, some simulation results are given to illustrate the advantages of the proposed protocols in point of the robustness on sampling interval and the transient consensus performance