Smart Grids (SG) rely on Home Area Networks (HAN) and Neighborhood Area Networks (NAN) to ensure efficient power distribution, real-time monitoring, and seamless communication between smart devices. Despite these adva...
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internet of Things (IoT) devices generate large amounts of data every day that can be combined with intelligent platforms for predictive analytics and scientific research. However, concerns about privacy and security ...
internet of Things (IoT) devices generate large amounts of data every day that can be combined with intelligent platforms for predictive analytics and scientific research. However, concerns about privacy and security hinder the willingness of individuals to share data. Blockchain emerged as a promising infrastructure for facilitating secure data sharing due to its decentralized, immutability, and auditable benefits. In this paper, we propose a blockchain-based cloud–edge collaborative privacy protection data sharing scheme (BCE-PPDS), which is decentralized and enables data requesters (DRs) to search data resources using smart contracts to efficiently obtain target data. To protect the identity privacy of data owners (DOs), we propose a novel certificateless linkable ring signature algorithm with efficient performance. This algorithm is not only suitable for deployment on resource-limited IoT devices, so that DOs can realize anonymous identity authentication, but also can aggregate the generated ring signatures for batch verification, so as to improve the efficiency of signature verification. In addition, we designed a key distribution algorithm using the Asmuth–Bloom secret sharing scheme to ensure the security of the key. Under the random oracle model, BCE-PPDS is provably secure. The experimental results verify that BCE-PPDS is efficient and practical.
The lymphatic system hosts a large number of therapeutic targets that can be used to modulate a wide range of diseases including cancers, autoimmune and inflammatory disorders, infectious diseases and metabolic syndro...
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The lymphatic system hosts a large number of therapeutic targets that can be used to modulate a wide range of diseases including cancers, autoimmune and inflammatory disorders, infectious diseases and metabolic syndrome;however, drug access to the lymphatic system is often challenging. Over the past decades significant efforts have been made to promote drug transport to the lymphatics through medicinal chemistry approaches, and a number of promising progresses are emerging. Nevertheless, so far it remains difficult to clearly delineate the mechanism of lymphatic drug transport and to map the design criteria for lymphotropic drug molecules, and the attempts to synthesize lymph-directing drug candidates or drug derivatives are largely in an experience-driven, trial and error basis. Furthermore, complex experimental procedures required for the study of lymphatic drug transport have limited data accumulation in the field, and this in turn hampers mechanistic studies and understanding of drug design criteria. Our current study aims to 1) review and summarize published work that assessed lymphatic drug transport by both direct measurement (e.g. determination of drug concentrations in lymph fluid) or indirect measurement (e.g. imaging methods or by comparing the changes of pharmacokinetics profile in the absence and presence of lymphatic transport blocker);2) to analyze lymphatic drug transport data of 185 drugs according to experimental models and conditions, followed by dataset regrouping according to the extent of lymphatic transport;3) to establish different Artificial Intelligence (AI) models including Graph Convolutional Network (GCN), Graph Attention Network (GAT) and Graph Transformer (GT) to predict the potential of drug transport via the lymphatics following oral administration, during which process data augmentation approaches were employed to compensate for the limited data. The results demonstrated that our model can enhance data and lymphatic drug transport p
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