“Blocking and rebalance”mechanism-guided design strategies of bimetallic doped 2D a-phosphorus carbide as efficient catalysts for N_(2) electroreduction

Compared to single atom catalysts(SACs),the introduction of dual atom catalysts(DACs)has a significantly positive effect on improving the efficiency in the electrocatalytic nitrogen reduction reaction(NRR)which provides an environmental alternative to the Haber-Bosch process.However,the research on the mechanism and strategy of designing bimetallic combinations for better performance is still in its early stages.Herein,based on"blocking and rebalance"mechanism,45 combinations of bimetallic pair dopedα-phosphorus carbide(TM_(A)TM_(B)@PC)are investigated as efficient NRR catalysts through density functional theory and machine learning method.After a multi-step screening,the combinations of TiV,TiFe,MnMo,and FeW exhibit highly efficient catalytic performance with significantly lower limiting potentials(-0.17,-0.18,-0.14,and-0.30 V,respectively).Excitingly,the limiting potential for CrMo and CrW combinations is 0 V,which are considered to be extremely suitable for the NRR process.The mechanism of"blocking and rebalance"is revealed by the exploration of charge transfer for phosphorus atoms in electron blocking areas.Moreover,the descriptorφis proposed with machine learning,which provides design strategies and accurate prediction for finding efficient DACs.This work not only offers promising catalysts TM_(A)TM_(B)@PC for NRR process but also provides design strategies by presenting the descriptorφ.

An Attribute-Based Access Control with Efficient and Secure Attribute Revocation for Cloud Data Sharing Service

Nowadays, there is the tendency to outsource data to cloud storage servers for data sharing purposes. In fact, this makes access control for the outsourced data a challenging issue. Ciphertext-policy attribute-based encryption（CP-ABE） is a promising cryptographic solution for this challenge. It gives the data owner（DO） direct control on access policy and enforces the access policy cryptographically. However,the practical application of CP-ABE in the data sharing service also has its own inherent challenge with regard to attribute revocation. To address this challenge, we proposed an attribute-revocable CP-ABE scheme by taking advantages of the over-encryption mechanism and CP-ABE scheme and by considering the semitrusted cloud service provider（CSP） that participates in decryption processes to issue decryption tokens for authorized users. We further presented the security and performance analysis in order to assess the effectiveness of the scheme. As compared with the existing attributerevocable CP-ABE schemes, our attribute-revocable scheme is reasonably efficient and more secure to enable attribute-based access control over the outsourced data in the cloud data sharing service.

Inherent-attribute-aware dual-graph autoencoder for rating prediction

Autoencoder-based rating prediction methods with external attributes have received wide attention due to their ability to accurately capture users'preferences.However,existing methods still have two significant limitations:i)External attributes are often unavailable in the real world due to privacy issues,leading to low quality of representations;and ii)existing methods lack considering complex associations in users'rating behaviors during the encoding process.To meet these challenges,this paper innovatively proposes an inherent-attribute-aware dual-graph autoencoder,named IADGAE,for rating prediction.To address the low quality of representations due to the unavailability of external attributes,we propose an inherent attribute perception module that mines inductive user active patterns and item popularity patterns from users'rating behaviors to strengthen user and item representations.To exploit the complex associations hidden in users’rating behaviors,we design an encoder on the item-item co-occurrence graph to capture the co-occurrence frequency features among items.Moreover,we propose a dual-graph feature encoder framework to simultaneously encode and fuse the high-order representations learned from the user-item rating graph and item-item co-occurrence graph.Extensive experiments on three real datasets demonstrate that IADGAE is effective and outperforms existing rating prediction methods,which achieves a significant improvement of 4.51%~41.63%in the RMSE metric.