Strategies of selective electroreduction of aqueous nitrate to N_(2) in chloride-free system:A critical review

Electroreduction of nitrate has been gaining wide attention in recent years owing to it's beneficial for converting nitrate into benign N_(2) from the perspective of electrocatalytic denitrification or into value-added ammonia from the perspective of electrocatalytic NH_(3) synthesis.By reason of the undesired formation of ammonia is dominant during electroreduction of nitrate-containing wastewater,chloride has been widely used to improve N_(2) selectivity.Nevertheless,selective electroreduction of nitrate to N2 gas in chloride-containing system poses several drawbacks.In this review,we focus on the key strategies for efficiently enhancing N_(2) selectivity of electroreduction of nitrate in chloride-free system,including optimal selection of elements,combining an active metal catalyst with another metal,manipulating the crystalline morphology and facet orientation,constructing core–shell structure catalysts,etc.Before summarizing the strategies,four possible reaction pathways of electro-reduction of nitrate to N_(2) are discussed.Overall,this review attempts to provide practical strategies for enhancing N2 selectivity without the aid of electrochlorination and highlight directions for future research for designing appropriate electrocatalyst for final electrocatalytic denitrifi-cation.

An Improved Binary Search Anti-Collision Protocol for RFID Tag Identification

Radio frequency identification(RFID)has been widespread used in massive items tagged domains.However,tag collision increases both time and energy consumption of RFID network.Tag collision can seriously affect the success of tag identification.An efficient anti-collision protocol is very crucially in RFID system.In this paper,an improved binary search anti-collision protocol namely BRTP is proposed to cope with the tag collision concern,which introduces a Bi-response mechanism.In Bi-response mechanism,two groups of tags allowed to reply to the reader in the same slot.According to Bi-response mechanism,the BRTP strengthens the tag identification of RFID network by reducing the total number of queries and exchanged messages between the reader and tags.Both theoretical analysis and numerical results verify the effectiveness of the proposed BRTP in various performance metrics including the number of total slots,system efficiency,communication complexity and total identification time.The BRTP is suitable to be applied in passive RFID systems.

FMSA:a meta-learning framework-based fast model stealing attack technique against intelligent network intrusion detection systems

Intrusion detection systems are increasingly using machine learning.While machine learning has shown excellent performance in identifying malicious traffic,it may increase the risk of privacy leakage.This paper focuses on imple-menting a model stealing attack on intrusion detection systems.Existing model stealing attacks are hard to imple-ment in practical network environments,as they either need private data of the victim dataset or frequent access to the victim model.In this paper,we propose a novel solution called Fast Model Stealing Attack(FMSA)to address the problem in the field of model stealing attacks.We also highlight the risks of using ML-NIDS in network security.First,meta-learning frameworks are introduced into the model stealing algorithm to clone the victim model in a black-box state.Then,the number of accesses to the target model is used as an optimization term,resulting in minimal queries to achieve model stealing.Finally,adversarial training is used to simulate the data distribution of the target model and achieve the recovery of privacy data.Through experiments on multiple public datasets,compared to existing state-of-the-art algorithms,FMSA reduces the number of accesses to the target model and improves the accuracy of the clone model on the test dataset to 88.9%and the similarity with the target model to 90.1%.We can demonstrate the successful execution of model stealing attacks on the ML-NIDS system even with protective measures in place to limit the number of anomalous queries.