3D Printing of Nacre-Inspired Structures with Exceptional Mechanical and Flame-Retardant Properties

Flame-retardant and thermal management structures have attracted great attention duc to the requirement of high-temperature exposure in industrial,aerospace,and thermal power fields,but the development of protoctive fire-retardant structures with complex shapes to fit arbitrary surfaces is still challenging.Herein,we reported a rotation-blade casting-assisted 3D printing process to fabricate nacre-inspired structures with exceptional mechanical and flame-retardant properties,and the relatedi fundamental mechanisms are studied.3-(Trimethoxysilyl)propyl methacrylate(TMSPMA)modified boron nitride nanoplatelets(BNs)were aligned by rotation-blade casting during the 3D printing process to build the"hrick and mortar"architecture.The 3D printed structures are more lightweight,while having higher fracture toughness than the natural nacre,which is attributed to the crack deflection,aligned BN(a-BNs)bridging,and pull-outs reinforced structures by the covalent bonding between TMSPMA grafted a-BNs and polymer matrix.Thermal conductivity is enhanced by 25.5 times compared with pure polymer and 5.8 times of anisotropy due to the interconnection of a-BNs.3D printed heat-exchange structures with vertically aligned BNs in complex shapes were demonstrated for efficient thermal control of high-power light-emitting diocles.3D printed helmet and armor with a-BNs show exceptional mechanical and fire-retardant properties,demonstrating integrated mechanical and thermal protection.

Effcient poisoning attacks and defenses for unlabeled data in DDoS prediction of intelligent transportation systems

Nowadays,large numbers of smart sensors(e.g.,road-side cameras)which com-municate with nearby base stations could launch distributed denial of services(DDoS)attack storms in intelligent transportation systems.DDoS attacks disable the services provided by base stations.Thus in this paper,considering the uneven communication traffic ows and privacy preserving,we give a hidden Markov model-based prediction model by utilizing the multi-step characteristic of DDoS with a federated learning framework to predict whether DDoS attacks will happen on base stations in the future.However,in the federated learning,we need to consider the problem of poisoning attacks due to malicious participants.The poisoning attacks will lead to the intelligent transportation systems paralysis without security protection.Traditional poisoning attacks mainly apply to the classi cation model with labeled data.In this paper,we propose a reinforcement learning-based poisoningmethod speci cally for poisoning the prediction model with unlabeled data.Besides,previous related defense strategies rely on validation datasets with labeled data in the server.However,it is unrealistic since the local training datasets are not uploaded to the server due to privacy preserving,and our datasets are also unlabeled.Furthermore,we give a validation dataset-free defense strategy based on Dempster-Shafer(D-S)evidence theory avoiding anomaly aggregation to obtain a robust global model for precise DDoS prediction.In our experiments,we simulate 3000 points in combination with DARPA2000 dataset to carry out evaluations.The results indicate that our poisoning method can successfully poison the global prediction model with unlabeled data in a short time.Meanwhile,we compare our proposed defense algorithm with three popularly used defense algorithms.The results show that our defense method has a high accuracy rate of excluding poisoners and can obtain a high attack prediction probability.