Beijing University of Technology

About: Beijing University of Technology is a education organization based out in Beijing, Beijing, China. It is known for research contribution in the topics: Microstructure & Computer science. The organization has 31929 authors who have published 31987 publications receiving 352112 citations. The organization is also known as: Běijīng Gōngyè Dàxué & Beijing Polytechnic University.

C–C Coupling on Single-Atom-Based Heterogeneous Catalyst

Abstract: Compared to homogeneous catalysis, heterogeneous catalysis allows for ready separation of products from the catalyst and thus reuse of the catalyst. C–C coupling is typically performed on a molecular catalyst which is mixed with reactants in liquid phase during catalysis. This homogeneous mixing at a molecular level in the same phase makes separation of the molecular catalyst extremely challenging and costly. Here we demonstrated that a TiO2-based nanoparticle catalyst anchoring singly dispersed Pd atoms (Pd1/TiO2) is selective and highly active for more than 10 Sonogashira C–C coupling reactions (R≡CH + R′X → R≡R′; X = Br, I; R′ = aryl or vinyl). The coupling between iodobenzene and phenylacetylene on Pd1/TiO2 exhibits a turnover rate of 51.0 diphenylacetylene molecules per anchored Pd atom per minute at 60 °C, with a low apparent activation barrier of 28.9 kJ/mol and no cost of catalyst separation. DFT calculations suggest that the single Pd atom bonded to surface lattice oxygen atoms of TiO2 acts as a ...

FlowGuard: An Intelligent Edge Defense Mechanism Against IoT DDoS Attacks

Abstract: Internet-of-Things (IoT) devices are getting more and more popular in recent years and IoT networks play an important role in the industry as well as people’s activities. On the one hand, they bring convenience to every aspect of our daily life; on the other hand, they are vulnerable to various attacks that in turn cancels out their benefits to a certain degree. In this article, we target the defense techniques against IoT Distributed Denial-of-Service (DDoS) attacks and propose an edge-centric IoT defense scheme termed FlowGuard for the detection, identification, classification, and mitigation of IoT DDoS attacks. We present a new DDoS attack detection algorithm based on traffic variations and design two machine learning models for DDoS identification and classification. To demonstrate the effectiveness of the two machine learning models, we generate a large data set by DDoS simulators BoNeSi and SlowHTTPTest, and combine it with the CICDDoS2019 data set, to test the identification and classification accuracy as well as the model efficiency. Our results indicate that the identification accuracy of the proposed long short-term memory is as high as 98.9%, which significantly outperforms the other four well-known learning models mentioned in the most related work. The classification accuracy of the proposed convolutional neural network is up to 99.9%. Besides, our models satisfactorily meet the delay requirements of IoT when deployed in edge servers with computational powers higher than a personal computer.

Winding-Locked Carbon Nanotubes/Polymer Nanofibers Helical Yarn for Ultrastretchable Conductor and Strain Sensor.

Abstract: ：Wearable and stretchable electronics including various conductors and sensors are featured with their light weight, high flexibility and easy integration into functional devices or textile...