Xiamen University

About: Xiamen University is a education organization based out in Amoy, Fujian, China. It is known for research contribution in the topics: Catalysis & Population. The organization has 50472 authors who have published 54480 publications receiving 1058239 citations. The organization is also known as: Amoy University & Xiàmén Dàxué.

Enhanced Sensitivity and Stability of Room-Temperature NH3 Sensors Using Core–Shell CeO2 Nanoparticles@Cross-linked PANI with p–n Heterojunctions

Abstract: We report a room-temperature NH3 gas sensor with high response and great long-term stability, including CeO2 NPs conformally coated by cross-linked PANI hydrogel. Such core–shell nanocomposites were prepared by in situ polymerization with different weight ratios of CeO2 NPs and aniline. At room temperature, the nanohybrids showed enhanced response (6.5 to 50 ppm of NH3), which could be attributed to p–n junctions formed by the intimate contact between these two materials. Moreover, the stability was discussed in terms of phytic acid working as a gelator, which helped the PANI sheath accommodate itself and enhance the mechanical strength and chemical stability of the sensors by avoiding “swelling effect” in high relative humidity. The sensors maintained its sensing characteristic (response of ca. 6.5 to 50 ppm of NH3) in 15 days. Herein, the obtained results could help to accelerate the development of ammonia gas sensor.

Variational Neural Machine Translation

Abstract: Models of neural machine translation are often from a discriminative family of encoderdecoders that learn a conditional distribution of a target sentence given a source sentence. In this paper, we propose a variational model to learn this conditional distribution for neural machine translation: a variational encoderdecoder model that can be trained end-to-end. Different from the vanilla encoder-decoder model that generates target translations from hidden representations of source sentences alone, the variational model introduces a continuous latent variable to explicitly model underlying semantics of source sentences and to guide the generation of target translations. In order to perform efficient posterior inference and large-scale training, we build a neural posterior approximator conditioned on both the source and the target sides, and equip it with a reparameterization technique to estimate the variational lower bound. Experiments on both Chinese-English and English- German translation tasks show that the proposed variational neural machine translation achieves significant improvements over the vanilla neural machine translation baselines.

Valence tautomeric transitions with thermal hysteresis around room temperature and photoinduced effects observed in a cobalt-tetraoxolene complex.

Abstract: A dinuclear complex, [(CoTPA)2(DHBQ)](PF6)3, has been successfully synthesized that exhibits a valence tautomeric transition with a distinct hysteresis effect (13 K) around room temperature and photoinduced valence tautomerism under low temperature.

High-energy-surface engineered metal oxide micro- and nanocrystallites and their applications.

Abstract: Because many physical and chemical processes occur at surfaces, surface atomic structure is a critical factor affecting the properties of materials. Due to the presence of high-density atomic steps and edges and abundant unsaturated coordination sites, micro- and nanocrystallites with high-energy surfaces usually exhibit greater reactivity than those with low-energy surfaces. However, high-energy crystal surfaces are usually lost during crystal growth as the total surface energy is minimized. Therefore, the selective exposure of high-energy facets at the surface of micro- and nanocrystallites is an important and challenging research topic. Metal oxides play important roles in surface-associated applications, including catalysis, gas sensing, luminescence, and antibiosis. The synthesis of metal oxide micro- and nanocrystallites with specific surfaces, particularly those with high surface energies, is more challenging than the synthesis of metal crystals due to the presence of strong metal–oxygen bonds and ...

Security in Mobile Edge Caching with Reinforcement Learning

Abstract: Mobile edge computing usually uses caching to support multimedia contents in 5G mobile Internet to reduce the computing overhead and latency. Mobile edge caching (MEC) systems are vulnerable to various attacks such as denial of service attacks and rogue edge attacks. This article investigates the attack models in MEC systems, focusing on both the mobile offloading and the caching procedures. In this article, we propose security solutions that apply reinforcement learning (RL) techniques to provide secure offloading to the edge nodes against jamming attacks. We also present lightweight authentication and secure collaborative caching schemes to protect data privacy. We evaluate the performance of the RL-based security solution for mobile edge caching and discuss the challenges that need to be addressed in the future.