On q-deformed infinite-dimensional n-algebra

The $q$-deformation of the infinite-dimensional $n$-algebra is investigated. Based on the structure of the $q$-deformed Virasoro-Witt algebra, we derive a nontrivial $q$-deformed Virasoro-Witt $n$-algebra which is nothing but a sh-$n$-Lie algebra. Furthermore in terms of the pseud-differential operators on the quantum plane, we construct the (co)sine $n$-algebra and the $q$-deformed $SDiff(T^2)$ $n$-algebra. We prove that they are the sh-$n$-Lie algebras for the case of even $n$. An explicit physical realization of the (co)sine $n$-algebra is given.Comment: 22 page

Research and simulation of fast, strong exothermic reaction in gas-solid fluidized bed about temperature distribution and hot spot problem

Gas-solid fluidized bed is widely used in petro-chemical and coal-chemical industry and other fields because of its superior heat transfer and mass transfer performances. In consideration of these performances, it is generally believed that there is a uniform temperature distribution and no hot spot in gas-solid fluidized bed compared with fixed bed. But in real industrial processes of fast, strong exothermic reactions, there are great axial and radial temperature differences and even hot spots in gas-solid fluidized bed. In this study, two-dimensional diffusion model based upon the momentum and energy conservation equations was successfully used to compute the temperature distribution of aniline reaction in fluidized bed. The result is in good agreement with real industrial measurement. In addition, this study discussed the influence of velocity and fluidized bed diameter on the temperature distribution. The result showed that in contrast to the fixed bed, increasing gas velocity during turbulent region in fluidized bed would help eliminate hot spot and reduce temperature difference. Finally, based on the comprehensive consideration of velocity and diameter, this study showed a stability region for scaling up of gas-solid fluidized bed with fast, strong exothermic reactions which helps to guide the practical operation. Please click Additional Files below to see the full abstract

Exploiting Contextual Information for Prosodic Event Detection Using Auto-Context

Prosody and prosodic boundaries carry significant information regarding linguistics and paralinguistics and are important aspects of speech. In the field of prosodic event detection, many local acoustic features have been investigated; however, contextual information has not yet been thoroughly exploited. The most difficult aspect of this lies in learning the long-distance contextual dependencies effectively and efficiently. To address this problem, we introduce the use of an algorithm called auto-context. In this algorithm, a classifier is first trained based on a set of local acoustic features, after which the generated probabilities are used along with the local features as contextual information to train new classifiers. By iteratively using updated probabilities as the contextual information, the algorithm can accurately model contextual dependencies and improve classification ability. The advantages of this method include its flexible structure and the ability of capturing contextual relationships. When using the auto-context algorithm based on support vector machine, we can improve the detection accuracy by about 3% and F-score by more than 7% on both two-way and four-way pitch accent detections in combination with the acoustic context. For boundary detection, the accuracy improvement is about 1% and the F-score improvement reaches 12%. The new algorithm outperforms conditional random fields, especially on boundary detection in terms of F-score. It also outperforms an n-gram language model on the task of pitch accent detection