消泡剂在发酵工业中的应用

泡沫问题是发酵工业中一个棘手的问题,几乎所用的液体深层发酵都会用到天然或者化学消泡剂。在过去的发酵过程中,泡沫控制一般是依靠经验,关于消泡剂的系统知识较少。本文综述了发酵工业中消泡剂的种类和作用机制,以及消泡剂的应用进展。     

A novel spherical-ordered macroporous CuO nanocatalyst for the electrochemical reduction of carbon dioxide

Journal of Applied Electrochemistry - The electrocatalytic reduction of CO2 is a promising research direction in resource utilization and sustainable energy development. However, there is still a...     

Fuzzy adaptive networks in machining process modeling: surface roughness prediction for turning operations

Due to the complexity of the machine tool structure and the cutting process, the dynamics of machining processes are still not completely understood. This is especially true due to the demand of high-speed machining to increase productivity. In order to model and control these complex processes, new approaches, which can represent complex phenomenon combined with learning ability, are needed. The combined neural–fuzzy approach appears to be ideally suited for this purpose. In this paper, the recently developed fuzzy adaptive network (FAN) is used to model surface roughness in turning operations. The FAN network has both the learning ability of neural network and linguistic representation of complex, not well-understood, vague phenomenon. Furthermore, it can continuously improve the initially obtained rough model based on the daily operating data. To illustrate this approach, a model representing the influences of machining parameters on surface roughness is established and then the model is verified by the use of the results of pilot experiments. Finally, a comparison with the results based on statistical regression is provided.     

虚拟化技术在数字化校园建设中的研究与应用

随着信息化技术的加速发展,数字化校园建设势必导致服务器数量的不断增加,给管理带来了巨大挑战.本文研究了服务器虚拟化部署方案,并将江西财经大学选课系统的WEB应用从实体服务器迁移到虚拟机上,通过对虚拟机和实体机的性能监测对比分析,发现虚拟机具有很强的替代性和推广型,为今后基于虚拟机技术构建数字化校园服务平台提供了可参考的实践依据.     

Extension of the gap statistics index to fuzzy clustering

The well-known gap statistic index proposed by Tibshirani et al. has successfully applied in many clustering evaluations. However, the gap statistic index cannot evaluate the clustering partitions from any fuzzy clustering algorithm. This is because fuzzy clustering cannot provide the within-cluster similarity measure that is used in the gas statistic index. Thus, the applicable range of the gap statistic index is very limited. In this paper, we present a new method that extends the gap statistic index to fuzzy clustering by using fuzzy membership notations. Our proposed method can extend the applicability of the gap statistic index, and outperform other existing fuzzy indices in several aspects. Experiments on eight sets of synthetic and real datasets are used to verify the applicability and efficiency of the proposed method.