A brief review on α-zirconium phosphate intercalation compounds and nano-composites

The layer structured zirconium phosphate(Zr P) can be intercalated with atoms, molecules, small organic groups and even polymers. The structures and properties of the Zr P intercalation compounds can be deliberately tuned, leading to promising potential applications in many fields. This article provides a brief review on the experimental results of the Zr P intercalation compounds, with the focus on the polymer/a-zirconium phosphate (α-Zr P) nano-composites. The computer simulations of the Zr P intercalation compounds at the atomic level play a significant role in designing and understanding the properties of Zr P, and in the promotion of the applications of compounds.     

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。     

一种移动新型农村合作医疗信息系统的设计*

针对目前新型农村合作医疗信息系统的不完善,提出了一种移动新型农村合作医疗信息系统的设计方案。在简单阐述了系统的整体设计方案之后,着重讨论了基于J2ME技术实现移动终端的界面交互、网络通信和XML解析等问题。     

凝胶法制备Cu-BTC块体及其吸附脱除二甲基二硫醚性能

金属有机骨架材料Cu-BTC具有高孔隙率、易调控的骨架结构以及含过渡金属等特点,是吸附脱除硫化物的优异材料。采用凝胶法在常温条件下制备块状样品Cu-BTC-g,并通过X射线衍射(XRD)、扫描电子显微镜(SEM)、红外光谱(FT-IR)、拉曼光谱(Raman)、X射线光电子能谱(XPS)、N₂吸附-脱附等手段对其进行分析表征,同时测定Cu-BTC-g样品对模型硫化物二甲基二硫醚(DMDS)的静态吸附容量、吸附热力学和动力学特性。结果表明：凝胶法制备的Cu-BTC-g样品晶体平均尺寸约为0.2μm、比表面积为901 m²/g、孔体积为0.40 cm³/g,晶体尺寸、比表面积和孔体积均小于溶剂热法合成的样品Cu-BTC-s;在吸附温度298 K下,Cu-BTC-g样品的DMDS吸附容量为110.8 mg/g,比Cu-BTC-s样品提高12%。     

Electrochemical investigation of La0.4Sr0.6TiO3 synthesized in air for SOFC application

Journal of Applied Electrochemistry - La-doped titanate materials have been widely investigated as alternative Ni-free anodes for solid oxide fuel cells (SOFCs). In this study, La0.4Sr0.6TiO3 (LST)...     

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.     

基于动态有理函数的功放模型及预失真应用

针对现代无线通信系统中射频功率放大器的非线性与记忆效应,提出一种新的低复杂度的动态有理函数模型,该模型简化了有理函数模型,通过两个多项式的比进行建模,但分子是包络记忆多项式的形式,分母由无记忆多项式构成.通过模型仿真和预失真应用系统验证,结果表明:与记忆多项式模型相比,动态有理函数模型所需的系数要少30.6％,模型精度却与其相近,邻信道功率比(ACPR)改善约20 dB,而与有理函数模型相比,所需系数要少21.9％,模型精度改善2.4 dB,ACPR改善约15 dB.由此证明了该模型在复杂度和精确度上的优越性,对功放预失真的研究具有重要的参考价值.     

软件可靠性评测的多因素决策模型

提出了一种利用软件生命周期过程中影响软件可靠性的软件质量特性进行软件可靠性评测的方法.从软件失效的机理出发,分析并提取了刻画软件可靠性各个方面的因素,同时借助模糊分析及专家系统的理论对软件可靠性因素进行了定量的划分和描述.针对软件可靠性评测的多因素决策问题,提出了一种基于随机森林的软件可靠性评测模型.通过蒙特卡罗模拟仿真建立了各个可靠性因素的概率模型以获得评测样本数据集,并在此数据集上对所提出的评测模型进行了评测和分析.实验结果表明,所提出的方法能够对软件可靠性进行准确的评测,且不依赖于特定的可靠性因素的先验概率,显著提高了软件可靠性的评测性能.同时验证了该方法能够克服小样本集上易出现的过拟合及表现力差的问题,具有较好的稳定性和鲁棒性.     

Re-ranking for microblog retrieval via multiple graph model

As a new information sharing platform, microblog has got explosive growth in recent years and has become an important source for public opinion mining. A variety of information like the reviews of brands/products or the trends of events can be socially sensed from such kind of data. However, it is still a challenging task to search relevant microblogs as the user generated content tends to be mixed with noise. Besides short text, image is getting popular in microblogs due to its power in visual information conveying. In this paper, we leverage textual and visual cues integratedly and propose a general re-ranking approach for microblog retrieval via multi-graph semi-supervised learning. We argue that the different types of information in microblogs correspond to different relationships among microblogs and each type of the relationship can be represented as a similarity graph. We then integrate different graphs into a unified framework and solve them simultaneously for microblog re-ranking. Extensive experiments on a recently published Brand-Social-Net dataset showed the effectiveness of the proposed method and marginal improvements have been achieved in accuracy as compared to the single graph model based method.