Fuzzy商代数与同态基本定理

FaningDang1996年引进了Fuzzy商代数的概念，但他给出的Fuzzy商代数不是Fuzzy集，为此重新定义了Fuzzy商代数，并给出了Fuzzy代数的同态基本定理。     

The new sideway of CNTF signal transduction pathway

The action of ciliary neurotrophic factor (CNTF) on intercellular free Ca²⁺ concentrations [Ca²⁺]_i induced by glutamate (Glu) in primary cultured hippocampal neurons were detected with Fura2/AM, a Ca²⁺-sensitive fluorophore, and the morphological influence of G-protein on it was objected. Glu could induce rapid increase of [Ca²⁺]_i in hippocampal neurons. CNTF had no significant action on [Ca²⁺]_i in resting hippocampal neurons. However, after incubation of CNTF for 5 min, the increase of [Ca²⁺]_i in hippocampal neurons rapidly induced by Glu was inhibited. Pretussis toxin (PTX)-sensitive G protein could block the action. These results indicate that a new non-genomic rapid sideway might exist in the upper stream of CNTF signal transduction pathway, which was related to Ca²⁺ signal transduction. Keywords:     

喷锚初期支护——围岩稳定性强度折减有限元分析(英文)

岩石隧道施工阶段具喷锚初期支护的围岩稳定性评价对隧道施工安全是非常重要的。根据一施工中的岩石隧道Ⅳ级围岩地段地质条件为基础,选用ABAQUS程序双曲线型D-P材料模型为岩土体破坏准则,建立了考虑喷射混凝土与岩体之间,锚杆与岩体之间相互作用的3-D有限元计算模型,以此模型利用强度折减技术研究了具锚喷初期支护的围岩稳定性安全系数。分析表明采用所提出的极限状态确定具喷锚支护的围岩稳定性安全系数是合理的。     

SBS改性沥青车辙因子的改进

分析了车辙因子G＊（sinδ）^-1用于评价沥青,特别是改性沥青高温性能的局限性,结合重复蠕变与恢复试验和Bouldin的模型假设,建立了改进型车辙因子G＊（sinδ）^-9,并通过一种基质沥青和4种丁二烯—苯乙烯嵌段共聚物（SBS）制备4种SBS改性沥青的动态剪切流变（DSR）试验结果,分析比较了G＊（sinδ）^-9和G＊（sinδ）^-1评价SBS改性沥青高温性能的合理性,得出结论：G＊（sinδ）^-9比G＊（sinδ）^-1作为改性沥青高温评价指标更加合理.     

土壤动物与环境质量关系探讨

土壤动物作为生态系统循环中的重要分解者，在生态系统中起着重要的作用，它们同环境因子间存在相对稳定、密不可分的关系。笔者经过近１０年的学习与研究实践，对东北羊草草原、东部山地及有关农田污灌区等生系统进行了研究，研究结果表明，土壤动物是相对稳定的、综合的环境指示因子，特别是在人为活动圈大的区域，土壤动物对环境质量反映的准确程度要强于植物。土壤动物与环境的水分、ＰＨ值、土壤质地、结构、土壤有机质含量、植     

氯化钠对脂肪醇聚氧乙烯醚硫酸盐(AES)粘度的影响

在实验的基础上,研究了氯化钠对阴离子表面活性剂AES形成胶团性质的影响。总结出普遍规律,为AES的应用提供了实验数据和理论基础。     

灼口综合症的神经心理学机制研究

灼口综合症属于口腔粘膜的慢性疼痛疾病,其病因尚不清楚.从神经心理学这一新的视角对此病的发病机制加以论述.     

整合素CD29/CD49/CD90在天然性逆转黄鳝雌雄生殖腺中的表达状况

整合素是细胞黏附分子家族中的一员,参与调节细胞的生长、增殖与分化.黄鳝是一种雌性先熟的雌雄同体鱼类,其性逆转发育机制亟待研究.应用免疫组化方法探讨CD29,CD49,CD90 3种整合素分子在黄鳝雌、雄性发育中的表达状况.研究结果表明,CD29,CD49,CD90在雌性生殖腺各级卵母细胞中均不表达,位于生殖褶边缘的性原细胞中可见表达;在雄性生殖腺的初级精原细胞中表达,但在成团分布的次级精原细胞、各级精母细胞中均未表达.     

Methods for backcasting,nowcasting and forecasting using factor‐MIDAS: With an application to Korean GDP

We utilize mixed‐frequency factor‐MIDAS models for the purpose of carrying out backcasting, nowcasting, and forecasting experiments using real‐time data. We also introduce a new real‐time Korean GDP dataset, which is the focus of our experiments. The methodology that we utilize involves first estimating common latent factors (i.e., diffusion indices) from 190 monthly macroeconomic and financial series using various estimation strategies. These factors are then included, along with standard variables measured at multiple different frequencies, in various factor‐MIDAS prediction models. Our key empirical findings as follows. (i) When using real‐time data, factor‐MIDAS prediction models outperform various linear benchmark models. Interestingly, the “MSFE‐best” MIDAS models contain no autoregressive (AR) lag terms when backcasting and nowcasting. AR terms only begin to play a role in “true” forecasting contexts. (ii) Models that utilize only one or two factors are “MSFE‐best” at all forecasting horizons, but not at any backcasting and nowcasting horizons. In these latter contexts, much more heavily parametrized models with many factors are preferred. (iii) Real‐time data are crucial for forecasting Korean gross domestic product, and the use of “first available” versus “most recent” data “strongly” affects model selection and performance. (iv) Recursively estimated models are almost always “MSFE‐best,” and models estimated using autoregressive interpolation dominate those estimated using other interpolation methods. (v) Factors estimated using recursive principal component estimation methods have more predictive content than those estimated using a variety of other (more sophisticated) approaches. This result is particularly prevalent for our “MSFE‐best” factor‐MIDAS models, across virtually all forecast horizons, estimation schemes, and data vintages that are analyzed.     

风险投资与高技术产业R＆D全要素生产率增长——基于中国数据的实证

利用1995—2011年风险投资与高技术产业R＆D的相关数据,采用DEA-Malmquist指数法、典型相关分析法和多元回归分析法等计量方法,对风险投资与高技术产业R＆D全要素生产率关系进行实证研究。结果显示：风险投资与技术进步指数、技术效率改善指数及全要素生产率指数具有很强相关性,风险投资对R＆D全要素生产率有显著的正向影响;整体上,风险投资对全要素生产率贡献程度在0．1个单位,年度风险投资额对全要素生产率的影响大于风险投资总规模;R＆D内部经费和新产品开发经费对R＆D全要素生产率具有显著的促进作用,是技术进步与创新直接推动因素。