注汞压力对致密砂岩储层物性下限分析的影响

最小流动孔喉半径法是目前常用的储层物性下限确定方法,它以岩芯压汞资料为基础,对岩石微观孔隙结构 与渗流能力的关系进行定量分析,进而确定储层物性下限。研究表明,过高的注汞压力可能影响岩石孔隙结构状态, 这引发了注汞压力的高低是否会对物性下限分析产生影响的思考,应该如何选取恰当的压汞数据来获取符合实际地 质情况的物性下限值。目前,这些问题还没有得到明确和有针对性的研究。以川中P 地区致密砂岩气藏为例展开了 较为深入的研讨。分析结果表明,当注汞压力从30 MPa 增加到200 MPa 时,最小流动孔喉半径法确定的有效储层物 性下限数值呈下降趋势。当注汞压力接近目的层位地层压力时,分析结果与生产测试等结论吻合较好。     

莺-琼盆地轴向水道沉积演化及勘探前景

莺琼盆地在莺歌海期和黄流期发育大规模的轴向水道沉积体系,但由于对这种轴向水道的成因机制及水道砂体的分布规律缺乏系统认识,勘探一直未能取得商业性突破。针对制约轴向水道沉积体系勘探的物源和储层展布问题,以最具代表意义的轴向水道——琼东南盆地中央峡谷为例,应用地质与地震相结合分析技术、多属性融合技术、适用于峡谷水道体系的地震沉积学等,开展了具有针对性的物源体系、沉积演化、储层展布等方面的研究,阐明了中央峡谷水道的物源、沉积演化及优质储层展布规律,建立了具有储层预测功能的峡谷沉积模式及水道砂叠置方式,指出乐东10-1峡谷水道等4个潜在的大型岩性圈闭群是轴向水道领域的下一步勘探有利区域,提升了轴向水道勘探潜力。     

多层地铁车站结构性能试验研究

为掌握地震作用下多层地铁车站结构从材料、局部构件到整体结构的非线性破坏状态,以上海市某多层地铁车站工程为背景,开展了1:10大型静力推覆(Pushover)试验。结合地下结构地震响应特征,采用倒三角分布的侧向位移推覆加载模式,并由多个作动器实现多点协调加载。分析研究了多层地铁车站结构的裂缝开展过程、结构及构件破坏形态、塑性铰开展顺序、Pushover曲线,并总结了结构整体和中柱的破坏模式,给出车站结构重要性能点数值,综合评价了多层地铁车站结构的抗震性能及特点,可为基于性能的抗震设计提供依据。     

确立"以人为本"理念推动高校改革发展

以人为本是科学发展观的核心和本质。“以人为本”的教育理念是高校改革与发展的必然选择。因此高校的各项工作要突出学生的主体地位,全面落实以人为本的新理念,以育人为宗旨,塑造大学生健全人格;构建以学生为本的教学模式创造良好的育人环境,为学生的全面发展提供真诚优质的服务。     

强平稳NA序列的随机指标中心极限定理

讨论了强平稳NA序列的随机指标中心极限定理,给出其成立的一个充分条件.     

中心地体系空间结构的标度定律与分形模型--对Christarller中心地模型的数学抽象与理论推广

从中心地理论出发,导出关于城市体系空间结构的一组标度定律;进而从标度定律引出幂律方程,并将其推广到分形几何领域;然后借助Christaller提供的德国南部中心地观察数据检验文章的推导结果,从而将确定型中心地模型发展为随机型分形模型,与Allen-Prigogine的中心地晶态结构非晶化模拟结果形成理论对应.     

掺混孔对中心分级燃烧室出口特性分析的数值模拟

燃烧室是航空发动机的核心部分,由于其出口工作环境复杂,研究分析其出口特性参数就显得尤其重要。本文针对的是中心分级燃烧室掺混孔几何特性方面对出口的影响,首先建立三级旋流器燃烧室的简化模型,再设计圆形,雨滴型,斜缝型三种掺混孔几何形状,利用FLUENT分析其各截面的的排放三个方面进行对比,经过模拟分析得雨滴形掺混孔对出口温度分布影响较好,圆形对燃烧室排放有着更好的调节作用。可见掺混孔对燃烧室出口特性有重要影响。     

Sleeping sickness and the brain

Recent progress in understanding the neuro-pathological mechanisms of sleeping sickness reveals a complex relationship between the trypanosome parasite that causes this disease and the host nervous system. The pathology of late-stage sleeping sickness, in which the central nervous system is involved, is complicated and is associated with disturbances in the circadian rhythm of sleep. The blood-brain barrier, which separates circulating blood from the central nervous system, regulates the flow of materials to and from the brain. During the course of disease, the integrity of the blood-brain barrier is compromised. Dysfunction of the nervous system may be exacerbated by factors of trypanosomal origin or by host responses to parasites. Microscopic examination of cerebrospinal fluid remains the best way to confirm late-stage sleeping sickness, but this necessitates a risky lumbar puncture. Most drugs, including many trypanocides, do not cross the blood-brain barrier efficiently. Improved diagnostic and therapeutic approaches are thus urgently required. The latter might benefit from approaches which manipulate the blood-brain barrier to enhance permeability or to limit drug efflux. This review summarizes our current understanding of the neurological aspects of sleeping sickness, and envisages new research into blood-brain barrier models that are necessary to understand the interactions between trypanosomes and drugs active against them within the host nervous system. Received 10 October 2001; received after revision 29 November 2001; accepted 5 December 2001     

影响住户集中供暖经济性的因素分析

在设有集中供暖的地方,住户的采暖费用实行"按户计量”、多用(热)多付款的方式将是必然的趋势,"采暖消费”成为人们用于居住投资消费的一个重要项目,其经济性受到多方关注.文章从普通住户的角度出发,分析各有关因素对住户集中供暖经济性的影响,以总的居住投资费用为目标,为广大的集中供暖住户提供了经济的参考方案.     

FORECASTING AUTOMOBILE WARRANTY PERFORMANCE IN PRESENCE OF ‘MATURING DATA＇ PHENOMENA USING MULTILAYER PERCEPTRON NEURAL NETWORK

Automobile companies that spend billions of dollars annually towards warranty cost, give high priority to warranty reduction programs. Forecasting of automobile warranty performance plays an important role towards these efforts. The forecasting process involves prediction of not only the specific months-in-service (MIS) warranty performance at certain future time, but also at future MIS values. However, ‘maturing data‘ (also called warranty growth) phenomena that causes warranty performance at specific MIS values to change with time, makes such a forecasting task challenging. Although warranty forecasting methods such as log-log plots and dynamic linear models appear in literature, there is a need for applications addressing the well recognized issue of ‘maturing data‘. In this paper we use an artificial neural network for the forecasting of warranty performance in presence of ‘maturing data‘ phenomena. The network parameters are optimized by minimizing the training and testing errors using response surface methodology. This application shows the effectiveness of neural networks in the forecasting of automobile warranty performance in the presence of the ‘maturing data‘ phenomena.