陆西凹陷包日温都断裂构造带岩性油藏研究

为寻找岩性油藏，应用波阻抗反演地震储层预测技术，对包日温都断裂构造带砂体展布情况进行了预测，发现并落实了2个岩性圈闭，由此认为，研究区北部是大型水下扇砂体与半深湖相暗色泥岩交汇之处，有利于形成岩性油藏，这一结论将对今后加强储层预测工作和寻找有利砂体提供借鉴。     

实施油气回收的必要性和技术方案的探讨

简要介绍油气回收技术;从环保和增加经济效益的角度,以福建炼化公司的实际情况为例提出了油气回收的方案,并对实行该方案的必要性和经济效益做了分析;希望增强炼化企业对油气回收问题的重视,达到安全生产、改善环境和节约能源的目的。     

大港白水头地区低阻油气层测井评价

在对白水头地区低阻油层的地质特征、物性特征及成因环境进行了分析研究后 ,应用测井曲线特征集法 ,把白水头油田常规油气层、低阻油气层、油水同层、水层等看成是独立的地质事件 ,建立了它们与测井曲线特征之间的对应关系 ,利用这种关系对未知地质事件进行了预测划分。最后根据所建模型定量计算储层各种参数 ,收到了良好的地质效果 ,表明测井曲线特征集法对于识别多成因的低阻油层是一种有效方法     

Aging response of the young’s modulus and mechanical properties of Ti-29Nb-13Ta-4.6Zr for biomedical applications

Alloys for implant devices require improved strength but a reduced Young’s modulus, in order to become mechanically more compatible with adjacent bone tissues. In this study, a new metastable β-type titanium alloy, Ti-29Nb-13Ta-4.6Zr (wt pct), was subjected to aging treatment to produce different microstructures, and the resulting mechanical properties, including the Young’s modulus, were measured. The Young’s modulus of this alloy is found to be sensitive to microstructures generated by various heat treatments. For microstructures varying from (α + β) to (α + β + ω) and (β + ω), the Young’s modulus increases with an accompanying increase in tensile strength and hardness, but decreases in ductility. The (β + ω) microstructure has a low strength, high modulus, and poor ductility and cannot be used for biomedical applications. For an (α + β) microstructure, the volume fraction of the phases is shown to be the main factor that determines the mechanical properties.     

A distributed adverse drug reaction detection system using intelligent agents with a fuzzy recognition‐primed decision model

Discovering unknown adverse drug reactions (ADRs) in postmarketing surveillance as early as possible is highly desirable. Nevertheless, current postmarketing surveillance methods largely rely on spontaneous reports that suffer from serious underreporting, latency, and inconsistent reporting. Thus these methods are not ideal for rapidly identifying rare ADRs. The multiagent systems paradigm is an emerging and effective approach to tackling distributed problems, especially when data sources and knowledge are geographically located in different places and coordination and collaboration are necessary for decision making. In this article, we propose an active, multiagent framework for early detection of ADRs by utilizing electronic patient data distributed across many different sources and locations. In this framework, intelligent agents assist a team of experts based on the well‐known human decision‐making model called Recognition‐Primed Decision (RPD). We generalize the RPD model to a fuzzy RPD model and utilize fuzzy logic technology to not only represent, interpret, and compute imprecise and subjective cues that are commonly encountered in the ADR problem but also to retrieve prior experiences by evaluating the extent of matching between the current situation and a past experience. We describe our preliminary multiagent system design and illustrate its potential benefits for assisting expert teams in early detection of previously unknown ADRs. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 827–845, 2007.     

Fast algorithms for determining the linear complexity of sequences over GF(p/sup m/) with period 2/sup t/n

We prove a result which reduces the computation of the linear complexity of a sequence over GF(p^m) (p is an odd prime) with period 2n (n is a positive integer such that there exists an element bisinGF(p^m), bⁿ=-1) to the computation of the linear complexities of two sequences with period n. By combining with some known algorithms such as the Berlekamp-Massey algorithm and the Games-Chan algorithm we can determine the linear complexity of any sequence over GF(p^m) with period 2^tn (such that 2 ^t|p^m-1 and gcd(n,p^m-1)=1) more efficiently     

Frequency domain identification of Wiener models

This paper proposes a frequency domain algorithm for Wiener model identifications based on exploring the fundamental frequency and harmonics generated by the unknown nonlinearity. The convergence of the algorithm is established in the presence of white noise. No a priori knowledge of the structure of the nonlinearity is required and the linear part can be nonparametric.