Some quadrature formulas for functions partially holomorphic with respect to each variable in a Jordan polydomain in C 2 and a complex double integral formula

In this paper, quadrature formulas with remaining term in integral form for functions partially holomorphic with respect to each variable in a Jordan polydomain in C ² and a complex double integral formula are given. For this, we used the Cauchy Integral formula for Jordan polydomain in C ².     

活性炭载体及其滤层对硫酸盐还原相反应器启动及SRB分布的影响

研究添加活性炭载体、活性炭滤层对硫酸盐还原相反应器的启动及硫酸盐还原茵（SRB）在反应器中分布的影响．采用两组平行的经改造的UASB反应器,一组添加活性炭载体并在反应器上部添加活性炭滤层（1^#）,一组不添加任何载体（2^#）,对比分析硫酸盐还原相反应器启动过程中各分析相的变化、SRB生物相差异及SRB的分布情况．结果表明,两组反应器均能成功启动,但1^#启动时间比2^#时间短．1^#形成的颗粒污泥粒径约为1．0-1．2mm,约为2^#的2倍．2^#中仍有部分结构松散的絮状污泥．1^#SRB主要分布在反应器的底部的活性炭载体上和反应器上部的活性炭滤层的下部,而2^#SRB较均匀分布在反应器各部分,因此2^#出水色度差．     

沿虚轴无穷区问主值积分与逆傅里叶积分变换

在工程技术和科学研究的许多领域,傅里叶积分变换极为重要,但逆傅里叶积分变换手工计算比较困难,限制了傅里叶积分变换的应用范围。研究发现,逆傅里叶积分变换可以变换成沿复平面虚轴上的无穷区间主值积分,由此,导出一个逆傅里叶积分变换的计算公式,可用来快速完成逆傅里叶积分变换计算。     

第一类Dirichlet积分的公式解

利用三角降次公式及留数解决了第一类Dirichlet积分问题,即给出了第一类Dirichlet积分的公式解。利用所得结果,定义了Dirichlet数Dn,证明了其一般性质。     

均匀设计法在粉末氯丁橡胶制备中的应用

以高分子树脂为包覆剂，采用凝聚包覆法将CRl21型氯丁胶乳制备成粉末氯丁橡胶。用均匀设计法研究了制备工艺路线中的主要4个因子(氯化钠、包覆剂M81、包覆剂M82和脂肪酸盐)对产物粒径分布以及物理机械性能的影响，并用回归分析法拟合了产物的粒径分布和物理机械性能与主要变量之间的试验数据，在试验范围内预测产物的性能并作了验证。结果表明，4个影响因子对硫化胶拉伸强度和扯断伸长率的影响不显著，但对产物中1-3mm和小于1mm粒子所占的质量分数、500％定伸应力、邵尔A型硬度和永久变形的影响很显著。     

级联网络分解法在材料电磁参数测量中的应用

介绍了一种由三级级联网络构成的取样器，论述了分式线性变换级联网络分解法，并利用奇偶模法计算被测材料的复电磁参数，该方法适用于糊状及粉末状材料电磁参数的测量，给出了多种材料测量实例和数据，并根据不同测量对象的特征，讨论了一些值得重视的误差因素和修正方法。     

韶钢常用铁矿粉烧结高温特性的试验研究

采用微型烧结试验设备对韶钢10种铁矿粉的烧结高温特性进行研究，为烧结厂优化配矿和生产优质烧结矿提供了依据．     

动态系统预测的一种统计方法

给出了动态系统预测的一种统计方法及其递推估计公式，该方法充分利用了新信息，提高了预测准确度，减少了计算量，预测效果优于普通最小二乘估计.     

Learning from examples with unspecified attribute values

A challenging problem within machine learning is how to make good inferences from data sets in which pieces of information are missing. While it is valuable to have algorithms that perform well for specific domains, to gain a fundamental understanding of the problem, one needs a “theory” about how to learn with incomplete data. The important contribution of such a theory is not so much the specific algorithmic results, but rather that it provides good ways of thinking about the problem formally. In this paper we introduce the unspecified attribute value (UAV) learning model as a first step towards a theoretical framework for studying the problem of learning from incomplete data in the exact learning framework.In the UAV learning model, an example x is classified positive (resp., negative) if all possible assignments for the unspecified attributes result in a positive (resp., negative) classification. Otherwise the classification given to x is “?” (for unknown). Given an example x in which some attributes are unspecified, the oracle UAV-MQ responds with the classification of x. Given a hypothesis h, the oracle UAV-EQ returns an example x (that could have unspecified attributes) for which h(x) is incorrect.We show that any class of functions learnable in Angluin’s exact model using the MQ and EQ oracles is also learnable in the UAV model using the MQ and UAV-EQ oracles as long as the counterexamples provided by the UAV-EQ oracle have a logarithmic number of unspecified attributes. We also show that any class learnable in the exact model using the MQ and EQ oracles is also learnable in the UAV model using the UAV-MQ and UAV-EQ oracles as well as an oracle to evaluate a given boolean formula on an example with unspecified attributes. (For some hypothesis classes such as decision trees and unate formulas the evaluation can be done in polynomial time without an oracle.) We also study the learnability of a universal class of decision trees under the UAV model and of DNF formulas under a representation-dependent variation of the UAV model.     

Recognition of Simple Enlarged Horn Formulas and Simple Extended Horn Formulas

The enlarged Horn formulas generalize the extended Horn formulas introduced by Chandru and Hooker (1991). Their satisfying truth assignments can be generated with polynomial delay. Unfortunately no polynomial algorithm is known for recognizing enlarged Horn formulas or extended Horn formulas. In this paper we define the class of simple enlarged Horn formulas, a subclass of the enlarged Horn formulas, that contains the simple extended Horn formulas introduced by Swaminathan and Wagner (1995). We present recognition algorithms for the simple enlarged Horn formulas and the simple extended Horn formulas whose complexity is bounded by the complexity of the arborescence-realization problem.