微地形对输电线路影响的探讨

利用国内外输电线路通过微地形地段风速研究的方法,通过微地形地段调查及确定,进行微地形地段气候成因分析,研究微地型对风速的影响,提出在福建沿海输电线路工程中应用的关键。     

气相色谱法测定卷烟烟气中烟碱的影响因素

为提高气相色谱法测定卷烟烟气中烟碱含量的准确性,依据行业标准YC/T156-2001对各种影响因素进行了考察,发现标准品配置方法、纯度是主要影响因素。根据试验结果,确定了用直接称重法制作标准工作曲线,采用校正标样的气相色谱法测定卷烟烟气中的烟碱。结果表明,改进后方法的准确度高、检测结果稳定,监测卷烟的测定结果与标定值基本一致。     

电子图书的发展及其对图书馆工作的影响

文章分析了电子图书的优势和存在的问题，以及给图书馆工作带来的影响，并对图书馆积极面对电子图书的挑战提出了建议。     

变压器第三线圈漏抗对滤波效果的影响及其解决方法

本文通过阻抗频率特性仿真,分析了变压器漏抗对滤波效果的影响,并从理论上提出了解决上述问题的方法.     

短路电流分流系数的计算与测试方法

本文以220kV变电站及输电线路为研究对象,对接地短路电流的分流系数进行了分析,提出了计算与测试方法,并通过对实际工程的验证,得出了适用的结论。     

Learning a decision maker's utility function from (possibly) inconsistent behavior

When modeling a decision problem using the influence diagram framework, the quantitative part rests on two principal components: probabilities for representing the decision maker's uncertainty about the domain and utilities for representing preferences. Over the last decade, several methods have been developed for learning the probabilities from a database. However, methods for learning the utilities have only received limited attention in the computer science community.

A promising approach for learning a decision maker's utility function is to take outset in the decision maker's observed behavioral patterns, and then find a utility function which (together with a domain model) can explain this behavior. That is, it is assumed that decision maker's preferences are reflected in the behavior. Standard learning algorithms also assume that the decision maker is behavioral consistent, i.e., given a model of the decision problem, there exists a utility function which can account for all the observed behavior. Unfortunately, this assumption is rarely valid in real-world decision problems, and in these situations existing learning methods may only identify a trivial utility function. In this paper we relax this consistency assumption, and propose two algorithms for learning a decision maker's utility function from possibly inconsistent behavior; inconsistent behavior is interpreted as random deviations from an underlying (true) utility function. The main difference between the two algorithms is that the first facilitates a form of batch learning whereas the second focuses on adaptation and is particularly well-suited for scenarios where the DM's preferences change over time. Empirical results demonstrate the tractability of the algorithms, and they also show that the algorithms converge toward the true utility function for even very small sets of observations.     

Design of a Medical Non‐Linear Drilling Device: The Influence of Twist and Wear on the Fatigue Behaviour of NiTi Wires Subjected to Bending Rotation

This paper considers fundamental and experimental aspects associated with the engineering design of a medical, non‐linear drilling device which exploits shape memory pseudoelasticity of NiTi wires. For this application it is important that the NiTi wires have a good fatigue resistance. This is why the present authors have previously determined the influence of various parameters on cyclic life, crack growth and stress state of pseudoelastic wires subjected to bending rotation fatigue. The actual drilling device has to withstand twist in addition to bending rotation because the free rotation is constrained by friction between the drill head and the bone material. In addition, friction between the wire and a NiTi guiding tube results in wear and this may well promote fatigue crack nucleation. In this paper, we explain the function of the medical drill. We then report results on the effect of the additional parameters (1) twist and (2) wear on the fatigue life of thin pseudoelastic NiTi wires. We finally discuss the implications of our experimental results for the design process of the medical drilling device.     

影响中国铅精矿进出口的风险因素分析

对我国过去4a铅精矿进出口状况进行了回顾,分析影响中国铅精矿进出口的风险因素,并预测我国在未来一段时期内铅精矿净进口量将呈现持续增加的态势。     

Study of factors that influence geomembrane air expansion deformation under ring-restrained conditions

In studies on the geomembrane air expansion in plain reservoirs, the forced deformation of a geomembrane is generally simplified as geomembrane air expansion deformation under ring-restrained conditions. In this study, a test apparatus was developed to measure geomembrane air expansion deformation, and a number of factors that can affect geomembrane air expansion deformation were investigated, including the test apparatus diameter, loading rate, and geomembrane defects. The results of this study show that under ring-restrained conditions, as the test apparatus diameter increases, the burst pressure decreases, and the burst crown height increases. Moreover, the burst pressure and the burst crown height gradually increase as the loading rate increases. Geomembrane defects, such as holes, folds, and scratches, decrease both the burst pressure and the burst crown height.     

Efficiently using contextual influence to recommend new items to ephemeral groups

Group recommender systems suggest items to groups of users that want to utilize those items together. These systems can support several activities that can be performed together with other people and are typically social, like watching TV or going to the restaurant. In this paper we study ephemeral groups, i.e., groups constituted by users who are together for the first time, and for which therefore there is no history of past group activities.Recent works have studied ephemeral group recommendations proposing techniques that learn complex models of users and items. These techniques, however, are not appropriate to recommend items that are new in the system, while we propose a method able to deal with new items too. Specifically, our technique determines the preference of a group for a given item by combining the individual preferences of the group members on the basis of their contextual influence, the contextual influence representing the ability of an individual, in a given situation, to guide the group’s decision. Moreover, while many works on recommendations do not consider the problem of efficiently producing recommendation lists at runtime, in this paper we speed up the recommendation process by applying techniques conceived for the top-$K$ query processing problem. Finally, we present extensive experiments, evaluating: (i) the accuracy of the recommendations, using a real TV dataset containing a log of viewings performed by real groups, and (ii) the efficiency of the online recommendation task, exploiting also a bigger partially synthetic dataset.