Using Web resources to construct multilingual medical thesaurus for cross-language medical information retrieval

Due to the language barrier, non-English users are unable to retrieve the most updated medical information from the U.S. authoritative medical websites, such as PubMed and MedlinePlus. However, currently, there is no any cross-language medical information retrieval (CLMIR) system that can help Chinese-speaking consumers cross the language barrier in finding useful English medical information. A few CLMIR systems utilize MeSH (Medical Subject Headings) to help overcome the language barrier. Unfortunately, the traditional Chinese version of MeSH is currently unavailable.In this paper, we employ a semi-automatic term translation method to construct a Chinese–English MeSH by exploiting abundant multilingual Web resources, including Web anchor texts and search–result pages. Through this method, we have developed a Chinese–English Mesh Compilation System to assist knowledge engineers in compiling a Chinese–English medical thesaurus with more than 19,000 entries. Furthermore, this thesaurus has been used to develop a prototypical system for cross-language medical information retrieval, MMODE, which can help consumers retrieve top-quality English medical information using Chinese terms.     

矿业投资问题和对策——以铜矿开发为例

本文以铜矿开发为例 ,从 5个方面分析了矿山企业融资困难实际情况 ,并对改变矿业投资难的重要性和必要性做了详细论述 ,最后对解决矿业投资难问题提出了有效途径和办法     

阳泉市煤矿采空区地质因素对公路的破坏

介绍了阳泉市煤矿采空区对公路建设所造成的危害，对其上覆岩层变形破坏的地质因素及其破坏机理作了分析，指出应该采取各种措施减少对公路的破坏。     

深大露天矿综合开采工艺主要设计参数的研究

深大露天矿的间断向半连续开采工艺过渡时，合理深度及半连续开采工艺的移设步距是两个重要的开采参数，对矿山的经济效果影响巨大，本文首次提出采用组合台阶单位体的开采来分析确定这两个重要的参数，得到的结论在霍林河煤矿的应用中取得了满意的结果，对其它类似矿山具有应用价值。     

镍代钴矿用硬质合金的新进展

概述了镍代钴矿用硬质合金的国内外进展情况．     

专家系统技术在采煤决策及设备选型的应用

专家系统的研究越来越受到人们的重视，它在综合机械化采煤中的应用刚刚起步，文章详细地论述了ＺＪＸＴ系统的知识获取与知识库构造，推理机的特点及系统的功能。     

提高炮采工作面块煤率的爆破技术

通过小规模爆破试验，用线性回归方法对块煤进行筛分分析，指出影响炮采工作面块煤率的因素是炸药性能、装药结构、爆破参数、起爆顺序、时间间隔等。     

新桥硫铁矿二步骤回采工艺及充填方案的探讨

介绍了新桥硫铁矿二步骤回采工艺，并从技术经济角度对该矿所采用的充填料及充填系统进行了探讨。     

Data-mining based SQL injection attack detection using internal query trees

Detecting SQL injection attacks (SQLIAs) is becoming increasingly important in database-driven web sites. Until now, most of the studies on SQLIA detection have focused on the structured query language (SQL) structure at the application level. Unfortunately, this approach inevitably fails to detect those attacks that use already stored procedure and data within the database system. In this paper, we propose a framework to detect SQLIAs at database level by using SVM classification and various kernel functions. The key issue of SQLIA detection framework is how to represent the internal query tree collected from database log suitable for SVM classification algorithm in order to acquire good performance in detecting SQLIAs. To solve the issue, we first propose a novel method to convert the query tree into an n-dimensional feature vector by using a multi-dimensional sequence as an intermediate representation. The reason that it is difficult to directly convert the query tree into an n-dimensional feature vector is the complexity and variability of the query tree structure. Second, we propose a method to extract the syntactic features, as well as the semantic features when generating feature vector. Third, we propose a method to transform string feature values into numeric feature values, combining multiple statistical models. The combined model maps one string value to one numeric value by containing the multiple characteristic of each string value. In order to demonstrate the feasibility of our proposals in practical environments, we implement the SQLIA detection system based on PostgreSQL, a popular open source database system, and we perform experiments. The experimental results using the internal query trees of PostgreSQL validate that our proposal is effective in detecting SQLIAs, with at least 99.6% of the probability that the probability for malicious queries to be correctly predicted as SQLIA is greater than the probability for normal queries to be incorrectly predicted as SQLIA. Finally, we perform additional experiments to compare our proposal with syntax-focused feature extraction and single statistical model based on feature transformation. The experimental results show that our proposal significantly increases the probability of correctly detecting SQLIAs for various SQL statements, when compared to the previous methods.