Algorithms of mining data records from website automatically

In order to improve the accuracy and integrality of mining data records from the web, the concepts of isomorphic page and directory page and three algorithms are proposed. An isomorphic web page is a set of web pages that have uniform structure, only differing in main information. A web page which contains many links that link to isomorphic web pages is called a directory page. Algorithm 1 can find directory web pages in a web using adjacent links similar analysis method. It first sorts the link, and then counts the links in each directory. If the count is greater than a given valve then finds the similar sub-page links in the directory and gives the results. A function for an isomorphic web page judgment is also proposed. Algorithm 2 can mine data records from an isomorphic page using a noise information filter. It is based on the fact that the noise information is the same in two isomorphic pages, only the main information is different. Algorithm 3 can mine data records from an entire website using the technology of spider. The experiment shows that the proposed algorithms can mine data records more intactly than the existing algorithms. Mining data records from isomorphic pages is an efficient method.

Detection of Turbulence Anomalies Using a Symbolic Classifier Algorithm in Airborne Quick Access Record(QAR)Data Analysis

As the risks associated with air turbulence are intensified by climate change and the growth of the aviation industry,it has become imperative to monitor and mitigate these threats to ensure civil aviation safety.The eddy dissipation rate(EDR)has been established as the standard metric for quantifying turbulence in civil aviation.This study aims to explore a universally applicable symbolic classification approach based on genetic programming to detect turbulence anomalies using quick access recorder(QAR)data.The detection of atmospheric turbulence is approached as an anomaly detection problem.Comparative evaluations demonstrate that this approach performs on par with direct EDR calculation methods in identifying turbulence events.Moreover,comparisons with alternative machine learning techniques indicate that the proposed technique is the optimal methodology currently available.In summary,the use of symbolic classification via genetic programming enables accurate turbulence detection from QAR data,comparable to that with established EDR approaches and surpassing that achieved with machine learning algorithms.This finding highlights the potential of integrating symbolic classifiers into turbulence monitoring systems to enhance civil aviation safety amidst rising environmental and operational hazards.

Design of universal data structure and interface based on dynamic self-incremental record

In the software of data management system, there are some different lengths of records needed storing in an array, and the number of records often increases in use of the software. A universal data structure is presented in the design, and it provide an unified interface for dynamic storage records in different length, so that the developers can call the unified interface directly for the data storage to simplify the design of data management system.

Lithographic patterning of ferromagnetic FePt nanoparticles from a single-source bimetallic precursor containing hemiphasmidic structure for magnetic data recording media

Patterning of L10 FePt nanoparticles(NPs) with high coercivity offers a promising route to develop bit-patterned media(BPM) for the next generation magnetic data recording system, but the synthesis of monodisperse FePt NPs and mass production of their nanopatterns has been a longstanding challenge. Here, highly efficient nanoimprint lithography was applied for large-scale universal patterning, which was achieved by imprinting the solution of a single-source bimetallic precursor. The rigid coplanar metallic cores and the surrounding flexible tails in the bimetallic complex permit the spontaneous molecular arrangements to form the highly ordered negative morphology replicated from the soft template.In-situ pyrolysis study was then investigated by one-pot pyrolysis of the precursor under an Ar/H2 atmosphere, and the resultant NPs were fully characterized to identify the phase,morphology and magnetic properties. Finally, highly-ordered patterns on certain substrates were preserved perfectly after pyrolysis and could be potentially utilized in magnetic data recording media.

Maintaining Consistency Based on Timely Updating Records List in Workflow Data

In order to settle the problem of workflow data consis-tency under the distributed environment, an invalidation strategy based-on timely updating record list is put forward. The strategy adopting the method of updating the records list and the recovery mechanism of updating message proves the classical invalidation strategy. When the request cycle of duplication is too long, the strategy uses the method of updating the records list to pause for sending updating message; when the long cycle duplication is requested again, it uses the recovery mechanism to resume the updating message. This strategy not only ensures the consistency of the workflow data, but also reduces the unnecessary network traffic. From theoretical comparison with those common strategies, the unnecessary network traffic of this strategy is fewer and more stable. The simulation results validate this conclusion.

Data Processing of Fault Recorder in Power System

This article studies the fault recorder in power system and introduces the Comtrade format. Andituses C++ programming to read recorded fault data and adopts Fourier analysis and symmetrical component method to filter and extract fundamental waves. Finally the effectiveness of the data processing method introduced in this paper is verified by CAAP software.

Application of fuzzy equivalence theory in data cleaning

This paper presented a rule merging and simplifying method and an improved analysis deviation algorithm. The fuzzy equivalence theory avoids the rigid way (either this or that) of traditional equivalence theory. During a data cleaning process task, some rules exist such as included/being included relations with each other. The equivalence degree of the being-included rule is smaller than that of the including rule, so a rule merging and simplifying method is introduced to reduce the total computing time. And this kind of relation will affect the deviation of fuzzy equivalence degree. An improved analysis deviation algorithm that omits the influence of the included rules' equivalence degree was also presented. Normally the duplicate records are logged in a file, and users have to check and verify them one by one. It's time-cost. The proposed algorithm can save users' labor during duplicate records checking. Finally, an experiment was presented which demonstrates the possibility of the rule.

Brittleness index predictions from Lower Barnett Shale well-log data applying an optimized data matching algorithm at various sampling densities

The capability of accurately predicting mineralogical brittleness index (BI) from basic suites of well logs is desirable as it provides a useful indicator of the fracability of tight formations.Measuring mineralogical components in rocks is expensive and time consuming.However,the basic well log curves are not well correlated with BI so correlation-based,machine-learning methods are not able to derive highly accurate BI predictions using such data.A correlation-free,optimized data-matching algorithm is configured to predict BI on a supervised basis from well log and core data available from two published wells in the Lower Barnett Shale Formation (Texas).This transparent open box (TOB) algorithm matches data records by calculating the sum of squared errors between their variables and selecting the best matches as those with the minimum squared errors.It then applies optimizers to adjust weights applied to individual variable errors to minimize the root mean square error (RMSE)between calculated and predicted (BI).The prediction accuracy achieved by TOB using just five well logs (Gr,ρb,Ns,Rs,Dt) to predict BI is dependent on the density of data records sampled.At a sampling density of about one sample per 0.5 ft BI is predicted with RMSE~0.056 and R^(2)~0.790.At a sampling density of about one sample per0.1 ft BI is predicted with RMSE~0.008 and R^(2)~0.995.Adding a stratigraphic height index as an additional (sixth)input variable method improves BI prediction accuracy to RMSE~0.003 and R^(2)~0.999 for the two wells with only 1 record in 10,000 yielding a BI prediction error of>±0.1.The model has the potential to be applied in an unsupervised basis to predict BI from basic well log data in surrounding wells lacking mineralogical measurements but with similar lithofacies and burial histories.The method could also be extended to predict elastic rock properties in and seismic attributes from wells and seismic data to improve the precision of brittleness index and fracability mapping spatially.

VB6.0数据环境(DataEnvironment)对象应用技术探讨

本文介绍了使用 VB6 .0数据环境 (Data Envi-ronment)对象进行数据库应用系统开发的优越性 ,具体举例说明了应用中值得注意的重要技术环节 ,从而使数据库应用系统的开发更加直观、简洁。

ADO对象的Data Shape技术及其应用

讨论了ADO对象引入的Data Shape与层次结构记录集技术， 并通过一些具体的实例说明了该技术在各种的环境下的具体使用方法。这类技术为复杂的主 细表操作与分类统计计算问题提供了一个比较简单而又直观的处理方法。

使用常规收集的卫生数据开展药物流行病学研究报告规范(RECORD-PE)及解读

常规收集的卫生数据在药物流行病学研究中发挥着越来越重要的作用。根据STROBE声明拓展而来的RECORD声明是目前利用常规收集卫生数据进行研究的主要指南,但RECORD声明并不完全适用于药物流行病学研究,RECORD-PE(RECORD for Pharmacoepidemiology)声明在RECORD声明基础上,针对药物流行病学研究扩展了15个条目。本文对RECORD-PE进行了详细解读,并给出了相应的实例,便于我国学者更好地掌握RECORD-PE声明并进行利用。

Data Report在复杂报表中的应用研究

本文介绍了目前Visual Basic6.0中制作复杂报表的几种方法:基于Excel生成复杂报表、利用水晶报表制作复杂报表、使用第三方控件生成复杂报表以及用VB自带的报表设计器(Data Report)生成报表等,比较了它们的优缺点,据此提出使用VB自带的Data Report制作报表的优越性,并以大坝渗流分析系统的一个复杂报表实例说明使用Data Report制作复杂报表的具体方法和步骤。

Research on the completeness of earthquake data in the Chinese mainland（I）──North China

In terms of the temporal-spatial distribution features of earthquakes, we study the completeness of historical data in North China where there is the most plenty historical data and with the longest record history by some meth ods of analysis and comparison. The results are obtained for events with Ms≥4 are largely complete since 1484 in North China (except Huanghai sea region and remote districts, such as Nei Mongol Autonomous region), but quakes with Ms≥6 are largely complete since 1291 in the middle and lower reaches of the Yellow River.

Polarization readout analysis for multilevel phase change recording by crystallization degree modulation

Four different states of Si15Sb85 and Ge2Sb2Te5 phase change memory thin films are obtained by crystallization degree modulation through laser initialization at different powers or annealing at different temperatures. The polarization characteristics of these two four-level phase change recording media are analyzed systematically. A simple and effective readout scheme is then proposed, and the readout signal is numerically simulated. The results show that a high-contrast polarization readout can be obtained in an extensive wavelength range for the four-level phase change recording media using common phase change materials. This study will help in-depth understanding of the physical mechanisms and provide technical approaches to multilevel phase change recording.

A Data Model of EHR Storage Based on HL7 RIM

Effective storage of healthcare information is the foundation of the rapid development of electronic health recorder （EHR）. This paper presents a research on the data model of EHR storage, focusing on solving the complex and abstract information model and data types in HL7 V3（Health Level 7 Version 3.0） as well as HL7 localizing storage. Using health-care information exchange and sharing standards may settle the problem of interoperability between heterogeneous systems. HL7 is the most widely accepted and used standard. HL7 standardizes the information format in the process of transmission. Nevertheless, it can not guide the storage of health-care data directly. HL7 V3 developed an abstract information model-reference information model （RIM） and data types in it are complex. In this paper, we propose an approach on converting from the abstract HL7 V3 information model to the relational data model. Our approach resolves RIM＇s complex relationships and its data types and localizes the HL7 V3.

Improving Diabetes Care by Data Feedback and Diabetes Templates

Importance: Health information technology has been used to improve diabetes care and outcomes. With the implementation of our diabetes registry, we discovered several flaws in the data. Objective: The aim of this paper is to demonstrate whether improving diabetes templates in electronic medical records associated with data feedback, improves process and quality outcomes for patients with diabetes. Methods: We redesigned our chronic diseases templates and clinical flow, built a diabetes registry and used the data for feedback to educate providers, staff and address inconsistencies. A total of 724 active diabetic patients were identified in October 2009 (pre-implementation) and 731 active diabetic patients were identified in June 2011 (post-implementation). Results: The results showed an improvement in the process outcomes of ordering hemoglobin A1C every 6 months and a microalbumin every 12 months (p-value 0.05). Discussion: Data feedback and lessons learned were instrumental to our practice change.

基于QAR数据的民机高高原进近着陆风险评估方法

民机高高原进近着陆是高原飞行高风险阶段。为有效实施高高原进近着陆风险识别和等级判据,提出基于熵权可变模糊识别的长短时记忆网络与深度神经网络(LSTM-DNN)相融合的深度学习风险评估方法。基于快速存取记录器(QAR)记录的高高原飞行数据,借鉴民机飞行品质监控(FOQA)咨询通告和行业QAR监控标准,结合指标重要度分析与Delphi专家调查,提取着陆时航向变化大、航迹低、610~305 m进近时下降率大、接地时垂直加速度及153~15 m进近时下降率大5个关键监控项目作为民机高高原进近着陆风险评估指标。为克服评估指标权重主观性偏差,应用熵权法确定评估指标权重,基于可变模糊识别方法构建风险等级隶属函数,建立基于LSTM-DNN的民机高高原进近着陆风险评估模型。以成都—拉萨进近着陆航段为例,提取QAR数据,对该风险评估模型进行训练与测试,并与Logistic多元回归、支持向量机(SVM)等评估方法进行比较,结果表明:所提方法平均准确率达到94.18%,最高可达94.79%,验证了方法的客观有效性。

在线数据记录和扰动观测器相结合的柔性机械臂复合学习控制

本文针对多输入多输出的柔性机械臂动力学模型,提出了一种基于神经网络估计和扰动观测器的复合学习控制策略.首先,通过奇异摄动分析将系统解耦为快慢子系统.然后,针对慢变刚性子系统的动力学模型,基于在线数据记录模型构造新型预测误差,结合跟踪误差设计自适应控制律;针对快变柔性子系统采用滑模控制抑制弹性振动.在此基础上,构建了扰动观测器实时估计复合扰动信号,并纳入在线数据记录模型作为前馈补偿.最后,基于Lyapunov稳定性分析可证系统误差信号一致终值有界,仿真算例验证了所提策略的有效性和优越性.

Translation in Data Mining to Advance Personalized Medicine for Health Equity

Personalized medicine is the development of “tailored” therapies that reflect traditional medical approaches with the incorporation of the patient’s unique genetic profile and the environmental basis of the disease. These individualized strategies encompass disease prevention and diagnosis, as well as treatment strategies. Today’s healthcare workforce is faced with the availability of massive amounts of patient- and disease-related data. When mined effectively, these data will help produce more efficient and effective diagnoses and treatment, leading to better prognoses for patients at both the individual and population level. Designing preventive and therapeutic interventions for those patients who will benefit most while minimizing side effects and controlling healthcare costs requires bringing diverse data sources together in an analytic paradigm. A resource to clinicians in the development and application of personalized medicine is largely facilitated, perhaps even driven, by the analysis of “big data”. For example, the availability of clinical data warehouses is a significant resource for clinicians in practicing personalized medicine. These “big data” repositories can be queried by clinicians, using specific questions, with data used to gain an understanding of challenges in patient care and treatment. Health informaticians are critical partners to data analytics including the use of technological infrastructures and predictive data mining strategies to access data from multiple sources, assisting clinicians’ interpretation of data and development of personalized, targeted therapy recommendations. In this paper, we look at the concept of personalized medicine, offering perspectives in four important, influencing topics: 1) the availability of “big data” and the role of biomedical informatics in personalized medicine, 2) the need for interdisciplinary teams in the development and evaluation of personalized therapeutic approaches, and 3) the impact of electronic medical record systems and clinical data warehouses on the field of personalized medicine. In closing, we present our fourth perspective, an overview to some of the ethical concerns related to personalized medicine and health equity.

面向数字政府建设的病案数据治理体系:框架建构与实施路径

数字中国战略背景下,病案数据共享复用成为掣肘医疗健康数据服务赋能数字政府建设的“瓶颈”,开展病案数据治理研究是应时之举。基于对现有研究及国内外病案数据开发利用相关案例进行分析并总结存在的问题,在此基础上构建了以治理目标、治理主体、治理客体、治理过程、治理要素为框架的病案数据治理体系的顶层设计,并探索多元主体协同合作、加强病案数据资源建设、体制机制创新和一体化平台构建、制度管理和技术实现双管齐下等实施路径。面向数字政府建设的病案数据治理体系,必须在政府主导下,建立跨领域、跨专业的病案数据管理中心,构建医疗机构—病案数据管理中心—数字政府公共服务一体化平台,实现不同机构、不同地区之间的病案数据整合、共享和复用,为完善民生信息服务体系建设提供病案数据支撑,为数字政府建设赋能。