Machine Learning for Selecting Important Clinical Markers of Imaging Subgroups of Cerebral Small Vessel DiseaseBased on a Common Data Model

Differences in the imaging subgroups of cerebral small vessel disease(CSVD)need to be further explored.First,we use propensity score matching to obtain balanced datasets.Then random forest(RF)is adopted to classify the subgroups compared with support vector machine(SVM)and extreme gradient boosting(XGBoost),and to select the features.The top 10 important features are included in the stepwise logistic regression,and the odds ratio(OR)and 95%confidence interval(CI)are obtained.There are 41290 adult inpatient records diagnosed with CSVD.Accuracy and area under curve(AUC)of RF are close to 0.7,which performs best in classification compared to SVM and XGBoost.OR and 95%CI of hematocrit for white matter lesions(WMLs),lacunes,microbleeds,atrophy,and enlarged perivascular space(EPVS)are 0.9875(0.9857−0.9893),0.9728(0.9705−0.9752),0.9782(0.9740−0.9824),1.0093(1.0081−1.0106),and 0.9716(0.9597−0.9832).OR and 95%CI of red cell distribution width for WMLs,lacunes,atrophy,and EPVS are 0.9600(0.9538−0.9662),0.9630(0.9559−0.9702),1.0751(1.0686−1.0817),and 0.9304(0.8864−0.9755).OR and 95%CI of platelet distribution width for WMLs,lacunes,and microbleeds are 1.1796(1.1636−1.1958),1.1663(1.1476−1.1853),and 1.0416(1.0152−1.0687).This study proposes a new analytical framework to select important clinical markers for CSVD with machine learning based on a common data model,which has low cost,fast speed,large sample size,and continuous data sources.

Security Vulnerability Analyses of Large Language Models (LLMs) through Extension of the Common Vulnerability Scoring System (CVSS) Framework

Large Language Models (LLMs) have revolutionized Generative Artificial Intelligence (GenAI) tasks, becoming an integral part of various applications in society, including text generation, translation, summarization, and more. However, their widespread usage emphasizes the critical need to enhance their security posture to ensure the integrity and reliability of their outputs and minimize harmful effects. Prompt injections and training data poisoning attacks are two of the most prominent vulnerabilities in LLMs, which could potentially lead to unpredictable and undesirable behaviors, such as biased outputs, misinformation propagation, and even malicious content generation. The Common Vulnerability Scoring System (CVSS) framework provides a standardized approach to capturing the principal characteristics of vulnerabilities, facilitating a deeper understanding of their severity within the security and AI communities. By extending the current CVSS framework, we generate scores for these vulnerabilities such that organizations can prioritize mitigation efforts, allocate resources effectively, and implement targeted security measures to defend against potential risks.

XML-based integration data model and schema mappingin multidatabase systems

Multidatabase systems are designed to achieve schema integration and data interoperation among distributed and heterogeneous database systems. But data model heterogeneity and schema heterogeneity make this a challenging task. A multidatabase common data model is firstly introduced based on XML, named XML-based Integration Data Model (XIDM), which is suitable for integrating different types of schemas. Then an approach of schema mappings based on XIDM in multidatabase systems has been presented. The mappings include global mappings, dealing with horizontal and vertical partitioning between global schemas and export schemas, and local mappings, processing the transformation between export schemas and local schemas. Finally, the illustration and implementation of schema mappings in a multidatabase prototype - Panorama system are also discussed. The implementation results demonstrate that the XIDM is an efficient model for managing multiple heterogeneous data sources and the approaches of schema mapping based on XIDM behave very well when integrating relational, object-oriented database systems and other file systems.

Panel data models with cross-sectional dependence: a selective review

In this review, we highlight some recent methodological and theoretical develop- ments in estimation and testing of large panel data models with cross-sectional dependence. The paper begins with a discussion of issues of cross-sectional dependence, and introduces the concepts of weak and strong cross-sectional dependence. Then, the main attention is primarily paid to spatial and factor approaches for modeling cross-sectional dependence for both linear and nonlinear （nonparametric and semiparametric） panel data models. Finally, we conclude with some speculations on future research directions.

Land Response to Atmosphere at Different Resolutions in the Common Land Model over East Asia

Towards a better understanding of hydrological interactions between the land surface and atmosphere, land surface mod- els are routinely used to simulate hydro-meteorological fluxes. However, there is a lack of observations available for model forcing, to estimate the hydro-meteorological fluxes in East Asia. In this study, Common Land Model （CLM） was used in offline-mode during the summer monsoon period of 2006 in East Asia, with different forcings from Asiaflux, Korea Land Data Assimilation System （KLDAS）, and Global Land Data Assimilation System （GLDAS）, at point and regional scales, separately. The CLM results were compared with observations from Asiaflux sites. The estimated net radiation showed good agreement, with r = 0.99 for the point scale and 0.85 for the regional scale. The estimated sensible and latent heat fluxes using Asiaflux and KLDAS data indicated reasonable agreement, with r = 0.70. The estimated soil moisture and soil temperature showed similar patterns to observations, although the estimated water fluxes using KLDAS showed larger discrepancies than those of Asiaflux because of scale mismatch. The spatial distribution of hydro-meteorological fluxes according to KLDAS for East Asia were compared to the CLM results with GLDAS, and the GLDAS provided online. The spatial distributions of CLM with KLDAS were analogous to CLM with GLDAS, and the standalone GLDAS data. The results indicate that KLDAS is a good potential source of high spatial resolution forcing data. Therefore, the KLDAS is a promising alternative product, capable of compensating for the lack of observations and low resolution grid data for East Asia.

Inter-Data Commonality Detection for Spectrum Monitoring in Wireless Sensor Networks

Cooperative spectrum monitoring with multiple sensors has been deemed as an efficient mechanism for improving the monitoring accuracy and enlarging the monitoring area in wireless sensor networks.However,there exists redundancy among the spectrum data collected by a sensor node within a data collection period,which may reduce the data uploading efficiency.In this paper,we investigate the inter-data commonality detection which describes how much two data have in common.We define common segment set and divide it into six categories firstly,then a method to measure a common segment set is conducted by extracting commonality between two files.Moreover,the existing algorithms fail in finding a good common segment set,so Common Data Measurement(CDM)algorithm that can identify a good common segment set based on inter-data commonality detection is proposed.Theoretical analysis proves that CDM algorithm achieves a good measurement for the commonality between two strings.In addition,we conduct an synthetic dataset which are produced randomly.Numerical results shows that CDM algorithm can get better performance in measuring commonality between two binary files compared with Greedy-String-Tiling(GST)algorithm and simple greedy algorithm.

省域数字经济对共同富裕的影响研究--基于要素流动视角的空间计量检验

在信息化时代,数字经济如何促进共同富裕具有重要的现实意义。基于2011―2021年31个省份面板数据,测算数字经济指数和共同富裕指数,选取人才和资本要素流动为中介变量,数据要素流动环境为调节变量,运用空间杜宾模型(SDM)实证检验数字经济发展对共同富裕的影响。结果表明:数字经济发展能明显促进共同富裕,对邻边地区存在显著的正向的空间溢出效应,且在地理分布上呈现明显的区域异质性。机制分析发现,数字经济能够通过人才和资本要素流动影响共同富裕;数字经济对共同富裕存在倒“U”型的非线性效应,且人才和资本要素流动对数字经济影响共同富裕起到“边际效应递增”作用;数据要素流动环境能合理调节数字经济对共同富裕的影响。据此,应大力发展数字经济,促进人才与资本要素的流动和精准匹配,形成优势互补、区域协调发展的新格局。

基于IFC标准的机场道面结构信息模型存储与传递方法

为解决机场跑道系统在全生命周期建筑信息模型(BIM)应用中的信息传递和数据交互问题,将工业基础类(IFC)标准在机场跑道系统进行扩展,建立机场道面结构的IFC实体架构,在此基础上创建面向运维应用的刚性道面板实体,并进行几何信息与属性信息的扩展;提出基于IFC的属性信息添加与传递方法,对信息传递过程中的属性信息丢失率进行分析。研究表明:通过建立IFC标准模型,可以实现机场跑道系统的信息传递和数据交互;采用基础模型族扩展法进行机场道面模型传递的属性信息丢失率高达84.21%;采用IFC解析扩展法首次传递可保证信息不丢失,但二次传递信息存在丢失问题;族模型IFC解析扩展法可实现信息首次传递不丢失,二次传递属性信息丢失率低至16.7%,并且采用不同IFC标准版本对模型传递过程中的属性信息丢失率产生较大影响。应用IFC标准进行机场领域扩展具有较高的可行性,所提的信息传递方法可实现机场道面全生命周期信息传递。研究成果可为机场飞行区新IFC实体的扩展提供支持,为机场工程数字化提供技术参考。

临床专病数据库建设现状与思考

目的/意义为我国专病数据库建设、临床科研和医院信息互联互通等提供新思路。方法/过程结合国内外医疗体系和医疗信息化发展历程阐述专病数据库发展现状,总结国内两种主流专病数据库构建模式,提出基于SQL集成技术和通用数据模型的专病数据库构建方案。结果/结论该方案可快速构建专病数据库,并在数据层面实现互联互通。

大型综合性医院生物样本信息资源大数据科研平台的建设与应用

目的/意义通过整合临床与生物样本信息,构建生物样本信息资源大数据科研平台,为科研人员提供一站式数据检索、集成、分析服务,建立数据治理体系,提升医院临床科研基础设施建设水平。方法/过程采用通用数据模型和数据治理技术,通过抽取、转换、加载等步骤整合来自不同厂商的数据源,提供统一数据访问入口。结果/结论该生物样本信息资源大数据科研平台具备多维数据筛查和快速集成分析的优势,可为临床科研提供支撑。

A Fluctuation Test for Structural Change Detection in Heterogeneous Panel Data Models

Structural change in panel data is a widespread phenomena. This paper proposes a fluctuation test to detect a structural change at an unknown date in heterogeneous panel data models with or without common correlated effects. The asymptotic properties of the fluctuation statistics in two cases are developed under the null and local alternative hypothesis. Furthermore, the consistency of the change point estimator is proven. Monte Carlo simulation shows that the fluctuation test can control the probability of type I error in most cases, and the empirical power is high in case of small and moderate sample sizes. An application of the procedure to a real data is presented.

PowerDesigner建模时CDM与PDM的转换原则

介绍了如何利用E-R图建立概念数据模型(CDM),如何将概念数据模型 转换为物理数据模型(PDM),阐述了使用PowerDesigner将概念数据模型转换为物理数 据模型的基本原则.

基于公共数据模型CDM的数据融合

提出了一种基于超图的公用数据模型CDM(common data model),实现了关系模式与XML文档相互转换的方法,从而实现了这两类数据的融合.

CDM在建筑结构设计阶段的应用

近年来,随着建筑信息化的推广,BIM技术凭借其可视化、参数化、集成性等优势,在建筑结构设计阶段展现出极大的优势。然而,基于IFC格式的传统BIM技术因其自身的一些弊端终究无法真正服务于实际工程。本文将阐述其存在的弊端并介绍CDM技术以及基于CDM的BIM技术在建筑结构设计中的应用、优势与发展。

中国天气雷达基数据模型与CDM集成研究

现有标准格式雷达基数据解析工具在设计上存在通用性和抽象性不足的问题,不便于雷达数据的解析和处理。为了解决这个问题,本文基于Unidata的CDM(Common Data Model),设计和构建了中国天气雷达基数据模型,在数据模型层面实现了对天气雷达标准格式基数据的访问,并以Unidata开源的NetCDF Java库和IDV(Integrated Data Viewer)可视化软件为基础,形成了一套基于CDM的天气雷达标准格式基数据内容提取和可视化分析工具。本研究以广州雷达新旧两种格式基本反射率数据对比为例,展示了研究成果在多普勒天气雷达标准格式基数据评估中的应用。结果表明:本研究成果方便了雷达标准格式基数据的使用,对雷达标准格式基数据的业务应用起到了促进作用。本研究成果亦可应用于雷达基数据处理与分析相关的实际业务和科研工作中,为雷达资料的应用提供基础支持。

基于CDM的分布式服务器诊断系统

基于Web的系统管理标准WBEM提出后,DMTF组织针对服务器管理和诊断都进行了相应的扩展,并提出了CDM的概念。CIM/WBEM的分布式和面向对象的特点为CDM集成各种硬件平台下的硬件诊断提供了可能。该文给出了一个分布式硬件诊断系统模型,并详细讨论了设计和实现。

BIM通用数据环境思考与实践

通用数据环境(CDE)是解决国内BIM协调性不足的有效手段,也是建筑行业数字化转型的重要工具。目前,国内CDE的建设仍处于探索阶段,与国外存在较大差距,且国产CDE建设仍面临模型数据解析流转等核心技术问题。本文参考CDE的理念,建立了信息共享的底层逻辑,搭建了AECMate 365云平台,为信息集成和共享提供了参考,并为中建自主可控生态系统的建设提供了技术基础。

Metadata as a Methodological Commons:From Aboutness Description to Cognitive Modeling

Metadata is data about data,which is generated mainly for resources organization and description,facilitating finding,identifying,selecting and obtaining information.With the advancement of technologies,the acquisition of metadata has gradually become a critical step in data modeling and function operation,which leads to the formation of its methodological commons.A series of general operations has been developed to achieve structured description,semantic encoding and machine-understandable information,including entity definition,relation description,object analysis,attribute extraction,ontology modeling,data cleaning,disambiguation,alignment,mapping,relating,enriching,importing,exporting,service implementation,registry and discovery,monitoring etc.Those operations are not only necessary elements in semantic technologies(including linked data)and knowledge graph technology,but has also developed into the common operation and primary strategy in building independent and knowledge-based information systems.In this paper,a series of metadata-related methods are collectively referred to as'metadata methodological commons',which has a lot of best practices reflected in the various standard specifications of the Semantic Web.In the future construction of a multi-modal metaverse based on Web 3.0,it shall play an important role,for example,in building digital twins through adopting knowledge models,or supporting the modeling of the entire virtual world,etc.Manual-based description and coding obviously cannot adapted to the UGC(User Generated Contents)and AIGC(AI Generated Contents)-based content production in the metaverse era.The automatic processing of semantic formalization must be considered as a sure way to adapt metadata methodological commons to meet the future needs of Al era.

OMOP CDM在临床科研中的应用思考

目的:针对RCT应用于临床研究的不足,借鉴国外经验,探索如何利用临床医疗数据进行观察性研究,挖掘临床医疗数据的价值。方法:从当前临床科研数据利用现状、国外的观测性医疗结果合作组织(OMOP)的通用数据模型(CDM)的经验出发,探讨了CDM与临床科研的关系,并对其应用于临床科研的难点进行了思考与总结。结果:OMOP CDM应用于临床观察性研究有值得借鉴的地方,但仍需要考虑数据结构化程度、数据质量控制、标准化术语、隐私保护等问题。结论:其方法对临床数据应用具有一定的参考价值和借鉴作用。

Hubble Diagram Test of 280 Supernovae Redshift Data

We compare the Hubble diagram calculated from the observed redshift (RS)/magnitude (μ) data of 280 Supernovae in the RS range of z = 0.0104 to 8.1 with Hubble diagrams inferred on the basis of the exponential tired light and the Lambda Cold Dark Matter (ΛCDM) cosmological model. We show that the experimentally measured Hubble diagram follows clearly the exponential photon flight time (tS)/RS relation, whilst the data calculated on the basis of the ΛCDM model exhibit poor agreement with the observed data.