Network Analysis Modeling Towards GIS Based on Object-Relation Database

This paper compares the differences between the mathematical model in graph theory and GIS network analysis model. Thus it claims that the GIS network analysis model needs to solve. Then this paper introduces the spatial data management methods in object\|relation database for GIS and discusses its effects on the network analysis model. Finally it puts forward the GIS network analysis model based on the object\|relation database. The structure of the model is introduced in detail and research is done to the internal and external memory data structure of the model. The results show that it performs well in practice.

A Software Risk Analysis Model Using Bayesian Belief Network

The uncertainty during the period of software project development often brings huge risks to contractors and clients. If we can find an effective method to predict the cost and quality of software projects based on facts like the project character and two-side cooperating capability at the beginning of the project,we can reduce the risk. Bayesian Belief Network(BBN) is a good tool for analyzing uncertain consequences, but it is difficult to produce precise network structure and conditional probability table.In this paper,we built up network structure by Delphi method for conditional probability table learning,and learn update probability table and nodes’confidence levels continuously according to the application cases, which made the evaluation network have learning abilities, and evaluate the software development risk of organization more accurately.This paper also introduces EM algorithm, which will enhance the ability to produce hidden nodes caused by variant software projects.

Link Budget and Networking Analysis of FuTURE 4G TDD Trial System

The FuTURE 4G Time Division Duplex (TDD) trial system uses 3.5 GHz carrier frequency and several crucial technologies including broadband Multiple Input Multiple Output (MIMO) and Orthogonal Frequency Division Multiplexing (OFDM). These technologies challenge the link budget and networking analysis of the FuTURE 4G TDD trial network. This paper analyzes the practical 3.5 GHz propagation model and the link budget of Radio Frequency (RF) parameters of the trial system. Moreover,it introduces networking analysis and network planning of the trial system,which combines the field test results of the MIMO system. The FuTURE 4G TDD trial system and its trial network have been accomplished with successful checkup. The trial system fulfills all the requirements with two Access Points (AP) and three Mobile Terminals (MT),which supports multi-user,mobility,a high peak rate of 100 Mb/s,High-Definition TV (HDTV),high-speed data download,and Voice over IP (VoIP) services.

Drug repurposing for cancer treatment through global propagation with a greedy algorithm in a multilayer network

Objective:Drug repurposing,the application of existing therapeutics to new indications,holds promise in achieving rapid clinical effects at a much lower cost than that of de novo drug development.The aim of our study was to perform a more comprehensive drug repurposing prediction of diseases,particularly cancers.Methods:Here,by targeting 4,096 human diseases,including 384 cancers,we propose a greedy computational model based on a heterogeneous multilayer network for the repurposing of 1,419 existing drugs in Drug Bank.We performed additional experimental validation for the dominant repurposed drugs in cancer.Results:The overall performance of the model was well supported by cross-validation and literature mining.Focusing on the top-ranked repurposed drugs in cancers,we verified the anticancer effects of 5 repurposed drugs widely used clinically in drug sensitivity experiments.Because of the distinctive antitumor effects of nifedipine(an antihypertensive agent)and nortriptyline(an antidepressant drug)in prostate cancer,we further explored their underlying mechanisms by using quantitative proteomics.Our analysis revealed that both nifedipine and nortriptyline affected the cancer-related pathways of DNA replication,the cell cycle,and RNA transport.Moreover,in vivo experiments demonstrated that nifedipine and nortriptyline significantly inhibited the growth of prostate tumors in a xenograft model.Conclusions:Our predicted results,which have been released in a public database named The Predictive Database for Drug Repurposing(PAD),provide an informative resource for discovering and ranking drugs that may potentially be repurposed for cancer treatment and determining new therapeutic effects of existing drugs.

XML-based Data Processing in Network Supported Collaborative Design

In the course of network supported collaborative design, the data processing plays a very vital role. Much effort has been spent in this area, and many kinds of approaches have been proposed. Based on the correlative materials, this paper presents extensible markup language （XML） based strategy for several important problems of data processing in network supported collaborative design, such as the representation of standard for the exchange of product model data （STEP） with XML in the product information expression and the management of XML documents using relational database. The paper gives a detailed exposition on how to clarify the mapping between XML structure and the relationship database structure and how XML-QL queries can be translated into structured query language （SQL） queries. Finally, the structure of data processing system based on XML is presented.

Network pharmacology-based approach to investigate the mechanism of Huang-Lian-Jie-Du-Decoction for treatment of type 2 diabetes mellitus

Background:Although the benefits of Huang-Lian-Jie-Du-Decoction(HLJDD)on type 2 diabetes mellitus are noted,the material base and action mechanism remain unknown.This paper aim is to reveal the material base and action mechanism of HLJDD against type 2 diabetes mellitus in a system pharmacology framework.Methods:The compounds in HLJDD were first retrieved from the Traditional Chinese Medicine Systems Pharmacology database and analysis platform.Once retrieved,they were fed into the SwissTargetPrediction database to predict the interacting targets.Meanwhile,a human expression profile dataset was analyzed in the Gene Expression Omnibus database,and subsequently,the differentially expressed genes were compared to the HLJDD-related targets.We conducted a protein-protein interaction analysis,Kyoto Encyclopedia of Genes and Genomes pathway analysis,and Gene Ontology analysis to identify the potential active compounds and targets.Lastly,to verify the binding affinities of those compounds and targets,we performed molecular docking.Results:We obtained 15 key compounds,such as quercetin,epiberberine,and berberine,and 10 hub genes,such as IκB kinase-βand phosphatidylinositol 3-kinase regulatory subunit alpha.The top 10 enriched pathways were also found to be tightly related to type 2 diabetes mellitus,including insulin resistance and FoxO signaling pathway.Moreover,all the key compounds were found to bind well to the hub genes.Particularly for the target of IκB kinase-β,11 out of 15 compounds bound to it with energies of<−9.0 kcal/mol.Conclusion:In summary,15 key compounds of HLJDD may affect type 2 diabetes mellitus development by multiple genes such as IκB kinase-βand phosphatidylinositol 3-kinase regulatory subunit alpha and signaling pathways such as insulin resistance and FoxO signaling pathway.

基于长链非编码RNA调控ceRNA网络的缺血性脑卒中相关分子机制

目的:利用生物信息学分析筛选缺血性脑卒中(IS)的差异表达基因,构建竞争性内源RNA(ce RNA)调控网络,探讨IS的相关分子机制。方法:从GEO数据库下载IS相关的mi RNA、m RNA和lnc RNA测序数据,借助R软件获取差异基因,通过star Base、mi RDB和mi Rwalk数据库进行lnc RNA-mi RNA和mi RNA-m RNA关系预测,并构建ce RNA网络。预测与差异分析的结果取交集后筛选出差异靶基因(DETm RNAs),利用DAVID数据库进行KEGG和GO富集分析,String数据库构建蛋白互作网络(PPI),采用Cytoscape软件识别核心靶基因。结果:共筛选出存在明显差异表达的mi RNAs 20个,m RNAs 1512个,lnc RNAs 278个,并成功构建了5lnc RNAs-6mi RNAs-102m RNAs互作的ce RNA网络。筛选出的285个DETm RNAs所富集的功能主要包括染色质的组织、磷蛋白磷酸酶活性的负向调节和细胞周期等生物学过程,涉及白细胞经内皮迁移、血小板激活等信号通路,最终识别出排名前10的核心靶基因为CREB1、MAPK1、GSK3B、SP1、PIK3R1、NR3C1、NCOR1、NFATC1、SETD2、NSD1。结论:构建ce RNA网络有助于进一步认识IS的分子机制和筛选潜在的生物标志物,为后续康复治疗靶点的确定和制定康复策略提供一些线索。

能量管理系统（EMS）中高级应用软件的设计与开发

本文按三个水平分别介绍了能量管理系统（ＥＭＳ）的应用软件：能量管理、发电控制与计划、网络分析，强调了“自上而下”的设计原则．由电力部电力科学研究院开发的ＥＭＳ高级应用软件已用于中国三大网调：华北、华中、华东和最大的省调山东。这些软件可以使经验型调度上升到分析型调度，从而提高电力系统运行的安全性与经济性。

基于NBN-EM的地铁施工事故致因分析模型研究

地铁施工事故具有易发性且事故类型复杂多样,针对现有地铁施工事故分析方法多依赖于专家主观经验构建,且仅有较少方法对事故报告信息加以利用的问题,提出一种基于NBN-EM的地铁施工事故致因分析模型。首先,以搜集的2010—2021年间的223起事故报告为数据来源,采用统计学方法提取及筛选风险因素,进一步归纳建立事故致因分析的指标体系;其次,采用改进的朴素贝叶斯网络构建风险因素指标与事故类型关系的图形结构,同时分层随机抽样80%的数据为训练样本,借助EM算法和Netica软件进行数据学习,确定各节点的先验概率和条件概率参数;最后,通过贝叶斯网络推理和敏感性分析得到不同类型事故的关键致因排序,不同视角下的情景分析对风险因素组合作用下的事故发生概率和风险源识别进行了确定。研究结果表明:施工工法、施工方案内容、安全隐患排查分别为三个维度上造成事故发生的最重要因素,不同类型事故的关键风险因素具有差异性,应区别预控,模型测试验证方法的有效性,平均正确率为84.55%。

多元数据融合的智能配电网负荷分析预测管理系统

针对多元大数据在智能配电网中的应用问题,为实现配电网的精益管理、科学预测和合理规划,文章开发了多元数据融合的智能配电网负荷分析预测管理系统。对软件系统的总体框架进行了设计;对软件系统的各功能模块进行开发和介绍;给出了软件系统的一个应用实例。该系统充分利用海量的历史负荷数据进行负荷特性分析,建立负荷特征库以及业扩信息库,通过对新接入用户进行信息匹配实现负荷管理及最大负荷预测。此外,该系统建立负荷预测方法模型库,可提供不同维度的负荷预测功能,从传统的地区负荷预测转变为馈线负荷预测,结合馈线现状以及业扩信息优化用户接入决策。总的来说,该系统具有功能模块数据链路互通、不同功能之间能提供信息支持、整体采用模块化设计思想等特点,可满足电网企业的日常应用需求。

结直肠侧向发育型肿瘤可能发病机制研究

目的:结直肠侧向发育型肿瘤(LST)检出率逐年升高,但其发病机制尚不明确。本研究将通过生物信息学方法分析该疾病mRNA表达数据集,探讨其发病机制。方法:利用差异基因分析,明确GSE77635中LST与正常对照组差异基因表达的情况,并对差异基因进行GO及KEGG分析。利用差异基因建立蛋白相互作用网络后选取关键基因进行分析。结果:共有1194个差异基因,其中包括469个上调基因及725个下调基因。其中PI3K-Akt信号通路可能参与LST的发生及发展。建立PPI网络后共选取5个关键差异基因(TP53、FN1、MYC、CD44、KRAS)。结论:LST的发生可能与PI3K-Akt通路密切相关。

Computational and bioinformatics tools for understanding disease mechanisms

Computational methods have significantly transformed biomedical research,offering a comprehensive exploration of disease mechanisms and molecular protein functions.This article reviews a spectrum of computational tools and network analysis databases that play a crucial role in identifying potential interactions and signaling networks contributing to the onset of disease states.The utilization of protein/gene interaction and genetic variation databases,coupled with pathway analysis can facilitate the identification of potential drug targets.By bridging the gap between molecular-level information and disease understanding,this review contributes insights into the impactful utilization of computational methods,paving the way for targeted interventions and therapeutic advancements in biomedical research.

基于计算机软件工程的数据库编程技术研究分析

计算机软件是发挥程序作用、有效体现信息技术优势的前提,这要求做好计算机软件工程管理,也客观要求发挥数据库编程技术价值。本文以数据库编程技术特征和功能为切入点予以简述,在此基础上,分别从客户端计算机软件工程中数据库的建立、服务端计算机软件工程中数据库编程技术的应用两个角度做具体分析,给出数据库需求分析、神经网络架构建设、节点加密等具体工作环节和工作方法,为计算机软件工程管理工作提供参考。

加权基因共表达网络分析结肠癌免疫相关生物标志物

目的旨在鉴定可能参与结肠癌发生发展的免疫相关预后基因,采用加权基因共表达网络对癌症基因组图谱(TCGA)和基因表达综合数据库(GEO)联合分析,筛选免疫生物标志物。方法2022年11月至12月,对GSE41657数据集和TCGA结肠癌数据集中免疫相关基因进行加权基因共表达网络分析,并从关键模块筛选免疫相关生物标志物,利用TCGA数据库中基因表达数据和临床信息进一步分析。结果获得了结肠癌发生发展中与免疫相关的最关键模块,其功能富集到免疫细胞趋化、游走、活化、增殖、迁徙等,通路富集到趋化因子信号通路、抗原处理和呈递、MAPK信号通路、HIF-1信号通路、PI3K-Akt信号通路、NF-kappa B信号通路等。找到了有望作为结肠癌预后靶点的10个基因:NMB、SCG2、IL1A、ULBP2、INHBB、COLEC12、F2RL1、ANGPTL1、NR3C2、TNFRSF17。通过TIMER数据库筛选出2个与结肠癌免疫相关性最为密切的基因——COLEC12、ANGPTL1,这两个基因富集到诸多免疫相关和癌症相关通路——JAK Stat信号通路、Toll样受体信号通路、MAPK信号通路等,可能在肿瘤免疫微环境中发挥重要作用。结论COLEC12、ANGPTL1可能通过多种信号通路对结肠癌及其免疫微环境进行调控,有望成为潜在的免疫治疗靶点。

基于加权基因共表达网络分析及机器学习构建铁死亡相关基因在多囊卵巢综合征的疾病模型及相关中药预测

目的:利用机器学习分析铁死亡基因与多囊卵巢综合征(PCOS)的相关性,构建诊断及预后风险模型,探讨PCOS的潜在铁死亡机制,并预测相关中药。方法:多芯片标准化合并GEO数据库的卵巢组织芯片,采用加权基因共表达网络分析(WGCNA)算法筛选与PCOS相关的铁死亡特征基因,对特征基因进行功能富集分析,利用多种机器学习进行构建并筛选疾病模型,提取最优的疾病特征基因,并构建风险模型,分析其与免疫浸润细胞及功能之间的相关性,对铁死亡基因进行中药及小分子化合物预测,并对小分子化合物与铁死亡靶点进行分子对接验证。结果:共提取26个铁死亡与PCOS的强相关交集靶基因,主要在铁死亡通路中的Xc-系统和铁代谢通路上发挥作用。通过WGCNA结合机器学习筛选以HMOX1、IFNA1、LPCAT3、FOXO4和PARP3为特征基因的RF模型有较优的模拟特性,并通过绘制列线图来预测各基因表达的患病风险性。上述模型基因与PCOS的发病呈正相关性。免疫浸润分析提示多种免疫细胞在两组之间存在显著浸润差异,预测的中药中丹参、三七可能是其潜在的分子药物来源,分子对接中丹酚酸B、葛根素与黄芪甲苷A的匹配程度最高。结论:以HMOX1、IFNA1、LPCAT3、FOXO4和PARP3为特征基因的铁死亡相关基因模型可为早期识别和治疗POCS提供新思路。铁死亡相关基因可通过干预多种途径在PCOS中发挥重要作用,中医药在干预PCOS中可能通过调节铁稳态代谢来介导铁死亡途径。

实验室数据动态信息化管理系统研究

随着信息技术的快速发展,实验室信息化管理已成为提升科研效率和质量的关键手段。本文深入探讨了实验室信息化管理在数据处理、资源利用、团队协作和研究创新方面的应用及其显著效益。通过实施电子数据管理、在线资源调度、跨地域协作平台和高级数据分析,信息化管理不仅提高了科研效率,还促进了研究质量的提升和创新能力的增强。信息技术的进一步发展将使实验室管理模式更加高效、安全、协同,为科学研究提供强有力的支持。

外文期刊数据库学科相关评估指标的量化研究——以同济大学理学部学科群为例

高校“双一流”建设的核心任务是学科建设,图书馆的文献资源建设要与学科的发展需求紧密结合,围绕学科建设提供精准的文献资源和服务。文章基于PageRank算法,运用引文分析法和社会网络分析法,提出了三个与学科相关联的外文期刊数据库评估指标,即数据库的学科需求度、数据库的学科学术效益、数据库的学科匹配度,构建了数据库学科评估模型,并以同济大学理学部学科群为例对外文期刊数据库进行实证分析,构建了不同等级的理学部的文献资源集群。

TIFAflow: Enhancing Traffic Archiving System with Flow Granularity for Forensic Analysis in Network Security

The archiving of Internet traffic is an essential function for retrospective network event analysis and forensic computer communication. The state-of-the-art approach for network monitoring and analysis involves storage and analysis of network flow statistic. However, this approach loses much valuable information within the Internet traffic. With the advancement of commodity hardware, in particular the volume of storage devices and the speed of interconnect technologies used in network adapter cards and multi-core processors, it is now possible to capture 10 Gbps and beyond real-time network traffic using a commodity computer, such as n2disk. Also with the advancement of distributed file system （such as Hadoop, ZFS, etc.） and open cloud computing platform （such as OpenStack, CloudStack, and Eucalyptus, etc.）, it is practical to store such large volume of traffic data and fully in-depth analyse the inside communication within an acceptable latency. In this paper, based on well- known TimeMachine, we present TIFAflow, the design and implementation of a novel system for archiving and querying network flows. Firstly, we enhance the traffic archiving system named TImemachine＋FAstbit （TIFA） with flow granularity, i.e., supply the system with flow table and flow module. Secondly, based on real network traces, we conduct performance comparison experiments of TIFAflow with other implementations such as common database solution, TimeMachine and TIFA system. Finally, based on comparison results, we demonstrate that TIFAflow has a higher performance improvement in storing and querying performance than TimeMachine and TIFA, both in time and space metrics.

基于实时车流信息的电动汽车未来行驶工况预测

在全球能源短缺和污染加重的背景下,新能源汽车领域关键技术成为研究热点。然而纯电动汽车剩余续航里程的不确定性,严重地影响了纯电动汽车的进一步推广。因此,精确地预测车辆未来能耗以确定其剩余行驶里程,具有重大意义。本研究基于实车在环实验平台,将测试车辆的历史行车数据OBD和同时收集的百度API实时车流信息经过预处理和特征参数的提取,再使用EM聚类分析算法将数据分为8个典型子工况,然后将聚类后的数据用于训练RBF神经网络分类器。根据百度API提供的实时车流信息,采用RBF分类器预测车辆在预定行驶路线上的工况类型和平均能耗,从而精确地预测汽车的剩余电量,即剩余续航里程。

电力营销海量客户交易行为异常检测系统设计

为提高电力营销中异常数据的检测能力,设计一种电力营销海量客户交易行为异常检测系统。通过优化系统中央控制器以及数据存储器功能,并设定电源电路为优化后设备供能完成系统的硬件设计,引入聚类分析算法获取用户交易异常信息,并将其输出,实现系统基础功能,实现电力营销海量客户交易行为异常检测系统的软件设计。性能测试结果表明:本文系统的功能测试合格,满足使用功能的要求;本文系统对海量数据的承载力较高,异常数据检测误差小,行为异常区分正确率高。本文系统表现出较好的应用性能。