Multi-source information fused generative adversarial network model and data assimilation based history matching for reservoir with complex geologies

For reservoirs with complex non-Gaussian geological characteristics,such as carbonate reservoirs or reservoirs with sedimentary facies distribution,it is difficult to implement history matching directly,especially for the ensemble-based data assimilation methods.In this paper,we propose a multi-source information fused generative adversarial network(MSIGAN)model,which is used for parameterization of the complex geologies.In MSIGAN,various information such as facies distribution,microseismic,and inter-well connectivity,can be integrated to learn the geological features.And two major generative models in deep learning,variational autoencoder(VAE)and generative adversarial network(GAN)are combined in our model.Then the proposed MSIGAN model is integrated into the ensemble smoother with multiple data assimilation(ESMDA)method to conduct history matching.We tested the proposed method on two reservoir models with fluvial facies.The experimental results show that the proposed MSIGAN model can effectively learn the complex geological features,which can promote the accuracy of history matching.

Fault location of distribution networks based on multi-source information

In order to promote the development of the Internet of Things(IoT),there has been an increase in the coverage of the customer electric information acquisition system(CEIAS).The traditional fault location method for the distribution network only considers the information reported by the Feeder Terminal Unit(FTU)and the fault tolerance rate is low when the information is omitted or misreported.Therefore,this study considers the influence of the distributed generations(DGs)for the distribution network.This takes the CEIAS as a redundant information source and solves the model by applying a binary particle swarm optimization algorithm(BPSO).The improved Dempster/S-hafer evidence theory(D-S evidence theory)is used for evidence fusion to achieve the fault section location for the distribution network.An example is provided to verify that the proposed method can achieve single or multiple fault locations with a higher fault tolerance.

A multi-source information fusion method for tool life prediction based on CNN-SVM

For milling tool life prediction and health management,accurate extraction and dimensionality reduction of its tool wear features are the key to reduce prediction errors.In this paper,we adopt multi-source information fusion technology to extract and fuse the features of cutting vibration signal,cutting force signal and acoustic emission signal in time domain,frequency domain and time-frequency domain,and downscale the sample features by Pearson correlation coefficient to construct a sample data set;then we propose a tool life prediction model based on CNN-SVM optimized by genetic algorithm(GA),which uses CNN convolutional neural network as the feature learner and SVM support vector machine as the trainer for regression prediction.The results show that the improved model in this paper can effectively predict the tool life with better generalization ability,faster network fitting,and 99.85%prediction accuracy.And compared with the BP model,CNN model,SVM model and CNN-SVM model,the performance of the coefficient of determination R2 metric improved by 4.88%,2.96%,2.53%and 1.34%,respectively.

Multi-segment and Multi-ply Overlapping Process of Multi Coupled Activities Based on Valid Information Evolution

Complex product development will inevitably face the design planning of the multi-coupled activities, and overlapping these activities could potentially reduce product development time, but there is a risk of the additional cost. Although the downstream task information dependence to the upstream task is already considered in the current researches, but the design process overall iteration caused by the information interdependence between activities is hardly discussed; especially the impact on the design process＇ overall iteration from the valid information accumulation process. Secondly, most studies only focus on the single overlapping process of two activities, rarely take multi-segment and multi-ply overlapping process of multi coupled activities into account; especially the inherent link between product development time and cost which originates from the overlapping process of multi coupled activities. For the purpose of solving the above problems, as to the insufficiency of the accumulated valid information in overlapping process, the function of the valid information evolution （VIE） degree is constructed. Stochastic process theory is used to describe the design information exchange and the valid information accumulation in the overlapping segment, and then the planning models of the single overlapping segment are built. On these bases, by analyzing overlapping processes and overlapping features of multi-coupling activities, multi-segment and multi-ply overlapping planning models are built; by sorting overlapping processes and analyzing the construction of these planning models, two conclusions are obtained： （1） As to multi-segment and multi-ply overlapping of multi coupled activities, the total decrement of the task set development time is the sum of the time decrement caused by basic overlapping segments, and minus the sum of the time increment caused by multiple overlapping segments; （2） the total increment of development cost is the sum of the cost increment caused by all overlapping process. And then, based on overlapping degree analysis of these planning models, by the V1E degree function, the four lemmas theory proofs are represented, and two propositions are finally proved： （1） The multi-ply overlapping of the multi coupled activities will weaken the basic overlapping effect on the development cycle time reduction （2） Overlapping the multi coupled activities will decrease product development cycle, but increase product development cost. And there is trade-off between development time and cost. And so, two methods are given to slacken and eliminate multi-ply overlapping effects. At last, an example about a vehicle upper subsystem design illustrates the application of the proposed models; compared with a sequential execution pattern, the decreasing of development cycle （22%） and the increasing of development cost （3%） show the validity of the method in the example The proposed research not only lays a theoretical foundation for correctly planning complex product development process, but also provides specific and effective operation methods for overlapping multi coupled activities.

Structural damage detection method based on information fusion technique

Multi-source information fusion （MSIF） is imported into structural damage diagnosis methods to improve the validity of damage detection. After the introduction of the basic theory, the function model, classifications and mathematical methods of MSIF, a structural damage detection method based on MSIF is presented, which is to fuse two or more damage character vectors from different structural damage diagnosis methods on the character-level. In an experiment of concrete plates, modal information is measured and analyzed. The structural damage detection method based on MSIF is taken to localize cracks of concrete plates and it is proved to be effective. Results of damage detection by the method based on MSIF are compared with those from the modal strain energy method and the flexibility method. Damage, which can hardly be detected by using the single damage identification method, can be diagnosed by the damage detection method based on the character-level MSIF technique. Meanwhile multi-location damage can be identified by the method based on MSIF. This method is sensitive to structural damage and different mathematical methods for MSIF have different preconditions and applicabilities for diversified structures. How to choose mathematical methods for MSIF should be discussed in detail in health monitoring systems of actual structures.

Patent portfolio analysis model based on legal status information

Purpose:This research proposes a patent portfolio analysis model based on the legal status information to chart out a competitive landscape in a particular field,enabling organizations to position themselves within the overall technology landscape.Design/methodology/approach:Three indicators were selected for the proposed model:Patent grant rate,valid patents rate and patent maintenance period.The model uses legal status information to perform a qualitative evaluation of relative values of the individual patents,countries or regions’ technological capabilities and competitiveness of patent applicants.The results are visualized by a four-quadrant bubble chart To test the effectiveness of the model,it is used to present a competitive landscape in the lithium ion battery field.Findings:The model can be used to evaluate the values of the individual patents,highlight countries or regions’ positions in the field,and rank the competitiveness of patent applicants in the field.Research limitations:The model currently takes into consideration only three legal status indicators.It is actually feasible to introduce more indicators such as the reason for invalid patents and the distribution of patent maintenance time and associate them with those in the proposed model.Practical implications:Analysis of legal status information in combination of patent application information can help an organization to spot gaps in its patent claim coverage,as well as evaluate patent quality and maintenance situation of its granted patents.The study results can be used to support technology assessment,technology innovation and intellectual property management.Originality/value:Prior studies attempted to assess patent quality or competitiveness by using either single patent legal status indicator or comparative analysis of the impacts of each indicator.However,they are insufficient in presenting the combined effects of the evaluation indicators.Using our model,it appears possible to get a more complete and objective picture of the current competitive situation.

Information freshness optimization of multiple status update streams in Internet of things:Generation rate control and service rate reservation

The Internet of things(IoT)has become a key infrastructure providing up-to-date and fresh information for policy analysis and decision-making of upper-layer applications.However,there are limited sensing and communication resources in IoT devices,which significantly affects the timeliness and freshness of the updated status.This work proposes two schemes,namely,the generation rate control and service rate reservation schemes,to improve the overall information freshness of multiple status update streams at the receiver.Specifically,using the recently proposed Age of Information(AoI)as the metric for evaluating information freshness,we characterized the overall information freshness,i.e.,the overall average AoI at the receiver for both schemes,by considering the urgency difference of status update and streams.Both schemes for status updates and streams,respectively,were formulated as two optimization problems.We proved that both problems are convex and the optimal generation and service rates for different streams are found by the standard convex optimization algorithm.Moreover,we proposed both approximate optimal generation and approximate optimal service rate for fast deployment in heavy and light load cases.Numerical results verify the theoretical findings and accuracy of the proposed approximate solutions,guiding the design and deployment of IoT.

Enhancing train position perception through Al-driven multi-source information fusion

This paper addresses the challenge of accurately and timely determining the position of a train,with specific consideration given to the integration of the global navigation satellite system(GNSS)and inertial navigation system(INS).To overcome the increasing errors in the INS during interruptions in GNSS signals,as well as the uncertainty associated with process and measurement noise,a deep learning-based method for train positioning is proposed.This method combines convolutional neural networks(CNN),long short-term memory(LSTM),and the invariant extended Kalman filter(IEKF)to enhance the perception of train positions.It effectively handles GNSS signal interruptions and mitigates the impact of noise.Experimental evaluation and comparisons with existing approaches are provided to illustrate the effectiveness and robustness of the proposed method.

Integration Technique of Multi-source Information Dominated by Aerial Radiometric Measure-ment and Its Application

This paper aims at exploring a digital image integration technique for multi-geoscience in formation dominated by airborne gamma-ray data, especially deeply discussing the method to secondly develop those aerial data by combining digital image processing system with the colored mapping system. Utilizing this technique , we have analyzed the geologic environment of uranium mineralization of Lianshanguan area > Liaoning Province, provided some important background information for further seeking of minerals. Meanwhile , experimental studies have been made to predict uranium mineralization , and evident results aquired. Practise shows that this new technique offers prospecting significance for mineral seeking and great practical value in survey of uranium resources.

Bioinformatics screening of breast cancer-related genes and potential drug research

Objective:To search the the differentially expressed genes between breast cell carcinoma tissues and normal tissues by using bioinformatics technology,and the potential therapeutic drugs for breast cancer were identified,which can provide reference for clinical immune targeted therapy and drug therapy of breast cancer in the future.Methods:"Breast cancer"was searched by using Gene Expression Omnibus(GEO),and GSE79586 chip data was downloaded.The differentially expressed genes in the control group and the breast cancer model group were screened by using bio-communication technology and subjected to GO function analysis,KEGG pathway analysis,differential gene characteristic expression analysis and protein-protein interaction network(PPI)analysis,and the analysis results were further visualized.Prognosis analysis,related function prediction and immune infiltration analysis were performed using the GEPIA,GeneMANIA,and Timer2.0 databases,respectively.Finally,the compounds with potential therapeutic effects on breast cancer are identified through Connectivity Map(CMap).Western blotting and real-time PCR(RT-PCR)were used to verify the core genes and potential therapeutic agents with the highest correlation in vitro.Results:A total of 3916 differentially expressed genes including 1786 up-regulated genes and 2130 down-regulated genes were screened.GO analysis showed that the differential genes were mainly involved in the positive regulation of phosphorylation,secretory vesicles,racemase and epimerase activities.KEGG analysis showed that differential genes were involved in systemic lupus erythematosus,alcoholism,sticky spots,amoebic dysentery Ras signal pathways and other disease pathways.The characteristic expression analysis of differential genes showed that MEK inhibitors,HSP90 inhibitors and signal transduction pathway kinase inhibitors were drugs similar to the differential genes.PPI results showed that H2AFJ,TFF1,GATA3,FOXA1,and CDH1 were core genes related to breast cancer.Two core genes of H2AFJ and TFF1 with the highest correlation were further selected for GEPIA analysis.The results of the analysis showed that the mRNA expression levels of H2AFJ and TFF1 in breast cancer cells were significantly higher than those in normal tissues,and there was a significant correlation with the pathological staging,overall survival rate and disease-free survival rate of breast cancer patients.H2AFJ and TFF1 may be potential prognostic biomarkers for survival of breast cancer patients.The functions of differentially expressed H2AFJ and TFF1 are mainly related to hormone receptor binding,epithelial structure maintenance and epigenetic negative regulation of genes,chromatin tissue involved in negative regulation of transcription,etc.The results of immune infiltration showed that the expressions of H2AFJ and TFF1 had a significant correlation with the infiltration of macrophages,neutrophils,monocytes,CD4+T,CD8+T,B lymphocytes and other immune cells.CMap results showed that compounds such as Gefitinib,Alpelisib,Sorafenib,and Sunitinib had potential therapeutic effects on breast cancer.Western blot and RT-PCR results showed that H2AFJ and TFF1 were significantly overexpressed in breast cancer cells.Gefitinib significantly inhibited the expression of H2AFJ and TFF1 in breast cancer cells(P<0.05,P<0.01).Conclusion:In this study,differentially expressed genes between breast cell carcinoma tissues and normal tissues were screened out by bioinformatics means to further identify key genes and compounds with potential therapeutic effects in the onset process of breast cancer and to further verify the effectiveness of the screened drugs on breast cancer through experiments.It will provide reference for clinical research and development of new drugs against breast cancer in the future in order to develop more effective treatment options.

The Landscape of the Legal Aspects of Informed Consent for Medical Treatment in Papua New Guinea

The major challenges militating against the proper practice of informed consent identified in our desk top review are related to cultural issues of the people.Many patients continue to value strongly their kinship ties with the past and believe in the traditional methods of healing sickness.The patients must be given some information about what the doctor proposes to do.The underlying spirit of informed consent is new in PNG,and it strives against the traditional attitudes of doctors that they know it best for the patients and the strong culture in customs and patients’misconceptions that affect their perceptions in making important health care decisions.Majority of the patients have some notion of informed consent,however not many people understand what it is.They still hang onto their traditional customs,beliefs and opinions that affect their health care decisions;they often look to their village elders,family and parents for advice in times of sickness or death in the family but where the law implies consent it is not often obtained.Summarization of current laws,reports of legal cases,and personal experiences were examined.Special requirements must be prescribed when patients are subjected to medical treatment.Documentation of a well-defined process,not only on paper,may not only protect the medical doctor from exposure to liability but increases the patient’s autonomy in decisions concerning health and encourages compliance with treatment;and advances the interests of both patient and doctor.Lack of informed consent can reinforce a claim of medical malpractice,and could well undermine relevant health care policy to protect patient autonomy.

腰椎间盘突出症患者中医康复健康信息获取行为量表的编制及信效度检验

目的 编制腰椎间盘突出症患者中医康复健康信息获取行为量表,并检验其信效度。方法 以Wilson信息行为模型为指导,通过文献分析法、半结构访谈、2轮Delphi专家函询及预调查形成量表施测稿。对432例腰椎间盘突出症患者进行问卷调查,检验量表的信效度。结果 腰椎间盘突出症患者中医康复健康信息获取行为量表包括中医康复健康信息需求、中医康复健康信息来源和中医康复健康信息获取障碍3个维度,共30个条目,累计方差贡献率为64.912%。总量表的内容效度指数为0.969,Cronbach α系数为0.914,重测信度为0.836,分半信度为0.853,验证性因子分析结果显示该量表结构效度良好。结论 腰椎间盘突出症患者中医康复健康信息获取行为量表信效度良好,可作为评估腰椎间盘突出症患者中医康复健康信息获取行为水平的有效工具。

EORTC QLQ-INFO25中文版信效度在我国癌症患者中的验证

目的:验证EORTC QLQ-INFO25中文版在我国癌症患者中的信效度。方法:通过便利抽样法收集2023年6月至2024年4月广西2所三级甲等医院的360名癌症患者,采用EORTC QLQ-INFO25中文版对患者信息水平及满意度进行调查。通过Cronbach'sα系数和重测信度验证量表的信度、内容效度和结构效度验证量表的效度。结果:量表总的Cronbach'sα系数为0.919,重测信度为0.878。条目水平的内容效度值为0.833~1,量表水平的内容效度均值为0.960。探索性因子分析提取4个固定公因子的累计方差贡献率为67.532%,各条目在其维度上的因子载荷均>0.400,条目归属与原量表基本一致。验证性因子分析χ2/ν为2.904,RMR为0.066,RMSEA为0.093,GFI、NFI、IFI和CFI分别为0.848、0.838、0.887和0.886,模型拟合度良好。结论:中文版EORTC QLQ-INFO25具有良好的信效度,可用于评价我国癌症患者的信息水平及满意度。

面向能效服务的标准化信息模型及信息交互方法

为满足负荷聚合商、综合能源服务商、用户等参与主体之间互操作的需求,需要建立面向能效服务的标准化信息模型并设计相应的信息交互方法。基于此,归纳并整理能效服务的数据主题域;剖析现有公共信息模型标准中与能效服务业务相关的信息模型,提出“项目-服务-计量-数据”框架的标准化信息模型设计方法,构建面向能效服务的标准化信息模型,通过基于资源描述框架的信息模型验证和数据集成,实现了数据交互;设计了能效服务应用的信息集成架构,以建筑能耗计算分析工具BETTER为例进行了应用说明,验证了数据交互方法可以灵活、快速、按需地管理能效服务业务模型,同时探讨了建筑领域的建筑信息模型与面向能效服务的标准化信息模型之间的兼容性。

城镇排水管网GIS数据校核技术与软件开发

地理信息系统(GIS)是“智慧水务”建设中常见数据库,由于数据库建立、维护等过程的失误,实际的排水管网GIS存在各类异常,这影响了排水管网运行分析、模拟、管理决策的准确性与有效性,故有必要研究排水管网GIS校核技术,以保证GIS数据质量。研究提出了构造拓扑信息算法,总结了拓扑、文本属性、数值属性等常见异常形式,并在此基础上针对各类数据提出通用校核方法,开发了集成排水管网GIS拓扑与属性校核技术以及可输出校核结果报告的软件平台,并将其用于校核Z市污水管网GIS。结果表明,该市排水管网GIS存在24 336条异常记录,经过校核67%以上的异常得到改善,表明这些校核技术可有效地检测与修复各类GIS异常。

信息安全态度量表的汉化及信效度检验

目的汉化信息安全态度量表(Information Security Attitude Questionnaire,ISA-Q)并检验其信效度。方法使用Beaton跨文化调适指南对ISA-Q进行汉化,通过翻译、整合、回译、专家委员会审核、跨文化调适以及预调查形成中文版ISA-Q,对4所三甲医院959名护士进行问卷调查,以评价量表的信效度。结果探索性因子分析提取特征值>1的6个公因子(环境控制、维护设备稳定、信息访问限制、工作系统性、提升职业责任感和持续教育培训),累计方差贡献率为64.222%;验证性因子分析显示,χ^(2)/df=1.219,RMSEA=0.022,AGFI=0.921,GFI=0.934,NFI=0.939,CFI=0.988,TLI=0.987。量表条目水平的内容效度指数为0.846~1.000,量表水平的内容效度指数为0.986。量表的Cronbach′sα系数0.870,重测信度0.884,折半信度0.781。结论中文版ISA-Q信效度良好,可作为我国护士信息安全态度的评估工具。

基于熵的微阵列数据特征选择

针对基于熵的特征加权算法忽略了数据集内在特性对特征重要性的影响,导致特征选择效果不佳的问题,提出一种改进的基于熵的特征加权算法,根据信息熵计算特征维度的重要性权重,通过引入交叉验证实现不同数据集的阈值学习,确定用于度量特征重要性的最佳阈值参数,并基于该阈值对数据集进行特征选择。在微阵列数据集上的数值实验结果表明:相比于原算法,所提算法能够减少更多的维度,且特征子集用于分类得到的准确率与原算法基本持平甚至有所提高,说明改进的算法是可行和有效的。

压力性损伤患者非正式照顾者知信行量表的编制及信效度检验

目的:压力性损伤(pressure injury,PI)患者的非正式照顾者的认知及照顾行为水平暂缺科学性测量工具,本研究拟编制PI患者非正式照顾者知信行量表,并对其进行信效度检验。方法:以知信行理论为框架,结合相关文献与专家头脑风暴,拟定量表初始条目池;经过德尔菲专家函询、预调查,形成初始量表。于2023年4至10月便利选取中南大学湘雅三医院住院部及其合作社区、长沙红枫康复医院的PI患者非正式照顾者进行调查,第1次纳入186名、第2次纳入213名进行量表项目分析及信效度检验;间隔3周,随机抽取已调查的20名重测,检验量表的重测信度。结果:PI患者非正式照顾者知信行量表共3个维度,19个条目。总量表内部一致性克龙巴赫α系数为0.916,量表所有条目水平的内容效度指数(item level-content validity index,I-CVI)为0.826~1.000,平均量表水平的内容效度指数(scale level-content validity index/average,S-CVI/Ave)为0.94。探索性因子分析提取3个公因子,累计方差贡献率为64.643%。验证性因子分析显示χ~2/df=2.54,近似误差的均方根(root mean square error of approximation,RMSEA)=0.085,比较拟合指数(comparative fit index,CFI)=0.920,标准化均方根残差(standardized root mean square residual,SRMR)=0.059,模型与数据整体适配良好。结论:PI患者非正式照顾者知信行量表具有较好的信度和效度,可用于非正式照顾者PI知信行水平的评估。

患者报告结局测量系统:儿童报告版与父母代报告版儿童整体健康量表的汉化及测量学检验

目的 汉化儿童报告版与父母代报告版儿童整体健康量表(PGH7+2),并进行测量学检验,旨在丰富患者报告结局测量系统。方法 采用国际标准的慢性病治疗功能评价翻译方法,对原量表进行翻译、回译、文化调适及预调查,选取104名5~7岁的儿童父母和123名8岁及以上的儿童,分别检验2个量表的信效度,并对测量数据进行Rasch模型分析。结果 儿童报告版PGH7+2的粗分总分为(35.46±5.00)分,总体Cronbach's α系数为0.863;父母代报告版PGH7+2粗分总分为(36.71±4.44)分;总体Cronbach's α系数为0.843。验证性因子分析结果显示2个量表的模型适配度均良好;标准化残差相关系数均<0.7;项目特征曲线均单调性递增,可行Rasch模型分析。儿童报告版量表的Rasch模型可以解释总变异的81.7%,父母代报告版量表模型可解释总变异的83.9%,但其首成分标准化残差特征根值均为2.7(>2.1),提示量表具有多维性可能。结论 汉化的儿童报告版与父母代报告版PGH7+2整体均具有良好的信度和效度,可用于评价儿童的整体健康状况,但部分条目还有待进一步文化调适。

MBI量表应用于老年脑卒中康复期病人非正式照顾者的信效度检验

目的:检验MBI量表在老年脑卒中康复期病人非正式照顾者中应用的信度和效度。方法:采用一般资料调查表、MBI量表、感知社会支持量表对新疆某2所三级甲等医院收治的284名老年脑卒中康复期病人非正式照顾者进行面对面调查,以检验量表的信度和效度。结果:信度分析结果显示,总量表Cronbach′sα系数为0.922,各维度的Cronbach′sα系数分别为0.918,0.755,0.848。总量表折半信度系数为0.759,各维度的折半信度系数分别为0.888,0.757,0.806。以感知社会支持量表作为检验MBI量表的校标效度,相关分析结果显示,MBI量表情绪衰竭维度与感知社会支持呈负相关(r=-0.516,P<0.01)、去人格化维度与感知社会支持呈负相关(r=-0.495,P<0.01)、个人成就感维度与感知社会支持呈正相关(r=0.532,P<0.01);验证性因子分析结果显示,各项因子载荷系数均>0.5,三因子模型的结构方程模型拟合度良好(χ2/ν=2.293,近似误差均方根=0.068,比较拟合指数=0.917,递增拟合指数=0.918,Tucker-Lewis指数=0.906)。结论:MBI量表具有良好的信效度,可用于测量老年脑卒中康复期病人非正式照顾者的倦怠水平。