Energy Cost Minimization Using String Matching Algorithm in Geo-Distributed Data Centers

Data centers are being distributed worldwide by cloud service providers(CSPs)to save energy costs through efficient workload alloca-tion strategies.Many CSPs are challenged by the significant rise in user demands due to their extensive energy consumption during workload pro-cessing.Numerous research studies have examined distinct operating cost mitigation techniques for geo-distributed data centers(DCs).However,oper-ating cost savings during workload processing,which also considers string-matching techniques in geo-distributed DCs,remains unexplored.In this research,we propose a novel string matching-based geographical load balanc-ing(SMGLB)technique to mitigate the operating cost of the geo-distributed DC.The primary goal of this study is to use a string-matching algorithm(i.e.,Boyer Moore)to compare the contents of incoming workloads to those of documents that have already been processed in a data center.A successful match prevents the global load balancer from sending the user’s request to a data center for processing and displaying the results of the previously processed workload to the user to save energy.On the contrary,if no match can be discovered,the global load balancer will allocate the incoming workload to a specific DC for processing considering variable energy prices,the number of active servers,on-site green energy,and traces of incoming workload.The results of numerical evaluations show that the SMGLB can minimize the operating expenses of the geo-distributed data centers more than the existing workload distribution techniques.

A Mathematical Solution to String Matching for Big Data Linking

This paper describes how data records can be matched across large datasets using a technique called the Identity Correlation Approach （ICA）. The ICA technique is then compared with a string matching exercise. Both the string matching exercise and the ICA technique were employed for a big data project carried out by the CSO. The project was called the SESADP （Structure of Earnings Survey Administrative Data Project） and involved linking the Irish Census dataset 2011 to a large Public Sector Dataset. The ICA technique provides a mathematical tool to link the datasets and the matching rate for an exact match can be calculated before the matching process begins. Based on the number of variables and the size of the population, the matching rate is calculated in the ICA approach from the MRUI （Matching Rate for Unique Identifier） formula, and false positives are eliminated. No string matching is used in the ICA, therefore names are not required on the dataset, making the data more secure ＆ ensuring confidentiality. The SESADP Project was highly successful using the ICA technique. A comparison of the results using a string matching exercise for the SESADP and the ICA are discussed here.

Identification of reservoir types in deep carbonates based on mixedkernel machine learning using geophysical logging data

Identification of reservoir types in deep carbonates has always been a great challenge due to complex logging responses caused by the heterogeneous scale and distribution of storage spaces.Traditional cross-plot analysis and empirical formula methods for identifying reservoir types using geophysical logging data have high uncertainty and low efficiency,which cannot accurately reflect the nonlinear relationship between reservoir types and logging data.Recently,the kernel Fisher discriminant analysis(KFD),a kernel-based machine learning technique,attracts attention in many fields because of its strong nonlinear processing ability.However,the overall performance of KFD model may be limited as a single kernel function cannot simultaneously extrapolate and interpolate well,especially for highly complex data cases.To address this issue,in this study,a mixed kernel Fisher discriminant analysis(MKFD)model was established and applied to identify reservoir types of the deep Sinian carbonates in central Sichuan Basin,China.The MKFD model was trained and tested with 453 datasets from 7 coring wells,utilizing GR,CAL,DEN,AC,CNL and RT logs as input variables.The particle swarm optimization(PSO)was adopted for hyper-parameter optimization of MKFD model.To evaluate the model performance,prediction results of MKFD were compared with those of basic-kernel based KFD,RF and SVM models.Subsequently,the built MKFD model was applied in a blind well test,and a variable importance analysis was conducted.The comparison and blind test results demonstrated that MKFD outperformed traditional KFD,RF and SVM in the identification of reservoir types,which provided higher accuracy and stronger generalization.The MKFD can therefore be a reliable method for identifying reservoir types of deep carbonates.

浅析String类中“= =”和equals的应用

String是编程中常用到的数据类型,Java中的String类是一种引用数据类型。它将从String类对象的两种创建方式入手,分析字符串作比较时"= ="和equals( )的具体应用。

Classification of vegetative types in Changbai Mountain based on optical and microwave remote sensing data

Highly accurate vegetative type distribution information is of great significance for forestry resource monitoring and management.In order to improve the classification accuracy of forest types,Sentinel-1 and 2 data of Changbai Mountain protection development zone were selected,and combined with DEM to construct a multi-featured random forest type classification model incorporating fusing intensity,texture,spectral,vegetation index and topography information and using random forest Gini index(GI)for optimization.The overall accuracy of classification was 94.60%and the Kappa coefficient was 0.933.Comparing the classification results before and after feature optimization,it shows that feature optimization has a greater impact on the classification accuracy.Comparing the classification results of random forest,maximum likelihood method and CART decision tree under the same conditions,it shows that the random forest has a higher performance and can be applied to forestry research work such as forest resource survey and monitoring.

A systematic study of Erzhu Erchen decoction against damp-heat internalized type 2 diabetes based on data mining and experimental verification

Background:Erzhu Erchen decoction(EZECD),which is based on Erchen decoction and enhanced with Atractylodes lancea and Atractylodes macrocephala,is widely used for the treatment of dampness and heat(The clinical manifestations of Western medicine include thirst,inability to drink more,diarrhea,yellow urine,red tongue,et al.)internalized disease.Nevertheless,the mechanism of EZECD on damp-heat internalized Type 2 diabetes(T2D)remains unknown.We employed data mining,pharmacology databases and experimental verification to study how EZECD treats damp-heat internalized T2D.Methods:The main compounds or genes of EZECD and damp-heat internalized T2D were obtained from the pharmacology databases.Succeeding,the overlapped targets of EZECD and damp-heat internalized T2D were performed by the Gene Ontology,kyoto encyclopedia of genes and genomes analysis.And the compound-disease targets-pathway network were constructed to obtain the hub compound.Moreover,the hub genes and core related pathways were mined with weighted gene co-expression network analysis based on Gene Expression Omnibus database,the capability of hub compound and genes was valid in AutoDock 1.5.7.Furthermore,and violin plot and gene set enrichment analysis were performed to explore the role of hub genes in damp-heat internalized T2D.Finally,the interactions of hub compound and genes were explored using Comparative Toxicogenomics Database and quantitative polymerase chain reaction.Results:First,herb-compounds-genes-disease network illustrated that the hub compound of EZECD for damp-heat internalized T2D could be quercetin.Consistently,the hub genes were CASP8,CCL2,and AHR according to weighted gene co-expression network analysis.Molecular docking showed that quercetin could bind with the hub genes.Further,gene set enrichment analysis and Gene Ontology represented that CASP8,or CCL2,is negatively involved in insulin secretion response to the TNF or lipopolysaccharide process,and AHR or CCL2 positively regulated lipid and atherosclerosis,and/or including NOD-like receptor signaling pathway,and TNF signaling pathway.Ultimately,the quantitative polymerase chain reaction and western blotting analysis showed that quercetin could down-regulated the mRNA and protein experssion of CASP8,CCL2,and AHR.It was consistent with the results in Comparative Toxicogenomics Database databases.Conclusion:These results demonstrated quercetin could inhibit the expression of CASP8,CCL2,AHR in damp-heat internalized T2D,which improves insulin secretion and inhibits lipid and atherosclerosis,as well as/or including NOD-like receptor signaling pathway,and TNF signaling pathway,suggesting that EZECD may be more effective to treat damp-heat internalized T2D.

基于K-prototypes的混合属性数据聚类算法改进

属性数据分为数值型数据和分类型数据,一般情况下对于数值型数据运算前要进行标准化处理,但是对于数值型数据差异大的数据,由于大数掩盖小数的影响,按照K-prototypes聚类算法,数值型数据标准化后而且不对相应的分类数据有任何预处理或者在计算时没有进行任何改变,很可能提高分类数据在聚类中的影响,并且分类型数据并未进一步地细分,不能满足不同要求的混合属性聚类。该文在将数值型数据标准化的基础上,将分类数据细分为二元数据和类型数据,并用相异度系数距离计算分类数据之间的距离,并且赋予二元和类型数据相应的权重,来改进K-prototypes聚类算法,使该算法满足不同要求的混合属性数据聚类,最后通过C#语言,在ArcEngine2010版本上实现。

Inter-hour direct normal irradiance forecast with multiple data types and time-series

Boosted by a strong solar power market,the electricity grid is exposed to risk under an increasing share of fluctuant solar power.To increase the stability of the electricity grid,an accurate solar power forecast is needed to evaluate such fluctuations.In terms of forecast,solar irradiance is the key factor of solar power generation,which is affected by atmospheric conditions,including surface meteorological variables and column integrated variables.These variables involve multiple numerical timeseries and images.However,few studies have focused on the processing method of multiple data types in an interhour direct normal irradiance(DNI)forecast.In this study,a framework for predicting the DNI for a 10-min time horizon was developed,which included the nondimensionalization of multiple data types and time-series,development of a forecast model,and transformation of the outputs.Several atmospheric variables were considered in the forecast framework,including the historical DNI,wind speed and direction,relative humidity time-series,and ground-based cloud images.Experiments were conducted to evaluate the performance of the forecast framework.The experimental results demonstrate that the proposed method performs well with a normalized mean bias error of 0.41%and a normalized root mean square error(n RMSE)of20.53%,and outperforms the persistent model with an improvement of 34%in the nRMSE.

Engineering DNA Materials for Sustainable Data Storage Using a DNA Movable-Type System

DNA molecules are green materials with great potential for high-density and long-term data storage.However,the current data-writing process of DNA data storage via DNA synthesis suffers from high costs and the production of hazards,limiting its practical applications.Here,we developed a DNA movable-type storage system that can utilize DNA fragments pre-produced by cell factories for data writing.In this system,these pre-generated DNA fragments,referred to herein as“DNA movable types,”are used as basic writing units in a repetitive way.The process of data writing is achieved by the rapid assembly of these DNA movable types,thereby avoiding the costly and environmentally hazardous process of de novo DNA synthesis.With this system,we successfully encoded 24 bytes of digital information in DNA and read it back accurately by means of high-throughput sequencing and decoding,thereby demonstrating the feasibility of this system.Through its repetitive usage and biological assembly of DNA movable-type fragments,this system exhibits excellent potential for writing cost reduction,opening up a novel route toward an economical and sustainable digital data-storage technology.

Exploring medication rules in Chinese randomized controlled trials of type 2 diabetes based on data mining technology

Background:To systematically summarize and categorize the Chinese herbal medicine in the domestic traditional Chinese medicine(TCM)literature on type 2 diabetes mellitus(T2DM),in this paper,we mine traditional Chinese medicine data for relationships and provide for future practitioners and researchers.Methods:Taking randomized controlled trials on the treatment of T2DM in TCM as the research theme,we searched for full-text literature in three major clinical databases,including CNKI,Wan Fang,and VIP,published between 1990 and 2020.We then conducted frequency statistics,cluster analysis,association rules extraction,and principal component analysis based on a corpus of medical academic words extracted from 1116 research articles.Results:The most frequently used is Astragali Radix,and the most commonly used two-herb combination in T2DM treatment consisted of Coptidis Rhizoma and Moutan Cortex.Moutan Cortex,Alismatis Rhizoma,and Dioscoreae Rhizoma were the most frequently used three-herb combination.We found a“lung”and“liver”and“kidney”model and confirmed the value of classical meridian tropism theory and pattern identification.The treatment is mainly to fill deficiency and clear heat and consider water infiltration,dampness,blood circulation,and silt.Conclusion:This study provides an in-depth perspective on the TCM medication rules for T2DM and offers practitioners and researchers valuable information about the current status and frontier trends of TCM research on T2DM in terms of diagnosis and treatment.

面向不同类型概念漂移的两阶段自适应集成学习方法

大数据时代,流数据大量涌现.概念漂移作为流数据挖掘中最典型且困难的问题,受到了越来越广泛的关注.集成学习是处理流数据中概念漂移的常用方法,然而在漂移发生后,学习模型往往无法对流数据的分布变化做出及时响应,且不能有效处理不同类型概念漂移,导致模型泛化性能下降.针对这个问题,提出一种面向不同类型概念漂移的两阶段自适应集成学习方法(two-stage adaptive ensemble learning method for different types of concept drift,TAEL).该方法首先通过检测漂移跨度来判断概念漂移类型,然后根据不同漂移类型,提出“过滤-扩充”两阶段样本处理机制动态选择合适的样本处理策略.具体地,在过滤阶段,针对不同漂移类型,创建不同的非关键样本过滤器,提取历史样本块中的关键样本,使历史数据分布更接近最新数据分布,提高基学习器有效性;在扩充阶段,提出一种分块优先抽样方法,针对不同漂移类型设置合适的抽取规模,并根据历史关键样本所属类别在当前样本块上的规模占比设置抽样优先级,再由抽样优先级确定抽样概率,依据抽样概率从历史关键样本块中抽取关键样本子集扩充当前样本块,缓解样本扩充后的类别不平衡现象,解决当前基学习器欠拟合问题的同时增强其稳定性.实验结果表明,所提方法能够对不同类型的概念漂移做出及时响应,加快漂移发生后在线集成模型的收敛速度,提高模型的整体泛化性能.

基于数据建模的浙江省稀有血型信息共享系统构建与应用

目的建立省级稀有血型库信息化管理平台,实现稀有血型的种类、献血者资料、血液库存等信息的互联互通和共享,提升稀有血型血液保障能力。方法采用鱼骨图,分析我省稀有血型管理在血站内部各业务环节以及血站间存在的信息壁垒。基于全省统一的采供血业务系统和血液云平台,引入数据建模技术,通过需求分析和模型设计,构建稀有血型献血者、稀有血型血液数据库视图,并开发统一的查询平台,实现稀有血型信息的共享。结果系统对稀有血型信息进行了集成、检索和展示,实现在血站内部各业务环节、血站间信息的互联互通。截止目前,除RhD阴性血型外,已入库的红细胞稀有血型种类8种,覆盖7个血型系统,稀有血型献血者289人,稀有血液血液实体冻存红细胞216单位。结论基于数据建模开发的信息系统,提高了系统整体效率和协同性,同时也降低了系统开发运维的工作量,为数据的标准化管理和应用发展提供技术借鉴,也为建立健全全国性的稀有血型信息网络提供模式参考和示范应用。

SOTEM野外数据采集中的关键参数分析

电性源短偏移距瞬变电磁法(SOTEM)在野外数据采集中涉及诸多参数,它们的选择与实测数据的信号质量、探测灵敏度等密切相关。本文依据中国地球物理学会团体标准《电性源短偏移距瞬变电磁法勘探技术规程》(T/CGS 002—2021)中的相关规定,并结合一些数值模拟和实际案例,对发射源长度、发射基频、偏移距、装置类型、观测分量等关键参数的选取依据进行了分析与阐述,获得的相关认识对指导SOTEM野外施工、发挥SOTEM探测性能具有重要意义。

基于文献挖掘和网络药理学探析阴虚型免疫性血小板减少症用药规律与作用机制

目的利用数据挖掘技术及网络药理学探讨中药治疗阴虚型免疫性血小板减少症用药规律与潜在作用机制。方法运用中国知网、万方数据库检索中药治疗阴虚型免疫性血小板减少症相关文献,通过中医传承辅助平台V2.5软件分析组方规律,确定常用药对;运用中药系统药理学数据库与分析平台(TCMSP)、本草组鉴数据库、GeneCards数据库获取关键基因和主要靶点信息,利用STRING数据库构建交集靶点蛋白相互作用网络,采用Cytoscape 3.7.1软件中的CytoHubba和MCODE插件筛选出关键靶点信息,采用METASCAPE数据库基因本体(GO)功能富集分析和京都基因与基因组百科全书(KEGG)通路富集分析。结果共筛选得到129篇有效文献,共计130个中药处方,通过关联规则筛选出“生地黄—牡丹皮—旱莲草”为最佳中药复方,HSP90AA1、STAT3、VEGFA、MAPK3等为关键靶点,槲皮素、山柰酚、金合欢素与木犀草素等为主要有效成分,其主要作用机制可能是调控癌症通路、Th17细胞分化及核转录因子κB(NF-κB)信号通路等。结论治疗阴虚型免疫性血小板减少症应抓住止血、凉血、行血三要素,初步揭示常用复方“生地黄—牡丹皮—旱莲草”通过多成分—多靶点—多通路发挥治疗作用,为今后研究提供理论依据。

倪青教授诊治糖尿病的临证经验及用药规律研究

目的通过数据挖掘研究倪青教授治疗糖尿病(Diabetes mellitus,DM)的用药规律及核心处方,探讨倪青教授诊治DM早期的临证经验及学术思想。方法通过临床医案采集,结合软件的分析系统,采用频次统计法,统计得出治法出现频次及中药性味归经频次;采用关联规则分析法,统计得出处方中药物组合使用频次及药物之间的关联规则,采用Cytoscape 3.7.1进行可视化展示。结果通过对99例DM早期患者进行分析,高频药物有茯苓,泽泻,甘草,猪苓,佩兰,藿香等;药物组合有“猪苓,茯苓”“猪苓,泽泻”“藿香,佩兰”“厚朴,半夏”“炒白术,茯苓”“车前子,川牛膝”“太子参,茯苓”“冬瓜仁,冬瓜皮”“泽兰,泽泻”“生黄芪,太子参”“檀香,丹参,砂仁”等。结论倪青教授辨证治疗DM早期重视病证结合,从湿、虚、瘀、浊着手,治以健脾祛湿,疏瘀化浊。

基于邻域优势粗糙集的区分度动态属性约简算法

为解决动态环境下数值型偏序关系数据的属性约简问题,利用优势粗糙集的区分度提出一种增量式属性约简算法。在数值型信息系统环境下,定义邻域优势区分度度量,通过邻域优势区分度设出一种非增量式属性约简算法;研究和分析对象变化场景下邻域优势区分度进行增量式更新的原理;分别提出数据对象增加和减少情形下数据集属性约简的增量式更新算法。在多个UCI数据集上进行实验验证,实验结果表明,该增量式算法能够有效完成动态数据的属性约简任务。

为形成新型生产关系提供数据法治保障

随着大数据、人工智能等数字技术的飞速发展与广泛应用,数据已成为推动经济社会发展的重要驱动力。数据不仅成为一种新型生产要素,而且还引发传统生产模式和生产主体关系发生变革,因此,必须形成与之相适应的新型生产关系。在此背景下,形成新型生产关系需要遵循数据法治逻辑,建立明晰的数据产权制度、合理的数据流通制度和公正的数据权益分配制度。为此,有必要制定综合性的数据法律规范、建立跨部门协同的数据治理机制、推动数据技术的负责任创新和构建数字化的权利救济机制。

基于SKNet注意力机制的飞机类型识别算法

飞机类型识别是细粒度图像分类的一种,重点在于设计神经网络模型使其能够分辨各飞机种类中细微而具有区分性的特征.针对当前飞机识别任务中飞机种类众多、类间差异细微、类内差异显著等问题,提出了一种基于改进SKNet注意力与数据增广的飞机类型识别算法.以ResNeXt101网络作为基础网络,改进CBAM注意力提出并行的通道-空间注意力PCSA并嵌入可选择卷积模块的不同分支,得到PCSA-SK注意力,将其嵌入基础网络以进一步融合基础网络提取的深层特征并为其分配权重.根据目标激活图中具有判别性信息的区域,在原图像上对判别性区域裁剪并加入训练集,实现数据增广.实验结果表明:该算法在FGVC-Aircraft数据集上取得了93.57%的识别准确率,优于其他飞机识别算法.

早发型2型糖尿病患者子女颈动脉全息血管硬度及影响因素分析

目的评估早发型2型糖尿病患者子女颈动脉弹性功能,并分析影响其弹性的因素。资料与方法前瞻性分析2020年10月—2021年8月河南省人民医院2型糖尿病患者子女63例,根据其父母糖尿病发病年龄(<40岁为早发型,≥40岁为晚发型)分为父母早发糖尿病子女组(早发组,32例)和父母晚发糖尿病子女组(晚发组,31例)。同期选择32例年龄、性别及体重指数相匹配的健康志愿者作为对照组。应用全息血管内-中膜厚度测量和全息血管硬度分析技术测量受试者颈总动脉内-中膜厚度、血管壁位移、血管收缩期管径、硬度指数和脉搏波传导速度,比较上述参数的组间差异。结果早发组颈动脉内-中膜厚度、脉搏波传导速度、硬度指数高于晚发组及对照组(t=0.054~1.228,P均<0.05),血管壁位移低于晚发组及对照组(t=0.048、0.109,P<0.05)。结论2型糖尿病患者子女颈动脉僵硬度明显高于正常对照组,且早发组的颈动脉弹性下降较晚发组更明显。

基于数据挖掘的大型邮轮船型特征及船型参数分析

以确定大型邮轮的最优船型参数,提升邮轮的航行性能为目标,提出基于数据挖掘的大型邮轮船型特征及船型参数分析方法。利用模糊C均值聚类算法,聚类大型邮轮船型数据,挖掘大型邮轮船型特征。基于粗糙集理论,对大型邮轮船型特征挖掘结果约简处理,约简后船型特征对应的参数,作为船型性能优化的船型参数。利用自由变形方法,构建大型邮轮船型变换模型。依据船型参数变化,调整模型控制体的控制顶点,变化大型邮轮形状。利用回归分析方法,拟合船型变换模型中的船型参数,确定大型邮轮的最佳船型参数。实验结果表明,该方法可以精准挖掘大型邮轮船型特征,确定最佳船型参数。最佳船型参数下的邮轮阻力下降了1.48%,提升了大型邮轮的航行性能。