Structure

结构专业常用英语词汇1. 一般术语房屋建筑工程building engineering 土木工程civil engineering 建构筑物construction works 结构structure基础foundation地基ground; foundation soils 木结构timber structure 砌体结构masonry structure 钢结构steel structure 混凝土结构concrete structure 特种结构special engineering structure房屋建筑building工业建筑industrial building 民用建筑civil building水工建筑物marine structure 剪力墙结构shear wall structure 混合结构mixed structure 板柱结构slab-column system 框架结构frame structure 壳结构shell structure 拱结构arch strucuture桁架结构truss网架结构space grid structure 悬索结构cable-suspended structure 框架-剪力墙结构frame-shear wallstructure筒体结构tube structure 高耸结构high-rise structure 斜拉桥cable stayed bridge 悬索桥suspension bridge 桁架桥trussed bridge 拱桥arch bridge 引桥approach span 桥墩pier 隧道tunnel 混凝土坝concrete dam 渠道channel2. 结构构件构件member 部件component; assembly parts 梁beam截面section 板slab; plate 柱column 墙wall 壳shell 桁架truss框架frame刚架rigid frame排架bent frame简支梁 simply supported beam 悬臂梁cantilever beam 连续梁continuous beam 叠合梁superposed beam 伸缩缝expansion and contraction joint 截水沟(天沟)catch ditch;intercepting channel 排水沟 drainage ditch 护坡slope protection;revetment 挡土墙retaining wall 止水sealing; seal;节点joint桩pile沉降缝settlement joint 防震缝aseismic joint施工缝construction joint基础梁foundation beam桩承台pile cap; bearing platform底板 bottom slab水池顶板basin top slab现浇墙板cast-in-place wall panel预制墙板pre-cast wall panel钢筋混凝土框架reinforced concreteframe钢筋混凝土柱reinforced concretecolumn先张法预应力混凝土管桩pretensioned spunconcrete piles钢筋混凝土现浇板cast-in-placereinforced concreteslab斜梁 stringer平台梁 beams of platform女儿墙 parapet人孔盖板 manhole cover集水坑catchment pit溢流槽 flood relief channel 爬梯 ladder牛腿 bracket仪表槽盒支架instrument trench support电缆支架cable trench support 支撑brace支架support3. 结构材料预埋件embedded plate钢板steel plate钢圆环 steel ring plate雨水篦子板 rain water grating 钢筋reinforcing bar I 级 grade I热轧光圆钢筋hot rolled plainsteel bars热轧带肋钢筋hot rolled ribbedsteel bars插筋anchor rebar钢筋锚固长度bond length of rebars钢筋搭接bar splicing箍筋stirrup纵向钢筋longitudinal bar弯起钢筋bent-up bar钢筋间距rod spacing;bar spacing搭接长度overlapping length胀锚螺栓 expansion bolts螺母nut锚栓anchor bolt地脚螺栓foundation boltM30六角螺帽M30 six angle screw cap外露100，丝扣80outcrop 100mm，thread 80mm 垫片 washer C40混凝土C40 concrete: class 40早强剂early-strength admixture细石混凝土fine aggregate concrete防冻附加剂antifreeze admixture配合比mixture ratio抗渗等级P8 seepage class:抗压强度compression strength混凝土保护层厚度minimum concrete cover普通硅酸盐水泥ordinary portland cement 砂sand卵石pebble碎石crushed stone砾石gravel骨料aggregate水泥cement粘土砖clay brick 垫层cushion4. 地基基础扩展基础spread foundation地基ground,foundation soils 刚性基础rigid foundation 独立基础single footing 联合基础combined footing 条形基础strip founcation 壳体基础shell foundation 箱形基础box foundation 筏形基础raft foundation 桩基础pile foundation沉井基础open caisson foundation 大直径桩基础cylinder pile foundation 土岩组合地基soil-rock composite ground地基允许变形值allowable subsoil deformation地基处理ground treatment 复合地基composite foundation 强夯dynamic compaction 支档结构retaining structure 基坑工程excavation engineering 高应变动力检测high strain dynamictesting of piles 单桩竖向抗压静载试验vertical compression bearing capacity static test for single pile 单桩水平静载试验lateral bearing capacitystatic test for single pile 单桩竖向抗拔静载试验vertical up-lifting bearing capacitystatic test for single pile 单桩竖向抗压极限承载力vertical ultimatecompress bearing capacity of a singlepile 桩侧摩阻力skin friction resistance 桩顶水平位移lateral displacement 桩端阻力end bearing resistance 单桩静载试验static test of single pile 单桩水平极限承载力ultimate lateral bearing capacity for a single pile 单桩竖向抗拔极限承载力ultimate vertical up-lifting bearing capacityfor a single pile 素填土 Plain Fill 粉土 Silt 粉质粘土 Silty Clay 淤泥质粉质粘土Sludgy Silty Clay 粉砂Silty Sand 高压缩性 high compressibility 中粗砂medium sand 中等压缩性 medium compressibility 中细砂fine sand 低压缩性 low compressibility 可塑~硬塑状态 plastic-hard plastic 泥质粉砂岩 Muddy Silty stone 流塑状态 flow plastic 粉砂质泥岩 Silty mudstone 局部软塑状态 partly soft plastic 泥岩 Mudstone 饱和saturated 强风化 high weathered 松散incompact 中等风化 medium weathered 轻微裂缝 slight defective pile 断桩 breakage of pile 桩身完整 pile of body integrity 缩颈 pile diameter reduction 缺陷 defect 压缩系数coefficient of compressibility 渗流seepage flow 内聚力（粘聚力） cohesion5. 钢结构钢结构 steel structure底漆 paint primer涂层 coating中间漆intermediate painting 油漆，涂料 paint面漆 finish coat流坠 hanging透底 disclosure皱皮 wrinkle漏涂 miss painting返锈 rust again漆膜厚度 film thickness结构构件 structural member垂直支撑vertical bracing桁架 truss小立柱 postWA325钢格栅板 WA325 grating斜梁 stringer踏步板 treads钢梯 steel stairway踏步钢格板 grating for stair tread斜梯栏杆 guard rails不锈钢栏杆 stainless steel handrail预制场地 prefabrication area 平台栏杆 GR of platform原材料 raw material半成品 semi-finished goods除锈 remove rust, cleaning 切割 cutting喷砂 sand blasting打磨坡口 grinding型钢 section steel热轧工字钢hot-rolled I-beam steel热轧钢板 hot-rolled plates热轧不等边角钢hot-rolled unequal-legangle steel热轧等边角钢hot-rolled equal-legangle steel热轧H型钢hot-rolled H steel热轧槽钢hot-rolled channel steel 无缝钢管 seamless steel pipes扁钢 strap steel不锈钢板 stainless steel plate垂直度 verticality分片组装be assembled by sections现场成框 framed at site焊缝 weld seam V型坡口V-type bevel焊缝高度 weld height焊枪 welding gun焊缝长度 weld length焊机 welding machine气焊 gas welding 焊炬 welding torch电焊 electric welding 自动和半自动电弧焊automatic andsemi-automatic welding 手工焊 hand welding对焊 butt welding 焊条weld rod, electrode 角焊缝 fillet 间断焊接 gap welding 咬边 undercut 针孔 pinhole 夹渣 slag裂纹 crack烧穿 burning out 漏焊 losing welding 弧坑 concavity 未焊透lack of fusion 打磨 grind火焰切割 torch cutting 喷砂除锈The sand blasting 机械钻孔drill by machine预拉力 prestressed 高强度大六角头螺栓Set of big hexagonalhigh-strength bolt 抗滑移系数Slide coefficient of faying surface 扭矩系数torsional moment coefficient终拧扭矩final torsional moment6. 荷载、强度、可靠度剪力shear 剪切变形shear deformation压力pressure 延伸率percentage of elongation 剪切模量shear modulus 拉力tension 应力stress 应变strain应力集中concentration of stress 应力松弛stress relaxation 应力图stress diagram 应力状态state of stress位移 displacement 弹性变形 elastic deformation 变形 deformation 塑性变形 plastic deformation 应变 strain 剪应变 shear strain 线应变 linear strain 主应变 principal strain 轴向力 normal force 主应力 principal stress 正应力 normal stress 预应力prestress应力应变曲线stress-strain curve 裂缝crack抗压强度compressive strength 抗弯强度bending strength 抗扭强度torsional strength 抗拉强度tensile strength 屈服yield屈服点yield point屈服荷载yield load 屈服极限limit of yielding 屈服强度yield strength 屈服强度下限lower limit of yield 荷载load横截面section, cross 承载力bearing capacity 承重结构bearing structure 弹性模量elastic modulus 塑性plasticity延性ductileity受弯构件member in bending轴向拉力axial tension受拉区tensile region可靠性reliability受压区compressive region粘结力cohesive force配筋率reinforcement ratio偏心受拉eccentric tension配箍率stirrup ratio偏心受压eccentric compression泊松比Poisson’s ratio偏心距eccentric distance跨度span疲劳强度fatigue strength跨高比span-to-depth ratio偏心荷载eccentric load设计限值 limiting design value截面宽度 breadth of section截面高度 height of section截面厚度 thickness of section截面直径 diameter of section截面面积 area of section截面周长 perimeter of section截面模量（抵抗矩） section modulus截面惯性矩 second moment of area截面极惯性矩polar second moment of area 截面回转半径 radius of gyration长细比 slenderness ratio偏心矩 eccentricity偏心率 relative ecdentricity振动 vibration自振频率 natural frequency加速度 acceleration振周期natural period of vibration 频率 frequency振幅 amplitude of vibration自由度 degree of freedom振型 mode of vibration阻尼 damp共振 resonance强迫振动 forced vibration设计荷载design load挠度deflection设计强度design strength构造construction脆性破坏brittle failure延性破坏ductile failure可靠性 reliability适用性 serviceability安全性 safety耐久性 durability设计基准期 design reference period可靠概率 probability of survival可靠指标 reliability index失效概率 probability of failure概率设计法 probabilistic method极限状态设计法 limit states method容许应力设计法 permissible stressesmethod正常使用极限状态serviceability limitstates承载能力极限状态 ultimate limit states极限状态 limit states分项系数 partial safety factor体分布力 force per unit volume 作用 action力矩 moment of force线分布力 force per unit length永久作用 permanent action面分布力 force per unit area可变作用 variable action偶然作用 accidental action静态作用 static action固定作用 fixed adtion动态作用 dynamic action自重 self weight温度作用 temperature action施工荷载 site load地震作用 earthquake action土压力 earth pressure水压力 water pressure风荷载 wind load雪荷载 snow load风振 wind vibration吊车荷载crane load屋面活荷载floor live load浮力 buoyance浪压力（波浪力） wave pressure泥沙压力 silt pressute冰压力 ice pressure冻胀力 frost heave force静水压强 hydro-static pressure静水总压力total hydro-static pressure 动水压强 hydro-dynamic pressure压力水头 pressure head作用代表值 representative valueof an action作用准永久值quasi-permanent valueof an action作用标准值 characteristic valueof an action作用组合值combination value ofactions作用设计值 design value of anaction作用效应 effects of actions作用组合值系数coeffcient forcombination value ofactions作用效应系数coefficient of effectsof actions作用效应基本组合 fundamentalcombination foraction effects短期效应组合combination for short-term action effects作用效应偶然组合 accidentalcombination foraction effects长期效应组合combination for long-term action effects轴线axes标高elevation; datum mark坐标coordinate基准点，标高datum mark中心标高center elevation绝对标高absolute elevation相对标高relative elevation平面控制plane control高程控制elevation control水准点bench mark经纬仪transit坐标控制点coordinate control points 水准仪surveyor level测杆surveying rod钢卷尺steel tape测距仪range finder7. 土建施工挖掘 excavate履带式推土机crawler dozer挖土机 excavator履带式起重机crawler crane反铲挖土机 back digger压路机 road roller挖沟机 trench digger翻斗车 tipping skip铲运机 scraper自卸卡车 dumping truck洒水车 sprinkler回填 back fill土方工程 earth work蛙式打夯机 frog rammer钢筋切断机 bar cutter钢筋弯曲机 angle--bender基坑foundation pit双排竖管脚手架independent scaffold脚手架scaffold管子脚手架pipe scaffold 单排竖管脚手架putlog scaffold 满堂脚手架full framing 立杆 the standing pole 扫地杆 ground bars 横杆 ledger脚手板 scaffold board 防护栏 protective barrier,guard rail隔离剂 isolating agent扣件coupler旋转扣件swivel coupler 十字扣件double coupler 套筒扣件sleeve coupler 钢筋加工 rebar fabrication 铁丝 iron wire 钢筋连接 rebar connection 绑扎lashing 搭接lap焊接 welding搭接接头 lap joint 焊接接头 welded joint 搭接焊lap welding 砂浆垫块mortar block制模，模板工程formwork 组合钢模板 combined steel formwork 模板form模板配板 formwork configuration 模板支设 formwork erection 安装偏差 installation deviation 模板接缝the formwork joint泵送混凝土pumping of concrete 混凝土中心搅拌站 centralized concretemixing plant 浇灌混凝土concrete pouring, depositing concrete,模板拆除 formwork removal 混凝土搅拌汽车concrete mixer truck混凝土搅拌concrete agitation 插入式振捣器 insertion type vibrator 振捣混凝土 vibrated concrete 平板振动器 plate vibrator 抹子，泥刀 trowel 抹灰，抹光 toweling坍落度slump两组试块two sets of testing blocks 混凝土取样witness sampling of concrete混凝土施工缝处理treatment of concrete construction joint 初凝 initial set 终凝final set初凝时间 initial setting time 终凝时间final setting time 混凝土养护concrete curing 草垫 straw mattress回填back fill回填夯实backfill consolidation 1:2水泥砂浆20厚1:2 cenment mortarthickness 20mm 找平层leveling blacket 抹灰plastering抹平，找平screeding 抹灰底层rendering coat 抹光，压光trowel finish 灌缝；灌浆grout抹灰罩面层setting coat 二次灌浆post-grouting 填料filling 翼环plate ring套管pipe sleeve 石棉水泥asbestos cement 油麻oil-- hemp 橡胶止水带rubber water stop 密封胶joint sealant 沥青涂层asphalt coating填缝板joint filler镀锌铁皮galvanized iron sheeture ;revetment80mmpile ompressibilityarea f vibrationssure ol pointsocks。

Structure 2.3中文使用手册Jonathan K. Pritchard aXiaoquan Wen aDaniel Falush b 1 2 3a芝加哥大学人类遗传学系b牛津大学统计学系软件来自2010年2月2日1我们在Structure项目中的其他的同事有Peter Donnelly、Matthew Stephens和Melissa Hubisz。

2开发这个程序的第一版时作者（JP、MS、PD）在牛津大学统计系。

3关于Structure的讨论和问题请发给在线的论坛上：structure-。

在邮递问题之前请查对这个文档并搜索以前的讨论。

1 引言程序Structure使用由不连锁的标记组成的基因型数据实施基于模型的聚类方法来推断群体结构。

这种方法由普里查德（Pritchard）、斯蒂芬斯（Stephens）和唐纳利（Donnelly）（2000a）在一篇文章中引入，由Falush、斯蒂芬斯（Stephens）和普里查德（Pritchard）（2003a，2007）在续篇中进行了扩展。

我们的方法的应用包括证明群体结构的存在，鉴定不同的遗传群体，把个体归到群体，以及鉴定移居者和掺和的个体。

简言之，我们假定有K个群体（这里K可能是未知的）的一个模型，每个群体在每个位点上由一组等位基因频率来刻画。

样本内的个体被（按照概率）分配到群体，或共同分配到两个或更多个群体，如果它们的基因型表明它们是混和的。

假定在群体内，位点处于哈迪-温伯格平衡和连锁平衡。

不精确地讲，个体被按达到这一点那样的方法指定到群体。

我们的模型不假定一个特别的突变过程，并且它可以应用于大多数通常使用的遗传标记，包括微卫星（microsatellites）、SNP和RFLP。

模型假定在亚群体内标记不处于连锁不平衡（LD），因此我们不能处理极其靠近的标记。

从2.0版开始，我们现在能够处理弱连锁的标记。

虽然这里实现的计算方法是相当强有力的，但是为了保证明智的答案，在运行程序的过程中还是需要谨慎。

一、Introduction英语文章的structure是指文章在语言和逻辑上的组织结构，包括开头、中间和结尾三个部分。

在撰写英语文章时，结构的合理性对于文章的表达和阅读体验非常重要。

本文将分析英语文章结构的重要性以及如何构建一个合理的文章结构。

二、The Importance of Structure1. Clarity and Coherence英语文章结构的重要性在于能够确保文章的清晰和连贯。

一个合理的结构可以帮助作者将写作思路有序地展现出来，使读者更容易理解作者的观点和论证。

良好的结构也可以让读者在阅读过程中更加流畅地跟随作者的思路，从而增强文章的可读性。

2. Emphasizing Key Points一个好的结构可以帮助作者强调文章的重点内容。

通过恰当的组织和排列，重要的论点和观点可以得到更加突出的展现，使读者更容易理解作者的核心主张。

3. Engaging the Reader合理的结构可以帮助作者更好地吸引读者的兴趣。

通过在开头引出主题，并在结尾做出总结或者引发思考，可以让读者在阅读过程中更加投入，增加阅读的愉悦感和吸引力。

三、The Structure of an English Article1. Introduction在英语文章中，开头部分应当包括文章的背景介绍、主题阐述和一些引人入胜的内容。

开头需要引起读者的兴趣，吸引他们继续往下阅读。

作者需在开头部分清晰地表达自己的观点和目的，以便读者能够明确文章的主题和重点。

2. Body英语文章的中间部分是文章的核心，需要围绕主题对论据和论点进行详细的展开和解释。

这部分内容应当包括作者的观点、事实依据和逻辑推理，以支撑和证明作者的立场。

在组织语言时，作者需要保持逻辑清晰、语意连贯，避免篇章结构紊乱、逻辑跳跃等问题。

3. Conclusion结尾部分是文章的收尾，在英语文章中同样非常重要。

作者可以在结尾部分对文章进行总结，概括主要观点，并展开对未来展望或引发读者的思考。

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第十章C語言Structure的功能假設我們有一組學生的資料，包括學生的學號、姓名和體重，我們要如何表示這種資料呢？對很多電腦語言而言，我們必須要有三個陣列。

這三個陣列分別表示學生的學號，姓名和體重。

舉例而言，假设我們有五位學生，他們的資料如表10-1：表10-1我們就需要三個陣列，如表10-2所示：表10-2麻煩的是：這三個陣列是互有關聯的。

若是我們要將學生的資料依照學號的大小排列，學號陣列當然會改變，可是我們必須跟著同時改變姓名、陣列和體重陣列。

改過以後的三個陣列如表10-3。

表10-3因此，我們只好承認這是一件很複雜的情形。

可是，在C語言中，我們有一個簡單的辦法，我們能够利用一種叫做structure的功能，一下子就解決了這個問題。

Structure使我們能够宣告學生的資料有三個欄位：學號、姓名、體重。

學號和體重都用整數來代表，姓名用文字來表示，因此我們能够作以下的宣告。

struct student {int idnum;char name[20];float weight;}從以上的宣告看來，學生的姓名最長不能超過20個英文字。

一旦對student下定義，我們就宣告一個陣列有student的結構，這個陣列當然也要有一個名字，我們不妨將它叫做sdata，sdata內容有如表10-1所示。

假設我們要找第i個sdata的資料，我們只要找][isdata即可。

若是我們要找第i個學生的學號，我們就要指定idnumi[，而他的體重sdata].[，他的姓名是nameisdata].則是weight[。

有了sdata以後，我們能够根據其中任何一個欄位排列。

假sdata].i設我們用學號排列，就能够够取得以下的sdata，如表10-4表10-4若是用體重來排列，我們會取得如表10-5所示的資料。

表10-5至於sdata如何會有資料的？當然是靠讀入的，我們通常應該宣告一個文字檔，用讀檔案的方法能够將資料讀到sdata去。

StructureA. WikipediaFrom Wikipedia, the free encyclopediaStructure is an arrangement and organization of interrelated elements in a material object or system, or the object or system so organized.Material structures include man-made objects such as buildings and machines and natural objects such as biological organisms, minerals and chemicals.Abstract structures include data structures in computer science and musical form. Types of structure include a hierarchy (a cascade of one-to-many relationships),a network featuring many-to-many links, or a lattice featuring connections between components that are neighbors in space.StructuralismIn early 20th-century and earlier thought, form often plays a role comparable to thatof structure in contemporary thought. The neo-Kantianism of Ernst Cassirer (cf.his Philosophy of Symbolic Forms, completed in 1929 and published in English translation in the 1950s) is sometimes regarded as a precursor of the later shiftto structuralism and poststructuralism. The development of ideas of structuralism found its extension in the Prototype theory. Several experiments conducted by the cognitive psychologists in particular show that some prototypes are formed on the basis of common features. Lots of cultures describe structure as a set of different levels that reflects its horizontal nature in comparison with a vertical bar having formed the image of structure . In mathematical logic sometimes used this sign instead of multiplication sign as more appropriate.B. Sci-tech encyclopedia1 StructureA Dictionary of Sociology | 1998 | GORDON MARSHALL© A Dictionary of Sociology 1998, originally published by Oxford University Press 1998.structure, social structure A term loosely applied to any recurring pattern of social behaviour; or, more specifically, to the ordered interrelationships between the different elements of a social system or society. Thus, for example, the different kinship, religious, economic, political, and other institutions of a society may be said to comprise its social structure, as might such components as its norms, values, and social roles. The major divergence in sociological usages of structure is between those who see the term as referring to the observable patterned social practices (roles, norms,and such like) that make up social systems or societies, and those for whom structure comprises the underlying principles (for example relationships to the means of production) that pattern these overt practices. Structural functionalists exemplify the former; structuralists (such as structural Marxists) are a good example of the latter. See also FORMALISM; FUNCTION;SOCIAL ORDER; SOCIOLOGY.2. StructureThe Oxford Pocket Dictionary of Current English | 2009© The Oxford Pocket Dictionary of Current English 2009, originally published by Oxford University Press 2009.struc·ture / ˈstrək CHər/• n. the arrangement of and relations between the parts or elements of something complex:flint is extremely hard, like diamond, which has a similar structure.∎ the organization of a society or other group and the relations between its members, determining its working. ∎ a building or other object constructed from several parts. ∎ the quality of being organized: we shall use three headings to give some structure to the discussion.• v. [tr.] (often be structured) construct or arrange according to a plan; give a pattern or organization to: the game is structured so that there are five ways to win.DERIVATIVES:struc·ture·less adj.3.Structure4.Fly Fishing: The Lifetime Sport | 2005COPYRIGHT 2005 HoneyBear Press, LLC.Structure is anything that furnishes holding cover. Logs and fallen trees enhance an area by providing cover. Structures cause current cushions and hiding places. A fallen tree can create a lie which astonishing numbers of fish can use. Structure may include a wide variety of objects such as roots, plants, rocks, fallen trees, and even abandoned car bodies.C. Collins English Dictionarynoun1. a complex construction or entity2. the arrangement and interrelationship of parts in a construction, such as a building3. the manner of construction or organization ⇒the structure of society4. biology morphology; form5. chemistry the arrangement of atoms in a molecule of a chemical compound ⇒the structure of benzene6. geology the way in which a mineral, rock, rock mass or stratum, etc, is made up of its component parts7. rare the actof constructing verb8. (transitive) to impart a structure to。

Documentation for structure software:Version2.3Jonathan K.Pritchard aXiaoquan Wen aDaniel Falush b123a Department of Human GeneticsUniversity of Chicagob Department of StatisticsUniversity of OxfordSoftware from/structure.htmlApril21,20091Our other colleagues in the structure project are Peter Donnelly,Matthew Stephens and Melissa Hubisz.2Thefirst version of this program was developed while the authors(JP,MS,PD)were in the Department of Statistics,University of Oxford.3Discussion and questions about structure should be addressed to the online forum at structure-software@.Please check this document and search the previous discus-sion before posting questions.Contents1Introduction31.1Overview (3)1.2What’s new in Version2.3? (3)2Format for the datafile42.1Components of the datafile: (4)2.2Rows (5)2.3Individual/genotype data (6)2.4Missing genotype data (7)2.5Formatting errors (7)3Modelling decisions for the user73.1Ancestry Models (7)3.2Allele frequency models (12)3.3How long to run the program (13)4Missing data,null alleles and dominant markers144.1Dominant markers,null alleles,and polyploid genotypes (14)5Estimation of K(the number of populations)155.1Steps in estimating K (15)5.2Mild departures from the model can lead to overestimating K (16)5.3Informal pointers for choosing K;is the structure real? (16)5.4Isolation by distance data (17)6Background LD and other miscellania176.1Sequence data,tightly linked SNPs and haplotype data (17)6.2Multimodality (18)6.3Estimating admixture proportions when most individuals are admixed (18)7Running structure from the command line197.1Program parameters (19)7.2Parameters infile mainparams (19)7.3Parameters infile extraparams (21)7.4Command-line changes to parameter values (25)8Front End268.1Download and installation (26)8.2Overview (27)8.3Building a project (27)8.4Configuring a parameter set (28)8.5Running simulations (30)8.6Batch runs (30)8.7Exporting parameterfiles from the front end (30)8.8Importing results from the command-line program (31)8.9Analyzing the results (32)9Interpreting the text output339.1Output to screen during run (34)9.2Printout of Q (34)9.3Printout of Q when using prior population information (35)9.4Printout of allele-frequency divergence (35)9.5Printout of estimated allele frequencies(P) (35)9.6Site by site output for linkage model (36)10Other resources for use with structure3710.1Plotting structure results (37)10.2Importing bacterial MLST data into structure format (37)11How to cite this program37 12Bibliography371IntroductionThe program structure implements a model-based clustering method for inferring population struc-ture using genotype data consisting of unlinked markers.The method was introduced in a paper by Pritchard,Stephens and Donnelly(2000a)and extended in sequels by Falush,Stephens and Pritchard(2003a,2007).Applications of our method include demonstrating the presence of popu-lation structure,identifying distinct genetic populations,assigning individuals to populations,and identifying migrants and admixed individuals.Briefly,we assume a model in which there are K populations(where K may be unknown), each of which is characterized by a set of allele frequencies at each locus.Individuals in the sample are assigned(probabilistically)to populations,or jointly to two or more populations if their genotypes indicate that they are admixed.It is assumed that within populations,the loci are at Hardy-Weinberg equilibrium,and linkage equilibrium.Loosely speaking,individuals are assigned to populations in such a way as to achieve this.Our model does not assume a particular mutation process,and it can be applied to most of the commonly used genetic markers including microsatellites,SNPs and RFLPs.The model assumes that markers are not in linkage disequilibrium(LD)within subpopulations,so we can’t handle markers that are extremely close together.Starting with version2.0,we can now deal with weakly linked markers.While the computational approaches implemented here are fairly powerful,some care is needed in running the program in order to ensure sensible answers.For example,it is not possible to determine suitable run-lengths theoretically,and this requires some experimentation on the part of the user.This document describes the use and interpretation of the software and supplements the published papers,which provide more formal descriptions and evaluations of the methods.1.1OverviewThe software package structure consists of several parts.The computational part of the program was written in C.We distribute source code as well as executables for various platforms(currently Mac,Windows,Linux,Sun).The C executable reads a datafile supplied by the user.There is also a Java front end that provides various helpful features for the user including simple processing of the output.You can also invoke structure from the command line instead of using the front end.This document includes information about how to format the datafile,how to choose appropriate models,and how to interpret the results.It also has details on using the two interfaces(command line and front end)and a summary of the various user-defined parameters.1.2What’s new in Version2.3?The2.3release(April2009)introduces new models for improving structure inference for data sets where(1)the data are not informative enough for the usual structure models to provide accurate in-ference,but(2)the sampling locations are correlated with population membership.In this situation, by making explicit use of sampling location information,we give structure a boost,often allowing much improved performance(Hubisz et al.,2009).We hope to release further improvements in the coming months.loc a loc b loc c loc d loc eGeorge1-914566092George1-9-964094Paula110614268192Paula110614864094Matthew2110145-9092Matthew2110148661-9Bob210814264194Bob2-9142-9094Anja1112142-91-9Anja111414266194Peter1-9145660-9Peter1110145-91-9Carsten2108145620-9Carsten211014564192Table1:Sample datafile.Here MARKERNAMES=1,LABEL=1,POPDATA=1,NUMINDS=7, NUMLOCI=5,and MISSING=-9.Also,POPFLAG=0,LOCDATA=0,PHENOTYPE=0,EX-TRACOLS=0.The second column shows the geographic sampling location of individuals.We can also store the data with one row per individual(ONEROWPERIND=1),in which case thefirst row would read“George1-9-9145-96664009294”.2Format for the datafileThe format for the genotype data is shown in Table2(and Table1shows an example).Essentially, the entire data set is arranged as a matrix in a singlefile,in which the data for individuals are in rows,and the loci are in columns.The user can make several choices about format,and most of these data(apart from the genotypes!)are optional.For a diploid organism,data for each individual can be stored either as2consecutive rows, where each locus is in one column,or in one row,where each locus is in two consecutive columns. Unless you plan to use the linkage model(see below)the order of the alleles for a single individual does not matter.The pre-genotype data columns(see below)are recorded twice for each individual. (More generally,for n-ploid organisms,data for each individual are stored in n consecutive rows unless the ONEROWPERIND option is used.)2.1Components of the datafile:The elements of the inputfile are as listed below.If present,they must be in the following order, however most are optional(as indicated)and may be deleted completely.The user specifies which data are present,either in the front end,or(when running structure from the command line),in a separatefile,mainparams.At the same time,the user also specifies the number of individuals and the number of loci.2.2Rows1.Marker Names(Optional;string)Thefirst row in thefile can contain a list of identifiersfor each of the markers in the data set.This row contains L strings of integers or characters, where L is the number of loci.2.Recessive Alleles(Data with dominant markers only;integer)Data sets of SNPs or mi-crosatellites would generally not include this line.However if the option RECESSIVEALLE-LES is set to1,then the program requires this row to indicate which allele(if any)is recessive at each marker.See Section4.1for more information.The option is used for data such as AFLPs and for polyploids where genotypes may be ambiguous.3.Inter-Marker Distances(Optional;real)the next row in thefile is a set of inter-markerdistances,for use with linked loci.These should be genetic distances(e.g.,centiMorgans),or some proxy for this based,for example,on physical distances.The actual units of distance do not matter too much,provided that the marker distances are(roughly)proportional to recombination rate.The front end estimates an appropriate scaling from the data,but users of the command line version must set LOG10RMIN,LOG10RMAX and LOG10RSTART in thefile extraparams.The markers must be in map order within linkage groups.When consecutive markers are from different linkage groups(e.g.,different chromosomes),this should be indicated by the value-1.Thefirst marker is also assigned the value-1.All other distances are non-negative.This row contains L real numbers.4.Phase Information(Optional;diploid data only;real number in the range[0,1]).This isfor use with the linkage model only.This is a single row of L probabilities that appears after the genotype data for each individual.If phase is known completely,or no phase information is available,these rows are unnecessary.They may be useful when there is partial phase information from family data or when haploid X chromosome data from males and diploid autosomal data are input together.There are two alternative representations for the phase information:(1)the two rows of data for an individual are assumed to correspond to the paternal and maternal contributions,respectively.The phase line indicates the probability that the ordering is correct at the current marker(set MARKOVPHASE=0);(2)the phase line indicates the probability that the phase of one allele relative to the previous allele is correct(set MARKOVPHASE=1).Thefirst entry should befilled in with0.5tofill out the line to L entries.For example the following data input would represent the information from an male with5unphased autosomal microsatellite loci followed by three X chromosome loci, using the maternal/paternal phase model:102156165101143105104101100148163101143-9-9-90.50.50.50.50.5 1.0 1.0 1.0where-9indicates”missing data”,here missing due to the absence of a second X chromo-some,the0.5indicates that the autosomal loci are unphased,and the1.0s indicate that the X chromosome loci are have been maternally inherited with probability1.0,and hence are phased.The same information can be represented with the markovphase model.In this case the inputfile would read:102156165101143105104101100148163101143-9-9-90.50.50.50.50.50.5 1.0 1.0Here,the two1.0s indicate that thefirst and second,and second and third X chromosome loci are perfectly in phase with each other.Note that the site by site output under these two models will be different.In thefirst case,structure would output the assignment probabilities for maternal and paternal chromosomes.In the second case,it would output the probabilities for each allele listed in the inputfile.5.Individual/Genotype data(Required)Data for each sampled individual are arranged intoone or more rows as described below.2.3Individual/genotype dataEach row of individual data contains the following elements.These form columns in the datafile.bel(Optional;string)A string of integers or characters used to designate each individualin the sample.2.PopData(Optional;integer)An integer designating a user-defined population from which theindividual was obtained(for instance these might designate the geographic sampling locations of individuals).In the default models,this information is not used by the clustering algorithm, but can be used to help organize the output(for example,plotting individuals from the same pre-defined population next to each other).3.PopFlag(Optional;0or1)A Booleanflag which indicates whether to use the PopDatawhen using learning samples(see USEPOPINFO,below).(Note:A Boolean variable(flag)isa variable which takes the values TRUE or FALSE,which are designated here by the integers1(use PopData)and0(don’t use PopData),respectively.)4.LocData(Optional;integer)An integer designating a user-defined sampling location(orother characteristic,such as a shared phenotype)for each individual.This information is used to assist the clustering when the LOCPRIOR model is turned on.If you simply wish to use the PopData for the LOCPRIOR model,then you can omit the LocData column and set LOCISPOP=1(this tells the program to use PopData to set the locations).5.Phenotype(Optional;integer)An integer designating the value of a phenotype of interest,foreach individual.(φ(i)in table.)(The phenotype information is not actually used in structure.It is here to permit a smooth interface with the program STRAT which is used for association mapping.)6.Extra Columns(Optional;string)It may be convenient for the user to include additionaldata in the inputfile which are ignored by the program.These go here,and may be strings of integers or characters.7.Genotype Data(Required;integer)Each allele at a given locus should be coded by a uniqueinteger(eg microsatellite repeat score).2.4Missing genotype dataMissing data should be indicated by a number that doesn’t occur elsewhere in the data(often-9 by convention).This number can also be used where there is a mixture of haploid and diploid data (eg X and autosomal loci in males).The missing-data value is set along with the other parameters describing the characteristics of the data set.2.5Formatting errors.We have implemented reasonably careful error checking to make sure that the data set is in the correct format,and the program will attempt to provide some indication about the nature of any problems that exist.The front end requires returns at the ends of each row,and does not allow returns within rows;the command-line version of structure treats returns in the same way as spaces or tabs.One problem that can arise is that editing programs used to assemble the data prior to importing them into structure can introduce hidden formatting characters,often at the ends of lines,or at the end of thefile.The front end can remove many of these automatically,but this type of problem may be responsible for errors when the datafile seems to be in the right format.If you are importing data to a UNIX system,the dos2unix function can be helpful for cleaning these up.3Modelling decisions for the user3.1Ancestry ModelsThere are four main models for the ancestry of individuals:(1)no admixture model(individuals are discretely from one population or another);(2)the admixture model(each individual draws some fraction of his/her genome from each of the K populations;(3)the linkage model(like the admixture model,but linked loci are more likely to come from the same population);(4)models with informative priors(allow structure to use information about sampling locations:either to assist clustering with weak data,to detect migrants,or to pre-define some populations).See Pritchard et al.(2000a)and(Hubisz et al.,2009)for more on models1,2,and4and Falush et al.(2003a)for model3.1.No admixture model.Each individual comes purely from one of the K populations.The output reports the posterior probability that individual i is from population k.The prior probability for each population is1/K.This model is appropriate for studying fully discrete populations and is often more powerful than the admixture model at detecting subtle structure.2.Admixture model.Individuals may have mixed ancestry.This is modelled by saying that individual i has inherited some fraction of his/her genome from ancestors in population k.The output records the posterior mean estimates of these proportions.Conditional on the ancestry vector,q(i),the origin of each allele is independent.We recommend this model as a starting point for most analyses.It is a reasonablyflexible model for dealing with many of the complexities of real populations.Admixture is a common feature of real data,and you probably won’tfind it if you use the no-admixture model.The admixture model can also deal with hybrid zones in a natural way.Label Pop Flag Location Phen ExtraCols Loc1Loc2Loc3....Loc LM1M2M3....M Lr1r2r3....r L-1D1,2D2,3....D L−1,LID(1)g(1)f(1)l(1)φ(1)y(1)1,...,y(1)n x(1,1)1x(1,1)2x(1,1)3....x(1,1)LID(1)g(1)f(1)l(1)φ(1)y(1)1,...,y(1)n x(1,2)1x(1,2)2x(1,2)3....x(1,2)Lp(1)1p(1)2p(1)3....p(1)LID(2)g(2)f(2)l(2)φ(2)y(2)1,...,y(2)n x(2,1)1x(2,1)2x(2,1)3....x(2,1)LID(2)g(2)f(2)l(2)φ(2)y(2)1,...,y(2)n x(2,2)1x(2,2)2x(2,2)3....x(2,2)Lp(2)1p(2)2p(2)3....p(2)L ....ID(i)g(i)f(i)l(i)φ(i)y(i)1,...,y(i)n x(i,1)1x(i,1)2x(i,1)3....x(i,1)LID(i)g(i)f(i)l(i)φ(i)y(i)1,...,y(i)n x(i,2)1x(i,2)2x(i,2)3....x(i,2)Lp(3)1p(3)2p(3)3....p(3)L ....ID(N)g(N)f(N)l(N)φ(N)y(N)1,...,y(N)n x(N,1)1x(N,1)2x(N,1)3....x(N,1)LID(N)g(N)f(N)l(N)φ(N)y(N)1,...,y(N)n x(N,2)1x(N,2)2x(N,2)3....x(N,2)Lp(L)1p(L)2p(L)3....p(1)LTable2:Format of the datafile,in two-row format.Most of these components are optional(see text for details).M l is an identifier for marker l.r l indicates which allele,if any,is recessive at each marker(dominant genotype data only).D i,i+1is the distance between markers i and i+1.ID(i) is the label for individual i,g(i)is a predefined population index for individual i(PopData);f(i)is aflag used to incorporate learning samples(PopFlag);l(i)is the sampling location of individual i (LocData);φ(i)can store a phenotype for individual i;y(i)1,...,y(i)n are for storing extra data(ignoredby the program);(x i,1l ,x i,2l)stores the genotype of individual i at locus l.p(l)i is the phase informationfor marker l in individual i.3.Linkage model.This is essentially a generalization of the admixture model to deal with“ad-mixture linkage disequilibrium”–i.e.,the correlations that arise between linked markers in recently admixed populations.Falush et al.(2003a)describes the model,and computations in more detail.The basic model is that,t generations in the past,there was an admixture event that mixed the K populations.If you consider an individual chromosome,it is composed of a series of“chunks”that are inherited as discrete units from ancestors at the time of the admixture.Admixture LD arises because linked alleles are often on the same chunk,and therefore come from the same ancestral population.The sizes of the chunks are assumed to be independent exponential random variables with mean length1/t(in Morgans).In practice we estimate a“recombination rate”r from the datathat corresponds to the rate of switching from the present chunk to a new chunk.1Each chunkin individual i is derived independently from population k with probability q(i)k ,where q(i)kis theproportion of that individual’s ancestry from population k.Overall,the new model retains the main elements of the admixture model,but all the alleles that are on a single chunk have to come from the same population.The new MCMC algorithm integrates over the possible chunk sizes and break points.It reports the overall ancestry for each individual,taking account of the linkage,and can also report the probability of origin of each bit of chromosome,if desired by the user.This new model performs better than the original admixture model when using linked loci to study admixed populations.It achieves more accurate estimates of the ancestry vector,and can extract more information from the data.It should be useful for admixture mapping.The model is not designed to deal with background LD between very tightly linked markers.Clearly,this model is a big simplification of the complex realities of most real admixed popu-lations.However,the major effect of admixture is to create long-range correlation among linked markers,and so our aim here is to encapsulate that feature within a fairly simple model.The computations are a bit slower than for the admixture model,especially with large K and unphased data.Nonetheless,they are practical for thousands of sites and individuals and multiple populations.The model can only be used if there is information about the relative positions of the markers(usually a genetic map).ing prior population information.The default mode for structure uses only genetic information to learn about population structure.However,there is often additional information that might be relevant to the clustering(e.g.,physical characteristics of sampled individuals or geographic sampling locations).At present,structure can use this information in three ways:•LOCPRIOR models:use sampling locations as prior information to assist the clustering–for use with data sets where the signal of structure is relatively weak2.There are some data sets where there is genuine population structure(e.g.,significant F ST between sampling locations),but the signal is too weak for the standard structure models to detect.This is often the case for data sets with few markers,few individuals,or very weak structure.To improve performance in this situation,Hubisz et al.(2009)developed new models that make use of the location information to assist clustering.The new models can often provide accurate inference of population structure and individual ancestry in data sets where the signal of structure is too weak to be found using the standard structure models.Briefly,the rationale for the LOCPRIOR models is as ually,structure assumes that all partitions of individuals are approximately equally likely a priori.Since there is an immense number of possible partitions,it takes highly informative data for structure to 1Because of the way that this is parameterized,the map distances in the inputfile can be in arbitrary units–e.g.,genetic distances,or physical distances(under the assumption that these are roughly proportional to genetic distances).Then the estimated value of r represents the rate of switching from one chunks to the next,per unit of whatever distance was assumed in the inputfile.E.g.,if an admixture event took place ten generations ago,then r should be estimated as0.1when the map distances are measured in cM(this is10∗0.01,where0.01is the probability of recombination per centiMorgan),or as10−4=10∗10−5when the map distances are measured in KB(assuming a constant crossing-over rate of1cM/MB).The prior for r is log-uniform.The front end tries to make some guesses about sensible upper and lower bounds for r,but the user should adjust these to match the biology of the situation.2Daniel refers to this as“Better priors for worse data.”conclude that any particular partition of individuals into clusters has compelling statistical support.In contrast,the LOCPRIOR models take the view that in practice,individuals from the same sampling location often come from the same population.Therefore,the LOCPRIOR models are set up to expect that the sampling locations may be informative about ancestry. If the data suggest that the locations are informative,then the LOCPRIOR models allow structure to use this information.Hubisz et al.(2009)developed a pair of LOCPRIOR models:for no-admixture and for admix-ture.In both cases,the underlying model(and the likelihood)is the same as for the standard versions.The key difference is that structure is allowed to use the location information to assist the clustering(i.e.,by modifying the prior to prefer clustering solutions that correlate with the locations).The LOCPRIOR models have the desirable properties that(i)they do not tend tofind struc-ture when none is present;(ii)they are able to ignore the sampling information when the ancestry of individuals is uncorrelated with sampling locations;and(iii)the old and new models give essentially the same answers when the signal of population structure is very strong.Hence,we recommend using the new models in most situations where the amount of available data is very limited,especially when the standard structure models do not provide a clear signal of structure.However,since there is now a great deal of accumulated experience with the standard structure models,we recommend that the basic models remain the default for highly informative data sets(Hubisz et al.,2009).To run the LOCPRIOR model,the user mustfirst specify a“sampling location”for each individual,coded as an integer.That is,we assume the samples were collected at a set of discrete locations,and we do not use any spatial information about the locations.(We recognize that in some studies,every individual may be collected at a different location,and so clumping individuals into a smaller set of discrete locations may not be an ideal representation of the data.)The“locations”could also represent a phenotype,ecotype,or ethnic group. The locations are entered into the inputfile either in the PopData column(set LOCISPOP=1), or as a separate LocData column(see Section2.3).To use the LOCPRIOR model you must first specify either the admixture or no-admixture models.If you are using the Graphical User Interface version,tick the“use sampling locations as prior”box.If you are using the command-line version,set LOCPRIOR=1.(Note that LOCPRIOR is incompatible with the linkage model.)Our experience so far is that the LOCPRIOR model does not bias towards detecting structure spuriously when none is present.You can use the same diagnostics for whether there is genuine structure as when you are not using a LOCPRIOR.Additionally it may be helpful to look at the value of r,which parameterizes the amount of information carried by the locations. Values of r near1,or<1indicate that the locations are rger values of r indicate that either there is no population structure,or that the structure is independent of the locations.•USEPOPINFO model:use sampling locations to test for migrants or hybrids–for use with data sets where the data are very informative.In some data sets,the user mightfind that pre-defined groups(eg sampling locations)correspond almost exactly to structure clusters,except for a handful of individuals who seem to be misclassified.Pritchard et al.(2000a)developed a formal Bayesian test for evaluating whether any individuals in the sample are immigrants to their supposed populations,or have recent immigrant ancestors.Note that this model assumes that the predefined populations are usually correct.It takes quite strong data to overcome the prior against misclassification.Before using the USEPOPINFO model,you should also run the program without population information to ensure that the pre-defined populations are in rough agreement with the genetic information.To use this model set USEPOPINFO to1,and choose a value of MIGRPRIOR(which isνin Pritchard et al.(2000a)).You might choose something in the range0.001to0.1forν.The pre-defined population for each individual is set in the input datafile(see PopData).In this mode,individuals assigned to population k in the inputfile will be assigned to cluster k in the structure algorithm.Therefore,the predefined populations should be integers between 1and MAXPOPS(K),inclusive.If PopData for any individual is outside this range,their q will be updated in the normal way(ie without prior population information,according to the model that would be used if USEPOPINFO was turned off.3).•USEPOPINFO model:pre-specify the population of origin of some individuals to assist ancestry estimation for individuals of unknown origin.A second way to use the USEPOPINFO model is to define“learning samples”that are pre-defined as coming from particular clusters.structure is then used to cluster the remaining individuals.Note:In the Front End,this option is switched on using the option“Update allele frequencies using only individuals with POPFLAG=1”,located under the“Advanced Tab”.Learning samples are implemented using the PopFlag column in the datafile.The pre-defined population is used for those individuals for whom PopFlag=1(and whose PopData is in(1...K)).The PopData value is ignored for individuals for whom PopFlag=0.If there is no PopFlag column in the datafile,then when USEPOPINFO is turned on,PopFlag is set to1 for all individuals.Ancestry of individuals with PopFlag=0,or with PopData not in(1...K) are updated according to the admixture or no-admixture model,as specified by the user.As noted above,it may be helpful to setαto a sensible value if there are few individuals without predefined populations.This application of USEPOPINFO can be helpful in several contexts.For example,there may be some individuals of known origin,and the goal is to classify additional individuals of unknown origin.For example,we might collect data from a set of dogs of known breeds (numbered1...K),and then use structure to estimate the ancestry for additional dogs of unknown(possibly hybrid)origin.By pre-setting the population numbers,we can ensure that the structure clusters correspond to pre-defined breeds,which makes the output more interpretable,and can improve the accuracy of the inference.(Of course,if two pre-defined breeds are genetically identical,then the dogs of unknown origin may be inferred to have mixed ancestry.Another use of USEPOPINFO is for cases where the user wants to update allele frequen-cies using only a subset of the individuals.Ordinarily,structure analyses update the allele frequency estimates using all available individuals.However there are some settings where you might want to estimate ancestry for some individuals,without those individuals affecting the allele frequency estimates.For example you may have a standard collection of learning samples,and then periodically you want to estimate ancestry for new batches of genotyped 3If the admixture model is used to estimate q for those individuals without prior population information,αis updated on the basis of those individuals only.If there are very few such individuals,you may need tofixαat a sensible value.。

1.Structural ConceptsAircraft constructionAircraft constructionAn aircraft consists of many structural components. The p principal ones being body, doors, p g empennage, L/G, nacelles, wing, and so on. 飞机由许多构件组成， 其中重要的包括机体， 门，尾部，起落架， 机翼等Aircraft constructionThese structural components are assembled using g smaller parts such as stringers, g longerons, ribs, bulkheads, and so on.从组装的角度看，这些 构件都各有较小的部分， 比如桁条；纵樑；肋； 壁板等Operating loads p gCruise Climb Stress Takeoff 0 Landing Taxi T i Lower wing skin stress history (下翼面应力变化) Operating loads establish the fatigue and durability requirements of the airplane.工作载荷确定了飞机结构对 疲劳和耐久要求。

疲劳和耐久要求 DescentAircraft constructionThe smaller parts are constructed from a wide variety of materials and are joined by rivets, bolts, screws, welding, welding or adhesives. 较小的结构部分采用 多种材料制成，以铆 接，螺纹，焊接或粘 接方法组装。

Structure各种数据结构//1.set集合：纯粹的容器；⽆需存储，就是⼀个容器Array/ArrayList/List/LinkedList/Queue/Stack/HastSet/SortedSet/Hashtable/SortedList/Dictionary/SortedDictionary IEnumerable、ICollection、IList、IQueryableArray{//Array：数组在内存上连续分配的，⽽且元素类型是⼀样的//可以坐标访问读取快（因为有索引）--增删慢，长度不变，定长Console.WriteLine("***************Array******************");int[] intArray = new int[3];intArray[0] = 123;string[] stringArray = new string[] { "123", "234" };//Arrayforeach (var item in stringArray){}for (int i = 0; i < intArray.Length; i++){Console.WriteLine(intArray[i]);}}//ArrayList:以前的开发中使⽤的⽐较多不定长的，连续分配的；//元素没有类型限制，任何元素都是当成object处理，如果是值类型，会有装箱操作//读取快--增删慢Console.WriteLine("***************ArrayList******************");ArrayList arrayList = new ArrayList();arrayList.Add("Richard");arrayList.Add("Is");arrayList.Add(32);//add增加长度// arrayList[4] = 26;//索引复制，不会增加长度//删除数据//arrayList.RemoveAt(4);var value = arrayList[2];arrayList.RemoveAt(0);arrayList.Remove("Richard");foreach (var item in arrayList){}for (int i = 0; i < arrayList.Count; i++){Console.WriteLine(arrayList[i]);}　//2.线型结构：⼀对⼀List:也是Array，在存储的时候；内存上都是连续摆放;不定长；泛型，保证类型安全，避免装箱拆箱；性能也是⽐Arraylist要⾼可⽤索引访问 //读取快--增删慢Console.WriteLine("***************List<T>******************");List<int> intList = new List<int>() { 1, 2, 3, 4 };intList.Add(123);intList.Add(123);//intList.Add("123");//intList[0] = 123;List<string> stringList = new List<string>();//stringList[0] = "123";//异常的foreach (var item in intList){}for (int i = 0; i < intList.Count; i++){Console.WriteLine(intList[i]);} 以上都可以⽤索引访问都为数组//LinkedList：泛型的特点;链表，元素不连续分配，每个元素都有记录前后节点不能⽤索引访问⾮连续摆放，存储数据+地址，找数据的话就只能顺序查找，读取慢；增删快，#region 链表{//节点值可以重复//能不能索引访问？不能，//1.查询元素就只能遍历查找不⽅便--查询慢//2.增删就⽐较⽅便--增删快Console.WriteLine("***************LinkedList<T>******************");LinkedList<int> linkedList = new LinkedList<int>();//linkedList[3] //不能索引访问--不是数组linkedList.AddFirst(123);//在最前⾯添加linkedList.AddLast(456); //在最后添加bool isContain = linkedList.Contains(123);LinkedListNode<int> node123 = linkedList.Find(123); //元素123的位置从头查找linkedList.AddBefore(node123, 123);linkedList.AddBefore(node123, 123);linkedList.AddAfter(node123, 9);linkedList.Remove(456);linkedList.Remove(node123);linkedList.RemoveFirst();linkedList.RemoveLast();linkedList.Clear();} //集合：hash分布，元素间没关系,动态增加容量去重--如果是同⼀个引⽤，就可以去掉重复；//应⽤场景：抖⾳发布的作品点赞！统计⽤户IP；IP投票//提供了⼀些计算：交叉并补--⼆次好友/间接关注/粉丝合集//应⽤场景：donkey：Seven 系统可能推荐⼀些可能认识的⼈：找出Seven好友列表：找出donkey这个同学的好友列表：求差集；---是donkey的好友，但是不是Seven好友。

c++中structure用法1.结构体是C++中一种用来封装一组不同类型的数据的数据结构。

Structures in C++ are used to encapsulate a group of different types of data.2.结构体可以包含不同的数据类型，例如整数、浮点数和字符型数据。

Structures can contain different data types, such as integers, floating point numbers, and character data.3.结构体可以通过使用关键字struct来定义。

Structures can be defined using the keyword struct.4.结构体中的数据成员可以用不同的访问修饰符进行访问控制。

Data members in a structure can be access controlled using different access modifiers.5.结构体可以包含成员函数，在C++中被称为方法。

Structures can contain member functions, which are called methods in C++.6.结构体中的成员变量可以使用点操作符来访问。

Member variables in a structure can be accessed using the dot operator.7.结构体可以作为参数传递给函数，也可以作为函数的返回值。

Structures can be passed as parameters to functions and also can be returned by functions.8.结构体可以用来表示复杂的数据结构，例如链表和树。

Structures can be used to represent complex data structures, such as linked lists and trees.9.结构体可以包含其他结构体作为其成员。