205_Model Development and Validation based on Carbon Balance Method for Hybrid Vehicle_泛亚_丁华杰

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人力资源管理师专业英文词汇

企业人力资源管理师（二级）专业词汇表序号认知词汇中译词意1 360-degree feedback process 360度反馈过程2 Absence 缺席3 Acceptability 可接受性4 Achievement tests 成就测试5 Action plan 行动计划6 Accountability 有责任7 Adolescent 青少年8 Adverse impact 负面影响9 Aggressive 有闯劲的，敢做敢为的10 Allowance 津贴，补助11 Ambition 野心，雄心12 Analytic approach 分析法13 Announcement 公告14 Applicant 求职者15 Application 申请16 Appraisal 评价，评估17 Appoint 任命18 Arbitrary 仲裁19 Assessment center 评价中心20 Attitude awareness and change program 态度认知与改变计划21 Attitudinal structuring 态度构建22 Authority 权威23 Audiovisual instruction 视听教学24 Audit approach 审计法25 Balanced scorecard 综合评价卡，平衡计分法26 Bargain 商谈27 Behavior modeling 行为模拟28 Behavior—based program 行为改变计划29 Benchmarks 基准30 Benefits 福利31 Bonus 奖金32 Business planning 企业规划33 Business division 事业部34 Business integration 业务整合35 Candidate 候选人36 Career anchor 职业锚37 Career counseling 职业咨询38 Career curves 职业曲线39 Career management system 职业管理系统40 Career development 职业发展41 Centralization 集权化42 Coach 教练43 Cognitive ability 认知能力44 Cognitive outcomes 认知性结果45 Collective bargaining process 劳资谈判过程46 Commitment 承诺，义务47 Communication skill 沟通技巧48 Compa—ratio 比较比率49 Compensable factors 报酬要素50 Compensation 报酬，补偿51 Competency assessment 能力评估52 Competency model 能力模型53 Competitive advantage 竞争优势54 Compromise 妥协55 Concentration strategy 集中战略56 Consultation 商量，请教57 Consumer price index, CPI 消费者价格指数58 Continuous learning 持续学习59 Coordination training 合作培训60 Core competencies 核心竞争力61 Cost structure 成本结构62 Critical incident method 关键事件法63 Cross—cultural preparation 跨文化准备64 Cross—training 交叉培训65 Cultural environment 文化环境66 Cultural shock 文化冲击67 Customer appraisal 顾客评估68 CV （curriculum vitae）简历69 Data flow diagram 数据流程图70 Decentralization 分散化71 Decision making 决策72 Decision support systems 决策支持系统73 Deficiency 缺乏74 Defined-benefit plan 养老金福利计划75 Defined-contribution plan 资方养老金投入计划76 Delayering 扁平化77 Demand forecasting 需求预测78 Depression 沮丧79 Development planning system 开发规划系统80 Differential piece rate 差额计件工资81 Direct costs 直接成本82 Discipline 纪律83 Disparate impact 差别性影响84 Disparate treatment 差别性对待85 Diversity training 多元化培训86 Dividends 红利87 Discrimination 歧视88 Dismiss 开除，解雇89 Downsizing 精简90 Downward move 降级91 Efficiency wage theory 效率工资理论92 Egalitarian 平等主义93 Earnings 所得，收入94 Efficiency 效率95 Employee empowerment 员工授权96 Employee leasing 员工租借97 Employee survey research 雇员调查与研究98 Entrepreneur 企业家99 Equal employment opportunity （EEO) 公平就业机会100 Ethics 道德101 Exit interview 离职面谈102 Expatriate 外派雇员103 Expert systems 专家系统104 Explicit knowledge 显性知识105 External analysis 外部分析106 External growth strategy 外边成长战略107 External labor market 外部劳动力市场108 Face to face discussion 当面讨论109 Factor comparison system 因素比较法110 Feedback 反馈111 Flat hourly rate 小时工资率112 Flexible benefits plans (cafeteria plans) 灵活的福利计划(自助福利方案)113 Flextime 灵活的时间114 Flowchart 流程图115 Follow up 跟随,追随116 Formal education programs 正规教育计划117 Frame of reference 参照系118 Functional job analysis, FJA 职能工作分析119 Gain sharing plans 收益分享计划120 Globalization 全球化121 Goals and timetables 目标和时间表122 Graphic rating-scale method 图式评估法123 Grievance 委屈124 Group mentoring program 群体指导计划125 Guidelines 指导方针126 Head hunter 猎头127 Healthy and safety 健康安全128 Handover 工作交接129 High—performance work systems 高绩效工作系统130 Hourly work 计时工资制131 Human capital 人力资本132 Human resource information system (HRIS) 人力资源信息系统133 Human resource management 人力资源管理134 Human resources planning, HRP 人力资源计划135 Income 收入，收益136 Indirect costs 间接成本137 Individualism/collectivism 个人主义/集体主义138 Inflation 通货膨胀139 Input 投入140 Insurance 保险141 Intellectual asset 知识资产142 Internal analysis 内部分析143 Internal growth strategy 内部成长战略144 Internal labor force 内部劳动力145 Internet 互联网146 Internship programs 实习计划147 Interview 面试148 Industrialization 产业化149 IT(Information Technology）信息技术150 Invest 投资151 Job analysis 工作分析152 Job classification system 工作分类法153 Job description 工作描述154 Job design 工作设计155 Job enlargement 工作扩大化156 Job enrichment 工作丰富化157 Job evaluation 工作评价158 Job commitment 工作认同159 Job ranking system 工作重要性排序法160 Job rotation 工作轮换161 Job satisfaction 工作满意度162 Job specification 工作规范163 Joint venture company 合资公司164 Key performance indicator，KPI 关键业绩指标165 Labor relations process 劳动关系进程166 Leaderless group discussion 无领导小组讨论法167 Learning organization 学习型组织168 Line manager 直线经理169 Maintenance of membership 会员资格维持170 Management by objectives, MBO 目标管理171 Management forecasts 管理预测172 Management process 管理过程173 Manager appraisal 经理评估174 Managing diversity 管理多元化175 Manpower 人力，劳动力176 Material incentive 物质奖励177 Mediation 调解178 Mentor 导师179 Merit guideline 绩效指南180 Minimum wage 最低工资181 Morale 士气182 Mobility 流动性183 Motivation to learn 学习的动机184 Needs assessment (培训）需要评价185 Night shift 夜班186 Nonprofit organization 非营利组织187 Occupation 职业188 On-the—job training，OJT 在职培训189 Open culture 开放文化190 Opportunity to perform 实践的机会191 Organization desgin and development 组织设计与发展192 Organizational analysis 组织分析193 Organizational capability 组织能力194 Organiztion chart 组织结构图195 Organization code 组织代码196 Orientation 入职培训197 Outlay 费用198 Outplacement counseling 重新谋职咨询199 Output 产出200 Outsourcing 外包201 Overpay 超额工资202 Panel interview 小组面试203 Pay claim 加薪要求204 Pay grade 工资等级205 Pay structure 工资结构206 Pay—for—performance standard 按绩效的报酬标准207 Pay—policy line 工资政策线208 Payroll 职工薪水册209 Pension 养老金,退休金210 Peer appraisal 同事评估211 Pep talk 鼓舞动员谈话212 Performance appraisal 绩效评价213 Performance feedback 绩效反馈214 Performance management 绩效管理215 Performance planning and evaluation (PPE）绩效规划与评价系统216 Post 岗位，职位217 Potential 潜在的，可能的218 Priority 优先219 Probation 试用220 Person characteristics 个人特征221 Personnel selection 人员甄选222 Piecework 计件工资223 Position analysis questionnaire, PAQ 职位分析问卷调查224 Power distance 权力差距225 Predictive validation 预测效度226 Profit sharing 利润分享227 Promotion 晋升228 Psychological contract 心理契约229 Questionnaire 调查问卷230 Rapport 和谐，亲善231 Readability 易读性232 Readiness for training 培训准备233 Reasoning ability 推理能力234 Reconciliation 和解235 Recognition 认可,承认236 Recruitment 招募237 Redundancy 冗余238 Reengineering 流程再造239 Reference 参考240 Reject 拒绝,否决241 Reinstatement 复职242 Relational database 关联数据库243 Reliability 信度244 Remuneration 报酬245 Reputation 声誉，名声246 Retention plan (核心人员)保持计划247 Repatriation 归国准备248 Replacement charts 替换表249 Return on investment (ROI）投资回报250 Role ambiguity 角色模糊251 Role analysis technique 角色分析技术252 Role play 角色扮演253 Senior management 高级管理层254 Settlement 解决，决定255 Sick le口口e 病假256 Simulation 仿真，模拟257 Self—appraisal 自我评估258 Subcontracting 转包合同259 Substantive reason 客观存在因素260 Successor 后任261 Supply forecasting 供给预测262 Survey 调查263 Target 目标，目的264 Talent 才能，才干265 Sick note 病假条266 Situational interview 情景面试267 Skill inventories 技能量表268 Skill-based pay 技能工资269 Specificity 明确性270 Spot bonus 即时奖金271 Staffing tables 人员配置表272 Strategic choice 战略选择273 Strategic congruence 战略一致性274 Strategic human resource management 战略性人力资源管理275 Strategy formulation 战略形成276 Strategy implementation 战略执行277 Subordinate 下属278 Succession planning 可持续发展计划279 Tacit knowledge 隐形知识280 Task analysis 任务分析281 Team leader training 团队领导培训282 Team building 团队建设283 Top stratum 高层284 Termination 终止285 Total quality management （TQM) 全面质量管理286 Training administration 培训管理287 Training outcomes 培训结果288 Transaction processing 事务处理289 Trend analysis 趋势分析290 Turnover 离职，流动291 Utility analysis 效用分析292 Validity 效度293 Verbal comprehension 语言理解能力294 Vesting 既得利益295 V oicing 发言296 Wage and salary survey 薪资调查297 Wage freeze 冻结工资增长298 Web—based training 网上培训299 Welfare system 福利体系300 Work permit/ work certificate 就业许可证。

制药行业常用英语词汇(缩写、中英文对照)

制药行业常用英语词汇（缩写、中英文对照）制药行业常用英语词汇（缩写、中英文对照）序号中文英文及缩写 1 药品生产质量管理规范 GMP：GoodManufacturingPractice 2 国家食品与药品监督管理局 State Food and Drug Administration 3 总则 GeneralProvisions 4 《中华人民共和国药品管理法》 the DrugAdministration Law of the People"s Republic of China 5 制剂Preparation 6 原料药 API: Active PharmaceuticalIngredient 7 成品finished goods 8 工序 process 9 机构与人员 organization and personnel 10 专业知识 professional knowledge 11 生产经验 production e_perience 12 组织能力 organizational skill 13 技术人员 technical staff 14 实施implementation 15 药品生产 pharmaceutical manufacturing 16 质量管理quality management 17 质量检验 quality inspection 18 专业技术培训professional and technicaltraining 19 基础理论知识 basic theoreticalknowledge 20 实际操作技能 practical operationskills 21 高生物活性 highly potent 22 高毒性 high to_icity 23 污染 contamination 24 考核评估 assessment25 厂房与设施 buildings and facilities 26 生产环境 production environment 27 空气洁净级别 clean air level 28 昆虫 insect 29 洁净室（区）clean room(area) 30 光滑 smooth 31 无裂缝 no cracks 32 无颗粒物脱落no particle shedding 33 耐受 endure 34 消毒 disinfection 35 无菌 sterile 36 交界处 junction, joint 37 弧形 arc 38 灰尘积聚 dues accumulation 39 储存区 store area 40 生产规模 production scale 41 设备 equipment 42 物料material 43 中间产品 intermediate product 44 待验品 quarantined material 45 交叉污染 cross-contamination 46 管道 pipeline, ductwork 47 风口 tuber 48 公用设施, 公用工程 utilities of publicservice 49 照明 lighting 50 照度 illumination 51 应急紧急情况 emergency 52 净化 purification, clean 53 微生物, 微生物学, 微生物 micro-organism,microbiology,microbiologic的 54 监测 monitoring 55 记录 record 56 天棚天花板 ceiling, roof 57 密封 seal 58 静压差 Static DifferentialPressure 59 温度 temperature 60 相对湿度 RH: Relative Humidity 61 低漏地漏 floor drainer 62 青霉素penicillin 63 分装室 separating room, fillingroom 64 相对负压 relative negativepressure 65 废气 waste gas,e_hausted air 66 β－内酰胺结构类药品 β-Lactasestructure drug, drugs of β-Lactic group 67 避孕药品 contraceptives 68 激素类 hormone 69 抗肿瘤类 anti-tumor, oncology 70 放射性药品 Radiopharmaceuticals 71 包装 packing, package 72 循环使用recycling 73 微粒 particles 74 辐射 radiation, irradiation 75 细菌bacteria 76 病毒 virus 77 细胞 cell 78 脱毒前后 pre and postdeto_ification 79 活疫苗与灭活疫苗 activevaccine/inactivatedvaccine 80 人血液制品 blood products 81 预防制品prevention products82 灌装 filling 83 中药 Chinesetraditional medicines 84 前处理pretreatment 85 提取 e_traction 86 浓缩 concentration 87 动物脏器viscera of animal,organ ofanimal 88 蒸、炒、炙、煅 ing, frying,sunburn, testing 89 炮制concocted 90 通风 ventilation 91 除烟 smoke removal 92 除尘 dust removal 93 降温设施 temperature-reducingestablishment,cooling 94 筛选 screening, sift 95 切片 slicing 96 粉碎 grinding 97 压缩空气 pressed air 98 惰性气体 noble gas 99 取样 Sling 100 称量室weighing room, dispensingroom 中药标本 Chinese herbalsle,e_emplar of TCM 102 检定鉴定 verification, identification 103 同位素 Isoe 104 设备 equipment 105 选型 model/type selection 106 耐腐蚀anticorrosion 107 吸附 adsorption, absorption 108 润滑剂, 润滑 lubricant, lubricate 109 冷却剂 coolant 110 流向 flow direction111 纯化水 PW: Purified Water 112 注射用水 WFI: Water for Injection 113 滋生 breeding 114 储罐 tank 115 死角 neglected portion 116 盲管blind pipe 117 纤维 fiber 118 疏水性 hydrophobicity 119 仪表instrumentation 120 量具 measuring tool 121 衡器 weighing instrument 122 精密度 precision 123 维修 maintenance 124 不合格 disqualified reject 125 物料 material 126 购买 purchasing 127 发放 releasing 128 产地 origin 129 入库 loading 130 固体 solid 131 液体 liquid 132 挥发性 volatile 133 净药材 medicine, TCM 134 麻醉药品 narcotics 135 精神药品 psychotropic drug 136 易燃 bustible 137 易爆 e_plosive 138 验收 acceptance 139 使用说明书instruction140 标签 label 141 卫生, 清洁/消毒 sanitation 142 车间, 辅房workshop 143 间隔时间 time interval 144 清洁剂 detergent 145 消毒剂disinfectant 146 废弃物 wastes 147 更衣室 changing room 148 工作服, work clothes 149 颗粒性物质, 颗粒剂 granules 150 耐药菌株 drug-resistantstrain 151 传染病 infectiousdisease 152 皮肤病 dermatitis 153 验证verification, validation 154 确认 qualification 155 安装 installation156 运行 running operation 157 性能 performance 158 原辅料 raw material and incipient 159 文件 document 160 投诉 plaint 161 报废 reject 162 品名product name 163 处方 preion, formula 164 技术参数 technicalparameter165 容器 container 166 半成品 semi-finished product,intermediate 167 申请 lication 168 稳定性 stability169 起草 draft 170 生产管理 production management,manufacturing control.171 事故 accident 172 混淆 mi_-up 173 喷雾 spray 174 合格证certificate 175 清场 clearance 176 质量管理 quality management 177 内控internal control,on-line test 178 滴定液 tartan 179 培养基 medium 180 有效期 validity, e_piry date,shelf life 181 产品销售与收回 product sales andrecovery/recall 182 投诉与不良反应报告 plaints and adversereaction 自检self-inspection 184 附则 schedule endi_ 185 平衡 balance 186 饮用水drinking water, potablewater 187 蒸馏法 distillation 188 离子交换法 ion e_change 189 反渗透法 RO: Reverse Osmosis 190 附加剂添加剂 additives 191 滞留 stranded resort 192 批 batch, lot 193 组分, 组成 ponent 194 无纤维脱落的过滤器 non-fiber-releasingfilter 195 活性成份 Active Ingredient 196 非活性成份 Inactive ingredient 197 中间产品 in-processproduct,intermediate product198 批号 batch number 199 药用物料 medicated feed 20__药用预混合料 medicated premi_ 201 质量控制部门 Quality control department 202 理论产量 Theoretical yield 203 实际产量 Actual yield 204 比率 Percentage, rate 205 验收标准可接受标准 Acceptance criteria 206 代表性样品 Representative sle 207 微粒状的 particulate 208 污染物contaminant 209 石棉 asbestos 210 诊断 diagnosis 211 缓解 mitigation 212 化学变化 chemical change 213 组分 ingredient, ponent 214 制备 fabricate preparation 215 复合 pound 216 混合 blend 217 加工 processing 218 浓度concentration 219 单位剂量 unit dose 220 药品包装容器 drug product containers 221 密封件, 封盖 closure 222 效价 Titer 223 纯度 purity 224 规格 strength 225 监督 supervise, monitor 226 实验室 laboratory 227 无菌操作 aseptic operation,sterileoperation 228 层流 laminar flow 229 湍流 turbulent air flow 230 空气过滤 air filtration 231 空气加热 air heating 232 预过滤器 profiler 233 排气系统 e_haust system 234 管件 plumbing 235 虹吸倒流 back-siphon age 236 污水 sewage 237 废料 refuse 238 盥洗设备 toilet facilities 239 空气干燥器 air drier 240 垃圾 trash 241 有机废料 organic waste 242 杀鼠剂rodenticides 243 杀昆虫剂 insecticides 244 杀真菌剂 fungicides 245 熏蒸剂 fumigating reagents 246 去垢剂 cleaning agents 247 消毒剂 sanitizing agents 248 滂沱剂 lubricant 249 自动化设备 automatic, mechanical,or electronic equipment 250 微型胶卷 microfilm 251 注射剂 injection 252 灭菌设备 sterilization equipment 253 无菌取样技术 aseptic sling techniques 254 显微镜 microscope 255 热, 内毒素 pyrogen, endoto_in 256 偏差 deviation 257 变更 change control 258 进料 charge-in 259 项目代码 item code 260 鉴别 identify 261 片剂 tablet 262 胶囊 capsule 263 颗粒剂 granule 264 溶解时间溶出时间 dissolution time 265 澄明度clarity 266 隔离系统 quarantinesystem, isolation system 267 返工reprocessing 268 发放 issuance, release 269 非处方药 OTC:over-the-counter 270 处方药 preed medicine 271 皮肤科药、牙粉、胰岛素、喉片dermatological,dentifrice,insulin, or throat lozenge product 272 保险包装 ter-resistant package 273 明胶硬胶囊 hard gelatin capsule 274 顺势治疗homeopathic 275 入库 warehousing 276 变质 deteriorate 277 准确性accuracy 278 灵敏性 sensitivity 279 特异性 specificity 280 重复性reproducibility, repeatability 281 变应原提取物 allergenic e_tracts 282 眼膏 ophthalmic ointment 283 粗糙或磨蚀物质 harsh or abrasivesubstances 284 控释制剂 controlled-releasedosage form 285 实验动物 laboratory animals 286 供应商 Supplier 287 光谱 spectrum 288 测量单位 units of measure 289 换算系数 conversion factors 290 试剂 reagent 291 安慰剂placebo 292 明确地 e_plicitly 293 取代 supersede 294 溶液 solution 295 批准 roval 296 (美国)食品药品监督管理局 FDA: Food and DrugAdministration 297 标准操作程序 SOP: StandardOperatingProcedure 298 质量保证 QA: Quality Assurance 299 质量控制QC:Quality Control 300 批生产记录 BPR: Batch ProductionRecord 301 批检验记录 BAR: Batch AnalysisRecord 302 工艺规程 PP: Process Procedure 303 健康,安全,环保 EHS: Environment,Health andSafe 304 美国联邦法规 CFR: Code of FederalRegulation 305 美国药典USP: The UnitedStatesPharmacopeia 306 欧洲药典 EP: European pharmacopeia 307 英国药典 BP: British pharmacopeia 308 药物主文件 DMF: Drug Master File 309 验证主计划 VMP: Validation MasterPlan 310 验证方案 VP: Validation Protocol 311 验证报告 : Validation Report 312 安装确认 IQ: Installation Qualification313 运行确认 OQ: Operation Qualification 314 性能确认 PQ: Performance Qualification 315 超出标准(限度) OOS: Out of Specification 316 冻干产品 freeze-dry product,lyophilizated product 317 工厂主述文件SMF: Site Master File。

geoscientific model development

geoscientific model developmentGeoscientific model development refers to the development of models that simulate the physical, chemical, and biological processes occurring in an area of the Earth's landscape. This modelling is done with a combination of data and computer simulations to create a model of the area that can be used to make predictions regarding the future conditions of the area, as well as to assess the impact of human activities, such as land use and energy sources, on the environment.Geoscientific models are used in a variety of applications and research, including environmental decision-making, climate forecasting, resource exploration and management, and engineering and planning. For example, geoscientific models have been used to predict the impacts of climate change on global sea levels, identify population trends in different parts of the world, develop land use plans for sustainable development, and assess potential earthquake hazards.Developing an effective geoscientific model requires the interdisciplinary integration of data from several sources. This includes geospatial data like topography, hydrology, and climate, as well as observational data from satellites, aircraft, and other sources. The data is then used to create a 3D computer representation of the area, which is then used to simulate the various processes and interactions thataffect the area.Geoscientific modelling is constantly evolving andbecoming increasingly sophisticated. As computer power and capabilities continue to improve, so too do the modelling techniques and algorithms used to create geoscientific models. This is allowing for more realistic simulations of theEarth's environment, which will improve our understanding of the impacts of human activities on the planet.。

苹果开发者指南说明书

A■alloc/copy method, 286Animator proxy, 325Apple Developer Connection (ADC), 2 applicationDidFinishLaunching:method, 94, 284, 287, 290 applicationDidFinishLaunching: delegatemethod, 273 applicationWillTerminate: method, 223 Assistant editor, 332Attributes, 156details attribute, 159divinity, goodness, 159editing, 159MythicalPerson entity, 158Optional, 158power, 159Transient, 158Unsupported Types, 160Attributes Inspector, 229, 306, 333, 354, 360 B■Bindings Inspector, 307C■Cappuccino, 384, 387Catching exceptions, 272 CMColorBlendView classblended colors to GUIbinding configuration, 266bind:toObject:withKeyPath:, 266@class declaration, 262CMDocument.h file, 262CMDocument.m, 265final output, 267#imports, 262list of connections, 264–265Size Inspector, 262soon-to-be-blended colors grid, 263–264windowControllerDidLoadNib:method, 265CGColorRefs, 261CMColorBlendView.m, 259drawRect: method, 260drawRect: mode, 259editing CMColorBlendView.h, 259@implementation section, 260@implementation, 261nonatomic, 259NSColor objects, 259subclass of NSView class, 259@synthesize, 259–260CMDocument class, 257Cocoaclasses, 1drawingautomatic reference counting, 316Bezier curve, 315–316Bezier plumbing, 318–319CEAppDelegate.h file, 316CECurveView, 316, 318control point, 323–324core animation (see Core animation)curve drawing, 320–321CurveEdit, 316custom view, 316frame rectangle vs. boundsrectangle, 296–297fundamentals, 295–296.h file, 317LOLmaker (see LOLmaker).m file, 317Index389mouse activity, 321–323NSRect, 297NSSize, 297NSView subclass (see NSView subclass)paths, 297view coordinate system, 296 site, 3Mac application, 2NeXTStep AppKit, 1Objective-C, 3object-oriented frameworks, 1OS X uses, 2programming knowledge, 3source code, 2Xcode, 2Cocoa bindingsDungeonThing project and preferenceswindowAttributes Inspector, 126Character Generationpreferences, 127Dungeon Generationpreferences, 129MainMenu.xib, 126Monster Generation preferences, 129Object Library pane, 126Tab View addition, 127Main Window creationAttributes Inspector, 134DungeonThingAppDelegate(see DungeonThingAppDelegate)Object Library pane, 134Size Inspector, 134text field creation, 134NSUserDefaultsControllerNSUserDefaultsController(see NSUserDefaultsController) Table ViewArray Controller, 145configuration, 143DungeonThingAppDelegate.h, 142DungeonThingAppDelegate.m file, 142init method, 142key-value coding, 149key-value observing, 150table display, 147text field, 147Cocoa skillsblocksfiltering, 381Grand Central Dispatch, 377init and dealloc methods, 379MyController instance, 380notification-handling code, 379NSEnumerator, 378–379NSNotification, 379observation, 379observation object, 380weakSelf, 381foreign languageF-Script, 385JavaScript, 384–385MacRuby, 383Nu, 384Objective-C and C++, 382PyObjC, 382GUI objects, 375MVC pattern, 375NSNotification, 376, 377ported (see Ported Cocoa)Cocoa Touch, 385–386Cocotron, 386ColorMix application, 253–254blend modes, 254CMColorBlendView class(see CMColorBlendView class) CMDocument class, 254Core Data, 254Core Graphics, 254data model, 255file format, 258nib files, 255NSDocument architecture, 254setting two colorsColorSet creation, 257GUI creation, 256NSObjectController, 256NSTextField, 255undo and redo, 268ColorSet object, 256, 257ConcurrencyGCD (see Grand Central Dispatch (GCD)) SlowWorkerdefinition, 358doWork: method, 364Cocoa (cont.)Editable checkbox, 360inaction, 358isWorking, 359Mac OS X’s Force Quit window, 361NSBlockOperation, 365single action method, 364SWAppDelegate.h, 358SWAppDelegate.m, 359threadAppKit classes, 365Attributes Inspector, 368closures, 362doWork: method, 366GCD, 363Indeterminate Progress Indicator, 369main thread, 361mutex, 361NSTextView, 365operating systems, 361operation queues, 362Start button: method, 367SWAppDelegate.h, 368SWAppDelegate.m, 368thread-safe, 361Units of Work, 362Value Transformer, 368 contentView, 296Core animationbasics, 325explicit animationsanimation layers, 328animator proxy, 328CABasicAnimation class, 327, 329CECurveView, 330–331MovingButton target, 329NSPanel, 331QuartzCore framework, 327timing function, 332grouping animationsANIM_DURATION, 335applicationDidFinishLaunching:method, 336–337assistant editor, 332attributes inspector, 333currentTabIndex, 336display window, 333FIAppDelegate.h file, 335FIAppDelegate.m class, 335FlipIt, 332@implementation block, 336items property, 336matching methods, 339–340NSArray, 336NSBox, 333NSInteger scalar property, 335NSTabView, 334NSView subclass, 334Object dock, 334prepareRightSide method, 336transitionInFromRightmethod, 336–337transitionOutToLeft method, 338transitions, 332implicit animations, 325–326Core databusiness logicCustom Attribute creation, 186Multiple Attributes validation, 184MythicalPerson class, 182Single Attributes validation, 183 CocoaBindings Inspector, 166managedObjectContext, 166MBAppDelegate, 166Model Key Path field, 167NSArrayController, 166NSImage, 168NSMutableDictionary, 166predicates, 171saveAction, 171table view, 168GUIAttributes Inspector, 161, 164Column Sizing, 161MainMenu.xib file, 161Mythical Details management, 164NSScrollView, 164NSTextField, 165NSTextView, 164Object Library, 161–162table view, 162integration with Cocoa bindings, 154MythBase creationattributes (see Attributes)Automatic ReferenceCounting, 155entities, 156Entity creation, 157relationships, 156Xcode’s model editor, 157NSMutableDictionary, 153persistence, 154template vode (see Template code)undo/redo support, 154Core data relationshipsArray Controller, 200entity modelattributes, 192configuration options, 194Delete Rule pop-up, 194destination pop-up, 194migration, 195multiple model versions, 191MythBase, 190one-to-many relationship, 193, 195run, 195to-many relationship, 194–195versioning and migrations, 191 GUI updationarrangedObjects, 201Attributes Inspector, 203band Grecian Formula, 202band window, 197Bindings Inspector, 201, 203–204Cocoa bindings, 198–199Content Mode, 203Gig List, 205Model Key Path, 202Mythical Bands controller, 201Mythical Bands window, 204Mythical People window, 200–201NSArrayController, 203pop-up button, 200venue window, 204MythBase application, 190D■DATA_RECEIVED notification, 376 dealloc method, 377, 379dispatch_async, 371dispatch_get_global_queue() function, 372 Distributed Objects (DO) technology, 283Document-based applicationColorMix (see ColorMix application)NSDocument class, 253doWork: method, 364 DungeonThingAppDelegateaction methods, 138constants definition, 136default preferences values specification, 137 E■ECAppDelegate, 273 enumerateKeysAndObjectsUsingBlock:, 379 enumerateObjectsUsingBlock:, 378 esNSDocumentController class, 253 Exception handlingcatching exceptions, 272Cocoa, 273debugger, 275Debug Navigator, 277definition, 271ECAppDelegate, 273invalidArgumentException_unrecognizedSelector method, 275 NSException class, 271NSInvalidArgumentException, 279–281NSRangeException, 282–283objc_exception_throw function, 278rax, 279Xcode’s Breakpoint Navigator, 276F■fileError method, 290File handlinghigh-level file operation (seeWhatAboutThatFile application) implicit file accessclasses, 341content interpretation, 341NSData, 342NSPropertyListSerialization class, 342property-list format, 341writeToFile, 342File-system attributes, 342FlipIt, 332freedObject method, 287F-Script, 385Core data (cont.)G■GNUstep, 386Grand Central Dispatch (GCD)NSOperationQueue, 370SlowWorkerconcurrent Blocks, 373dispatch_get_main_queue( )function, 372SlowWorker’s doWork: method, 370, 372 GUI component, 67Cocoa UI elements, 67–68codingdefault villain, 93input, 99key names, 92–93updateDetailViews(see updateDetailViews)NSButton, 68NSControl, 67VillainTrackerAppDelegate classconnections inspector, 90delegate method, 90GUI class, 85notesView property, 91NSMutableDictionary, 91outlet/action, 85–87run button, 92self.villain, 92setVillain, 91takeLastKnownLocation, 88–89takeLastSeenDate, 85VillainTracker applicationbox view, 81check box, 78combo box, 73date picker, 72image view, 77level indicator, 74MainMenu.xib, 70MVC design, 69NSView, 69pop-up button, 79radio buttons, 75resizing, 83text field, 70text view, 80VillainTracker.xcodeproj, 70H■Human Interface Guidelines (HIG), 14I■Identity inspector, 298, 306@implementation block, 257Indeterminate Progress Indicator, 369@interface block, 257init method, 257initWithObjectsAndKeys method, 93 initWithType: error, 258 invalidArgumentException_unrecognizedSelector method, 275, 281 isWorking, 359J■JavaScript, 384–385JSCocoa, 384K■keyPathsForValuesAffectingFileIcon class, 345 keyPathsForValuesAffectingFilenameclass, 345L■localizedDescription, 291LOLmakerattributes inspector, 306bindings inspector, 307bitmap drawing, 309–310identity inspector, 306LOLAppDelegate.h file, 306LOLAppDelegate.m, 307LOLcat-style imagery, 305LOLView, 308–309scrolling, 310–312text drawing, 312–313Value attribute, 307window, 306–307M■MacRuby, 383MacRuby Language, 383MainMenu.xib, 360, 367Mission Control, 230Modal Windowsalert function, 242open and save panels, 243Run Modal Alerts, 243Model–view–controller (MVC), 29 Mutex, 361MyController class, 259MythBase, 209N■nib-defined predicate, 218Nib fileAttributes Inspectoralignment buttons, 18font and size, 18Interface Builder, 16–17label’s attributes, 17Mac OS X color picker, 18–19NSTextField class, 17system-default font, 19–20blue guidelines, 13–14Editor, 11Inspectorattributes, 14definition, 14Interface Builder’s Inspectors, 15user interface, 11Interface Builder mode, 11Label, 12–13Library, 11–12 NSArrayController, 211, 213, 216, 218 NSArray’sindexesOfObjectsPassingTest: method, 381 NSCocoaErrorDomain, 289 NSDocument class, 253 NSDocumentController class, 253, 255 NSErrorapplicationDidFinishLaunching: method domains and codes, 288–289error-generating method, 290fileError method, 290fileManager, 291file-related errors, 288localizedDescription, 291NSFileManager class, 289NSString, 291po command, 292presentError: method, 293 NSInvalidArgumentException, 279–281 NSManagedObjectContextobject, 269NSOperation, 362 NSOperationQueue, 362 NSOSStatusErrorDomain, 289 NSPersistentDocument, 257 NSPOSIXErrorDomain, 289NSPredicatecreation, 216NSAppController, Xcode, 218QuoteMonger (see QuoteMonger)saving, 222user-defined predicatesapp delegate, 219editor configuration, 220nib-defined predicate, 218NSPredicateEditor, 218Search window, 220 NSPredicateEditor, 209, 218 NSPredicateEditorRowTemplate, 221 NSPropertyListSerializationclass, 342 NSRangeException, 282–283NSRect, 297NSTableCellView, 108NSTextField class, 17NSUserDefaults, 222–223 NSUserDefaultsControllercharacter generationcharacterClassAllowedBard,Fighter, 133characterClassAllowedPaladin key, 133characterMaxNameLength, 132Max Value, 130Min Value, 130NSUserDefaults, 132NSUserDefaultsController, 132Selected Tag attribute, 133Dungeon Generation, 133Monster Generation, 133NSView subclassboundariesinitWithFrame: method, 304resizing, 303–304setFrameSize: method, 304–305stretching, 305view position and size, 304 CGRectInset, 301drawRect: method, 298, 301graphics context, 300graphics states, 299identity inspector, 298MainMenu.xib file, 298manual path construction, 302path helpers, 299size inspector, 298NSWindow and NSPanelApple’s Human InterfaceGuidelines, 226input handling, 227panel use, 228System Panels (see System Panels) Window Attributes, 228windows sample, 227 NSWindowControllerNib loadingARC system, 237Easy Window, 238File’s Owner, 238Load Easy Window, 237subclassing, 239NSWorkspace class, 345Nu language, 384O■objc_exception_throw function, 278 objectAtIndex: method, 282 objectController, 257–258Objective-C compilercontroller class creationAssistant Editor, 59ButtonAppDelegate class, 59ButtonsAppDelegate.m, 60control-dragging, 59–60implementation, 61label property, 60nib file, 59Xcode, 59delegate applicationButtonsAppDelegate class, 63configuration, 63Documentation Browser, 64main( ) function, 62NSApplication, 62outlet and actionAttributes Inspector, 52Autosave field, 53Cocoa application, 49, 50control-dragging, 48IBAction, 48I-beam, 55IBOutlet, 47interface builder, 49MainMenu.xib, 50method, 48Minimize checkbox control, 53nameField, 47placeholder object, 51Resize control, 53Size Inspector, 53–54Window Interface (see WindowInterface)Xcode, 48Objective-J, 387P■po command, 292Ported CocoaCappuccino/Objective-J, 387Cocoa Touch, 385–386GNUstep and Cocotron, 386Mac software, 385 presentError: method, 293PyObjC Language, 382Q■QMAppDelegate.h file, 219 QMAppDelegate.m, 219, 222 Quartz, 295Quote entity, 210QuoteMongerdata entry window, 209–210initial quotes, 214MainMenu.xib, 211NSWindow instance, 211Quote entity, 211Quote Finder window, 215quoting Quotes, 213Show entity, 211showing Shows, 211project and data model creation, 210 search window, 209–210R■Responder chain, 233 resultsTextView property, 359S■searchPredicate property, 219 setName: method, 269Sheets, 251Show entity, 210Signalalloc/copy method, 286applicationDidFinishLaunching:method, 284, 287ARC, 286Cocoa programmers, 284freedObject method, 287NSMutableString*, 284Objective-C object, 284release or autoreleasemessage, 287SIGSEGV/SIGILL, 285Size Inspector, 298SlowWorker, 358, 361 stringEncodingName method, 347 SWAppDelegate.h, 358, 368 SWAppDelegate.m, 359, 368System MenusbindingsBoolean attribute, 245menu items setting up, 247turbo property, 246Value binding, 247Value Transformer, 247first responderaction method, 250flowchart, 248menu items, 249new window creation, 249Object Library, 250responder chain, 248own menus, 245single horizontal strip, 245standard application menu, 245Windows application, 245System Panelscolor panelMultiline Label, 232responder chain, 233Show Color Panel, 232Text Color, 234window layout, 231Font Panel, 234T■Table viewaddition and deletion method, 103Attributes Inspector, 105–106code editioncolumn identifier method, 118dataSource and delegate outlet, 117delegate method, 116delete villain: method, 120edition, 122isEqual: method, 121lazy loading, 117method, 118Project Navigator, 116selectRowIndices, 116Villains addition, 118Villains selection, 119VillainTrackerAppDelegate.m, 117 Column Size, 106Content Mode, 107data collection, 103Identity Inspector, 108Image combo box, 108mugshot, 108NSAddTemplate, 108NSTableColumn, 108Object Library, 106, 108Project Navigator panel, 105resize/constraints, 109appearance box, 113–114blue line guidelines, 111Cocoa Auto Layout, 111Content Hugging control, 111edition, 114–115QuoteMonger (cont.)interface builder pane, 112myButton, 111resize handle, 110scrollview selection, 107Table View Cell, 108VillainTrackerAppDelegate, 109VillainTrackerAppDelegate class’sinterface, 103VillainTrackerAppDelegate preparation, 104 Template codeApp Delegate implementationaction method, 179applicationSupportDirectorymethod, 174managedObjectContext accessormethod, 178managedObjectModel accessormethod, 175NSApplication delegate method, 180NSWindow delegate method, 179persistentStoreCoordinator accessormethod, 176App Delegate interface, 173U■UILabel object, 256Undo stack, 268updateDetailViewsapplicationDidFinishLaunchingmethod, 96evilnessView, 95fast enumeration, 98lazy loading, 96powers, 98primaryMotivation, 96–98setStringValue:, 94swornEnemy, 95User-interface control, 107User interfacescontroller classes, 30frameworksAppKit, 28Cocoa framework, 28definition, 27foundation framework, 28MVC model, 29OS X unique, 27outlets and actionsaction method, 30akeIntValueFrom, 43–44Attributes Inspector, 33, 36BookAppDelegate, 31Cocoa Simulator, 43Connections Inspector, 41–42control-dragging, 39–40integral values, 92Interface Builder mode, 32label properties, 33laying out User Interface controls, 35NSTextField class, 34Object Library, 33–35outlets definition, 30project settings, 31–32resize, 37–38slider, 37–39takeDoubleValueFrom, 42, 44Text Fields, 35–36View section, 33Xcode, 31V■VillainTrackerAppDelegate classconnections inspector, 90delegate method, 90GUI class, 85notesView property, 91NSMutableDictionary, 91outlet/action, 85–87run button, 92self.villain, 92setVillain, 91takeLastKnownLocation, 88–89takeLastSeenDate, 85VillainTracker applicationbox view, 81check box, 78combo box, 73date picker, 72image view, 77level indicator, 74MainMenu.xib, 70MVC design, 69NSView, 69pop-up button, 79radio buttons, 75resizing, 83text field, 70text view, 80VillainTracker.xcodeproj, 70W■weakSelf, 381WhatAboutThatFile applicationcharacter string, 343codeapplicationDidFinishLaunchingmethod, 344chooseFile method, 344chosenEncoding property, 344Cocoa bindings, 344encodingNames method, 346error checking, 348error handling, 344fileAttributes, 346keyPathsForValuesAffectingFileIcon, 345keyPathsForValuesAffectingFilename, 345MainMenu.xib, 343NSString, 346NSWorkspace class, 345setStringEncodingName method, 348stringEncodingName method, 347WATAppDelegate class, 343GUIarrangedObjects controller key, 353file attributes, 352file selection, 350–351MainMenu.xib, 349NSDictionaryController class, 353Opens Application, 351–352pop-up button, 355string encoding, 354text view, 353–354window components, 349 windowControllerDidLoadNib:method, 257–258Window Interfaceblue guidelines, 56–57font panel, 58GUI object, 58Interface Builder pane, 58Object Library, 55–56writeToFile method, 342X, Y, Z■Xcodearchive build process, 24–25Cocoa Application icon, 5–6Editor pane, 9icon application, 20–22Mac App Store, 6MainMenu.xib., 10Navigator pane, 9nib file (see Nib file)project options, 6–7project’s main window, 8–9property list, 22–23run, 23–24save location, 7–8XML-based format, 10VillainTracker application (cont.)。

产品开发管理系统简介PDMS Introduction

Issue DCR
D
DVT Test Application Feedback
Significant Change Back to P2
C
PCBA V.A. Proto. T1 Tooling Design
PCBA Debug / Modify T1 Sample Modification Signal Integrity Test Certification Pre-test PreEMC / Safety Modification
PM • Market Requirement Spec. (001) • Product Feasibility Report (003) • Product Roadmap (004)
Strategic Meeting (PS)
NO GO
YES GO
A
Abandon
March 16, 08 Version 1.0
March 16, 08 Version 1.0
產品開發管理簡介
p.1 p.1
產品開發管理系統簡介
P System
P0 Proposal P1 Product Management Planning P2 R&D Design P3 LPR / DVT ENG
P4 EPR / EVT P5 PPR / MVT
Design Guideline DFX, Design Guide
B
Product Engineering Specification
Circuit Schematics 2D / 3D Drawings CAD Simulation Part List / E-BOM E-
R&D Internal Design Review (R&D)

Pipette Calibration Solutions ISO8655 Compliant 制品

Calibrating Pipettes P i p e t t e C a l i b r a t i o n2P i p e t t e C a l i b r a t i o nEfficient Calibration of Pipettes for Fast & Traceable ResultsWhether you’re an accredited in-house lab or service provider, pipettecalibrations must be performed accurately, efficiently and must fulfill regu-latory guidelines such as ISO8655.METTLER TOLEDO offers comprehensive solutions for pipette calibration.Our reputation for weighing, pipettes and process-oriented software, combined with our expertise of over 20 METTLER TOLDEO calibration labs worldwide results in a user-oriented, efficient and compliant calibration portfolio like no other manufacturer.of the pipette after calibration and adjustment .Calibration Cert iﬁc ateIssue a calibration cert iﬁcate as proof that the pipette has been tested and adjusted.The Calibration ProcessMETTLER TOLEDO's weighing reputation combined with process oriented software, results in an efficient, reliable and compliant pipette calibration process according to ISO8655.3/pipcalISO 8655 compliantISO 17025 accredited labs cali-brate according to ISO8655 for volumetric devices. Our solutions are fully compliant from the balance for measurements to software, with reports for a fast and secure process.4Fast Micro Pipette Calibration Down to 1 μl VolumesCalibration of smallest volumes down to 1 μl are a challenge to do accu -rately and efficiently. The XPE26PC was designed by specialists and is the benchmark in many calibration laboratories world-wide.The micro balance weighing cell, combined with the motion sensor lid and large evaporation trap enable a focused workflow with accurate results right first time. Additional functions such as the StatusLight, the large waste container and optional sensors support an efficient calib-ration process with trusted results.P i p e t t e C a l i b r a t i o nFast & efficient Continuous calibration Unique durable designThe motion sensor lid and large 80 ml evaporation trap of the XPE26PC ensure fewer handling steps and fast, stable results even when calibrating micro pipettes down to 1 μl according to ISO 8655.The large 10 ml container allows many calibrations in a largerange from 1 μl or higher without interruption. A suction pump is included to easily empty the waste water container.Thanks to the unique hanging weighing pan design, the bottom plate is completely sealed and prevents water or fluids dripping into the balance mechanics and weighing cell, for a durable andconsistent lifetime performance.5/pipcalValid results Improve ergonomics Stability increases speedThe built-in StatusLight™ uses color to indicate intuitively the status of the balance. The green light indicates clearly that the balance is ready for you to begin your next calibration, so you can be sure your results will be valid.Optimize your process with an optional motion sensor which can be placed freely on the bench for touchless and conta-mination-free operation of the balance.An optional dual framed weig-hing table reduces to a minimum any user or environmental vibra-tions, substantially speeding up measurement times. With this complete solution, pipette calib-ration is faster than ever.6Calibrate from 20 μl to 10 ml on One BalanceThe XPE analytical balances with professional evaporation traps make it possible to calibrate volumes from 20 μl to 10 ml with just one balance, according to ISO8655 quickly and efficiently.In addition, the unique design with hanging pan and detachable draft shield facilitate optimal working ergonomics and easy cleaning, without the risk of fluids or dirt entering the weighing cell.P i p e t t e C a l i b r a t i o nFrom 2 μl to 2000 μl From 2 ml to 10 ml Simply adapt to your needsGlass evaporation traps enable fast calibrations from 20 microli-ters to 2 milliliters, thanks to the stable setup, good visibility and independent waste container.Simply switch the trap to cali-brate volumes from 2 to 10milliliters. The metal 100 milliliter evaporation container is easily exchanged on the XPE analytical balance.The terminal, motion sensor doors and draft shield can simply be detached anytime to improve access and ergonomicsor just to make cleaning easier.7/pipcalUnique construction Easy Leveling Training DayThanks to the unique hanging waste container, the base is completely sealed which pre-vents dust, fluids and powder from entering the weighing cell ensuring a lasting performance of your balance.The LevelGuide™ provides you with a warning when the XPE balance is not leveled. Full inst-ructions and a graphical leveling bubble are shown on the touch-screen so you can proceed your calibration within e the built-in training module and Pipette Check to train users or check pipettes independently of the system. Users are guided through the process and their individual performance can betracked.8Efficient and Compliant Calibrations with Calibry SoftwareCalibration task planning, guided calibration processes, collecting data, generating reports and being compliant at the same time, these are the benefits of using Calibry Software – effiency personified.Calibry was developed with calibration professionals, who know what is important and necessary in accredited calibration laboratories. With over decades of experience and continuous improvements, Calibry is an established benchmark for many calibration laboratories all over the world.P i p e t t e C a l i b r a t i o nGuided process Complaint and traceable Clever SolutionsCalibry software supports all steps of the calibration process. The large cockpit screen during calibration ensures an easy and secure calibration process of all pipettes.Calibry software saves all calib-ration data in a secure SQL data-base, and ensures compliance with ISO8655 regulations and reports.RFID Pipettes from Rainin need only to be scanned to initiate a task overview or to start a calib-ration directly.9Customized reports 21CFR Part 11 supportive Software validationISO8655 compliant and compre-hensive reports can be generated and adapted to specific needs. If required all data can be exported and used to generate custom specific reports as well.For regulated areas Calibry contains many FDA 21 CFR Part 11supportive features, such as user management, report release, method change history and audit trail.The software validation binder saves valueable time for prepa-ration and validation process. Due to the prefilled information and templates, software releases are completed quickly and more accurately./pipcal10Pipette Calibration Systems Technical InformationCalibration SystemXPE26PCCalibration SystemXPE205Calibration range according ISO8655≥ 1µL – 1'000µL > 10 µL – 10'000 µLBalance type XPE26PC XPE205Evaporation trapBuilt-in 80 mlOptional: 20 ml - 11140043Optional:100 ml - 11138440Motion Sensor Lid opening •-Motion Sensor Draft Shield doors-•Hanging Pan design with closed bottom plate for safe cleaning ••Detachable draft shield -•Detachable terminal ••LevelControl ••StatusLight••ProFact internal Temp. & Time adjustment ••Extra Motion Sensor and/or Footswitch Light Barrier - 11140029ErgoSens - 11132601Footswitch - 11106741Footswitch - 11106741Waste water suction pump •Optional 11138268CarePac Test Weight Set Optional - 11123006Optional - 11123008Calibry Software compatability ••Weighing tableOptional - 11138041Optional - 11138042• included / - not applicableBuilt your own Calibration System that suits your needs.A Calibration System is based on an XPE balance with optional accessories to improve your calibration process. Every balance can be connected to Calibry Software for secure data capture.T e c h n i c a l S p e c i f i c a t i o ns11Model XPE26PC XPE205Order no.3010590130087653Typical Pipette Calibration Range* 1 - 200 µl 20 - 10'000 µl Weighing range 0 - 22 g 0 - 220 g Readability 0.001 mg 0.01 mg Repeatability 0.0015 mg 0.015 mg*) according ISO8655Balance AccessoriesItem Order no.Item Order No.RFID Reader for Calibry Software 30215407Motion Sensor typ "Light Barrier"11140029USB-RS232 Cable 30091827FootSwitch 11106741Ethernet Interface 11132515Weighing Table XPE26PC 11138041RS232 Interface 11132500Weighing Table XPE20511138042Suction pump with tubes 11138268CarePac S Testweight set 1g / 20g 11123006Motion Sensor typ ErgoSens 11132601CarePac S Testweight set 10g / 200g 11123001Pipette Data Management Software functionalities Calibry Single Station Calibry NetworkSoftware package, 1 balance license included 1113841911138420Windows 7 (SP1), 8 and 10 compatible ••Number of max.balances supported 520Databases to manage Pipettes, Methods and Customers ••Calibration Task Planning with reminder ••Calibration report with customization ••ISO8655 compliant and free definable Methods ••Calibration Methods with split, As Found, Maintenance, As Return data••Pipette History data and trending of performance ••Z-factor according to ISO8655, TR or custom with formula editor••Advanced settings for additional uncertainty calculations ••RFID Calibration & Service tracking with MethodCards, SmartTags and Rainin pipettes••System Testing with balance internal & test weights ••Calibry License OptionsInstrument License 1 - to connect 1 additional balance 3041576830415768Instrument License 3 - to connect 3 additional balance 3041732630417326Instrument License 5 - to connect 5 additional balances -30417327Instrument License 1 - to connect 1 other brand balance 3042138230421382User Management License - to manage user rights 30415770included Audit Trail License - to document all system changes 3041577130415771Export File license - - to export in XML and MS Office 30417468included Capture Tool License - to automatically integrate allenviromental data 3041576930415769Evaporation trapsModel Evaporation Trap 10ml Evaporation Trap 20 ml Evaporation Trap 100 ml Order no.111400411114004311138440Compatible with XPE 26 / 56 Micro balance XS / XPE Analytical balance XS /XPE Analytical balance Typical Calibration Range 1 - 200 µl 20 - 1'000 µl 100 - 10'000 ul Volume container 10 ml 6 ml & 20 ml 100 ml Material Aluminium / POM-esd Aluminium / GlassAluminium / POM-esdMETTLER TOLEDO GroupLaboratory DivisionLocal contact: /contactsSubject to technical changes© 10/2017 METTLER-TOLEDO. All rights reserved30393683Global MarCom 2280 LK/PH /pipcalExpect more from usWith decades of experience in pipetting & weighing METTLER TOLEDO can offer you a wide range of online learning resources. Take advantage of our expertise to enhance your know-how and make the most out of your equipment. Check out the services on our internet for a wide range of relevant services and professional solutions we offer.Installation and QualificationTake the stress-free route to meet your producti-vity, quality and regulatory requirements imme-diately. Your specific needs can be addressedby choosing the most suitable option from our comprehensive service offering./serviceSmartStand – Never miss a calibrationAvoid costly efforts and retry's with out-of-specification pipettes or those are beyond cali-bration date. Rainin XLS+ with RFID tags showtheir status every time you put them on the stand.Combined with the EasyDirect Software you can monitor and manage all pipettes in your lab./smartstandRainin pipettes – performance you can trustIts comfortable handle, light springs and “Magne-tic Assist™” technology, ensure light and smoothoperation, while significantly reducing the risk ofrepetitive strain injuries. Tip shaft options includedlow-force LTS™ for improved ergonomics and universal fit./rainin。

管理研究方法课程设计V2

管理研究方法课程设计2010级硕士生，管理学院，中国科学技术大学学分： 3 (60学时/12周)时间：周六：1-5节教师：刘和福电话：3606822E-mail：liuhf@办公室：管理学院大楼413室(周四下午4：00 - 6：00 答问时间)课程计划课程性质和任务：此课程主要目的是使学生具备进行管理学研究所必需的知识和技能。

课程将对管理研究方法中所涉及的内容进行考核，主要包括模型构建，概念度量，取样，研究设计，调查研究，试验研究和案例研究方面。

同时也会考核相关数据分析方法。

课程结束后，学生需能：1．领会管理研究方法的内涵2．了解当前管理研究的现状3．了解管理研究方法范式4．可评估，选择，及运用适当方法解决管理研究问题5．能就其希望解决的问题设计合理的研究方案6．掌握数据收集和分析方法7．了解学术论文撰写的基本范式8．能批判性的评估相关研究成果课程要求：1．阅读参考文献并准备回答相关问题，积极参与课堂中的互动事项。

2．以小组为单位，就指定的题目准备20-30分钟简报。

简报主题将围绕课堂内容，以参考文献为基础展开。

3．每个学生需选择一个可以用实证方法进行研究的管理课题。

该课题应具有可操作性，能够在指定时间内完成。

4．准备研究提案，该提案需包括以下部分1)研究主题（研究问题）2)选题的意义（理论和实践动机）3)相关研究（文献综述）4)具体的研究问题（假设）5)研究路线和方案（研究方法和数据分析方法）时间安排：1）初步提案：第6周（一页纸，单行，五号字体）2）最终报告：第12周（三页纸，单行，五号字体）5．准备研究提案简报并在课堂回答相关问题。

评分标准：课题讨论15%文献阅读和讨论10%提案50%提案简报5%期末考试20%总计100%教材：商业研究方法/(美) 唐纳德·R·库珀, (美) 帕梅拉·S·辛德勒著郭毅, 詹志俊主译北京:中国人民大学出版社,2006 (Cooper, R.D., and Schindler, P.S. Business Research Methods, MacGraw-Hill, 2006.)第二周：研究过程和科学问题提出(Research Process and Problem Definition)基本内容2.1 管理研究术语2.2 管理研究的步骤2.3 管理研究问题的识别与界定阅读参考必读:1.Cooper, Chapter2, 3, 4.选读1.Fox, D.J. “The Research Process in Education,” any edition, New York: Rinehart andWinston Inc.2.Eisenhardt, K.M., …Building theories from case study research‟, Academy of ManagementReview, 1989, 14(4), 532-50.3.Chapman, R., “Problem-definition in marketing research studies,”Journal of ConsumerMarketing, 1993, 6 (2): 51-59.4.Meyer, K., “Asian management research needs more self-confidence,”Asia Pacific Journalof Management, 2006, 23 (2): 119-137.5.McCaslin, M. and Scott, K., “The five-question method for framing a qualitativeresearch study,”The Qualitative Report, 2003, 8 (3): 447-461.相关问题1.为什么研究被看成一个过程(process),而不是一个结果(outcome)？究竟什么是研究过程?2.如何展开研究过程？它包含哪些步骤？这些步骤是如何相互关联起来的？3.研究过程的第一步是什么？4.你认为研究问题的主要来源是什么？5.在研究中想法(idea)和推断(speculation)的作用是什么？第三周：理论和概念化(Theory and Conceptualization)基本内容2.1 理论的解释，改进与应用2.2 构建理论框架与形成假设2.3 概念的操作性阅读参考必读:2.Cooper, Chapter 2, 4.3.刘军，管理研究方法：原理与应用，中国人民大学出版社2008 第三，四章选读1.Sutton, R., Staw, B., “What theory is not,”Administrative Science Quarterly,1995, 40(3),371-3842.Bacharach, S.B. “Organizational Theories: some Criteria for Evaluation,” Academy ofManagement Review, 1989, 496-515.3.Dubin, R., "Theory Building in Applied Areas," in Dunnette, Marvin D. (ed.), Handbook ofIndustrial and Organizational Psychology, (Chicago, Ill.: Rand McNally College Pub. Co.,), pp. 17-39. Focus on “Example of a Theoretical Model”and “Theory” on pages 23-30.4.Summers, J.O., “Guidelines for Conducting Research and Publishing in Marketing: FromConceptualization Through the Review Process,” Journal of the Academy of MarketingScience, 2001, 29 (4), 405-415.5.Zaichkowsky, J.L., “Conceptualizing Involvement,” Journal of Advertising, 1986, 15 (2),4-14.6.James, L.R., Mulaik, S.A., and Brett, J.M., “A Tale of Two Methods,” OrganizationalResearch Methods, 2006, 9 (2), 233-244.相关问题1.什么是理论(theory)？理论的基本构成要素是什么?2.一个理论应具备什么样的特性3.根据Dubin的观点，一个理论模型的主要要素和基本特征是什么? 选择著名理论模型并描述其主要要素。

正、负性情绪的跨文化心理测量PANAS 维度结构

正、负性情绪的跨文化心理测量:PANAS维度结构检验张卫东Ξ　刁　静(华东师范大学心理学系,上海200062)　Constance J.Schick (美国Bloomsburg大学心理学系,PA17815)摘　要　对中国大学生(N=201)和美国大学生(N=321)进行PANAS测评,以检验该量表中、英文版的因素效度。

探索性因素分析会聚性地验证了该量表的PA和NA两维度结构具有跨文化一致性,中、英文量表两因子的累积方差贡献率分别为51.31%和44.25%,接近Watson等的研究结果。

然而研究结果也显示测项偏差问题,因此中文量表的PA分量表的测项组成不等同于原量表。

中、英文量表的PA与NA分量表同样具有较高内在一致性信度,表明其符合心理测量学要求。

关键词:PANAS量表　正、负性情绪(激活)　跨文化测量　维度结构1　引言在D.Watson和A.Tellegen的情绪两因素模型(two-factor model)中,正性情绪(positive affect,PA)和负性情绪(negative affect,NA)被认为是自陈情绪结构(the structure of self-report affect)的两大彼此相对独立的基本维度(basic di2 mensions)[1]。

Waston等对PA和NA的简要定义是,前者反映人们感觉热心(enthusiastic)、积极活跃(active)和警觉(alert)的程度,高度的PA是一种精力充沛、全神贯注、欣然投入的状态,而低度PA则表现为悲哀和失神无力。

NA是一种心情低落和陷于不愉快激活境况的基本主观体验,包括各种令人生厌的(aversive)情绪状态,诸如愤怒、耻辱、憎恶、负疚、恐惧和紧张等,低度NA是一种平和与宁静的状态[2]。

自陈情绪的结构具有等级性,PA和NA作为一般维度(gen2 eral dimensions),包括不同的特定情绪反应。

2型糖尿病患者低血糖风险预测模型的构建与验证

•专科护理•2型糖尿病患者低血糖风险预测模型的构建与验证左丹，赵锡丽，代旭丽摘要：目的构建2型糖尿病患者低血糖风险预测模型，并验证其临床预测效果。

方法采用便利抽样法选取1149例2型糖尿病患者相关资料，随机分为建模组（766例）、验证组（383例），采用Logistic回归分析构建预测模型、Hosmer-Lemeshow（H-L）检验模型的拟合优度及受试者工作特征曲线下面积判断模型的预测效果。

结果建模组、验证组低血糖发生率分别为26.76%、23.24%；Logistic回归分析结果显示：体重指数（0犚=0.120）、住院时间（OR=2.052）、糖尿病病程（0犚=6.434）、周围血管病变（OR=2.979）、糖化血红蛋白（OR=0215）和三酰甘油（OR=0470）是低血糖发生的主要影响因素；构建模型的受试者工作特征曲线下面积为0.867,约登指数最大值为0.614,最佳临界值为0.329,对应的灵敏度为0.766、特异度为0.848,H-L检验P= 0.071；验证组独立数据检验结果显示，受试者工作特征曲线下面积为0.895,灵敏度为0.730,特异度为0.884。

结论构建的预测模型效能较好，可为临床医护人员早期识别2型糖尿病低血糖高危人群提供参考。

关键词：2型糖尿病；低血糖；体质量；周围血管病变；三酰甘油；血糖；风险预测；模型中图分类号：R473.5文献标识码:A DOI：10.3870/j.issn.1001-4152.2021.01.030Establishment and verification of a hypoglycemia risk prediction model for type2diabetic patients Zuo Dan,Zhao XUi,Dai Xuli. Department of Endocrinology,Second Affiliated Hospital of Chongqing Medical University,Chongqing400010,China Abstract:Objective To establish a hypoglycemia risk prediction model for type2diabetic patients,and to verify its predictive effect.Methods The related data of1149type2diabetic patients were selected using convenience sampling,which were then randomized into a modeling group(n=766)andavalidationgroup(n=383)Thelogisticregressionanalysiswasusedtoestablish theprediction model，the Hosmer-LemeshowtestandtheareaundertheROCcurvewereutilizedtojudgethegoodnessoffitand predictive effect of the model.Results The occurrence rate of hypoglycemia in the modeling group and validation group was26.76% and2324%respectively Thelogisticregressionanalysisshowedthat，body massindex(OR=0120)，lengthofhospitalstay (OR=2052)，durationofdiabetes(OR=6434)，peripheralvasculardisease(OR=2979)，glycosylatedhemoglobin(OR= 0215)andtriacylglycerol(OR=0470)were the main influencing factors of hypoglycemia Asforthemodelinggroup，thearea undertheROCcurvewas0867，withthemaximum valueofYoudenindex was0614，theoptimalcriticalvaluewas0329，the sensitivity was0.766,the specificity was0.848,and H-L test P=0.071.The test results of the validation group data indicated that,the area under the ROC curve was0.895,the sensitivity was0.730,and specificity was0.884.Conclusion The established prediction modelhasgoodpredictivee f ect，whichcouldprovidereferenceforclinicalmedicalsta f identifyingthehigh-riskgroups ofhypoglycemiaearlyKeywords：type2diabetes mellitus；hypoglycemia；weight；peripheral vascular disease；triacylglycerol；glucose；risk prediction；model据国际糖尿病联盟最新统计数据显示，全球约4.63亿糖尿病患者，预计到2030年将增至5.784亿；其中，中国糖尿病患者人数位居全球首位，高达1.164亿，且以2型糖尿病为主［］。

上海人力资源管理师三级专业英语

上海人力资源管理师三级专业英语人力资源管理师三级-英语l.Absence 缺席2.Acceptability 可接受性3Achievement tests 成就测试4Action plan行动计划5Adverse impact 负面影响6Allowance津贴，补助7Announcement 公告8Applicant 求职者9Application 申请10Appraisal评价，评估11Appoint 任命12Arbitrary 仲裁13Assessment center 评价中心14Authority 权威15Audiovisual instruction 视听教学16Audit approach 审计法17Balanced scorecard综合评价卡，平衡计分法18Behavior modeling 行为模拟19Behavior-based program 行为改变计划20Benchmarks 基准21Benefits 福利22Bonus 奖金23Business planning 企业规划24Candidate 候选人25Career anchor 职业锚26Career counseling 职业咨询27Career curves 职业曲线28Career development 职业发展29Centralization 集权化30Coach 教练31Cognitive ability 认知能力32Commitment 承诺，义务33Communication skill 沟通技巧34Compensable factors 报酬要素35Compensation 报酬，补偿36Competency assessment 能力评估37Competency model 能力模型38Competitive advantage 竞争优势39Compromise 妥协40Concentration strategy 集中战略41Consultation 商量，请教42Continuous learning 持续学习43Coordination training 合作培训44Core competencies 核心竞争力45Cost structure 成本结构46Critical incident method 关键事件法47Cross-cultural preparation 跨文化准备48Cross-training 交叉培训49Cultural environment 文化环境50Cultural shock 文化冲击51Customer appraisal 顾客评估52Data flow diagram 数据流程图53Decentralization 分散化54Decision making 决策55Deficiency 缺乏56Delayering 扁平化57Demand forecasting 需求预测58Depression 沮丧59Development planning system 开发规划系统60Differential piece rate 差额计件工资61Direct costs 直接成本62Discipline 纪律63Dismiss 解雇64Disparate treatment 差别性对待65Diversity training 多元化培训66Dividends 红利67Discrimination 歧视68Downsizing 精简69Downward move 降级70Efficiency wage theory 效率工资理论71Egalitarian 平等主义72Earnings所得，收入73Efficiency 效率74Employee empowerment 员工授权75Employee leasing 员工租借76Employee survey research 雇员调查与研究77Entrepreneur 企业家78Equal employment opportunity (EEO)公平就业机会79Ethics 道德80Exit interview 离职面谈81Expatriate外派雇员82Expert systems 专家系统83Explicit knowledge 显性知识84External growth strategy 外边成长战略85External labor market外部劳动力市场86Face to face discussion 当面讨论87Factor comparison system 因素比较法88Feedback 反馈89Flat hourly rate 小时工资率90Flextime灵活的时间91Flowchart 流程图92Formal education programs 正规教育计划93Frame of reference 参照系94Functional job analysis, FJA 职能工作分析95Gain sharing plans收益分享计划96Globalization 全球化97Goals and timetables 目标和时间表98Group mentoring program 群体指导计划U99 Head hunter 猎头100 Healthy and safety 健康安全101 High-performance work systems 高绩效工作系统102 Hourly work计时工资制103 Human capital 人力资本104 Human resource information system 人力资源信息系统105 Human resource management 人力资源管理106 Human resources planning, HRP 人力资源计划107 Income收入，收益108 Indirect costs 间接成本109 Inflation通货膨胀110 Input 投入111 Insurance 保险112 Intellectual asset 知识资产113 Internal analysis 内部分析114 Internal growth strategy 内部成长战略115 Internal labor force 内部劳动力116 Internet 互联网117 Internship programs 实习计划118 Interview 面试119 Industrialization 产业化120 IT(Information Technology)信息技术121 Invest 投资122 Job analysis 工作分析123 Job classification system 工作分类法124 Job description 工作描述125 Job design工作设计126 Job enlargement 工作扩大化127 Job enrichment 工作丰富化128 Job evaluation 工作评价129 Job ranking system 工作重要性排序法130 Job rotation 工作轮换131 Job satisfaction 工作满意度132 Job specification 工作规范133 Job structure 工作结构134 Labor relations process 劳动关系进程135 Leaderless group discussion 无领导小组讨论法136 Learning organization 学习型组织137 Line manager 直线经理138 Maintenance of membership 会员资格维持139 Management by objectives, MBO 目标管理140 Management forecasts 管理预测141 Management process 管理过程142 Manager appraisal 经理评估143 Managing diversity 管理多元化144 Manpower人力，劳动力145 Markov analysis马克夫分析法146 Material incentive 物质奖励147 Mediation 调解148 Mentor 导师149 Merit guideline 绩效指南150 Minimum wage 最低工资151 Morale 士气152 Motivation to learn 学习的动机153 Needs assessment （培训）需要评价154 Night shift 夜班155 Nonprofit organization 非营利组织156 Occupation 职业157 On-the-job training, OJT 在职培训158 Opportunity to perform 实践的机会159 Organization design and development 组织设计与发展160 Organizational analysis 组织分析161 Organization chart 组织结构图162 Organization code 组织代码163 Orientation 入职培训164 Outplacement counseling 重新谋职咨询165 Outsourcing 夕卜包166 Overpay超额工资167 Panel interview 小组面试168 Pay claim 加薪要求169 Pay grade工资等级170 Pay structure 工资结构171 Pay-for-performance standard 按绩效的报酬标准172 Pay-policy line 工资政策线173 Payroll职工薪水册174 Pension养老金，退休金175 Peer appraisal 同事评估176 Performance appraisal 绩效评价177 Performance feedback 绩效反馈178 Performance management 绩效管理179 Performance planning and evaluation 绩效规划与评价系统180 Post岗位，职位181 Priority 优先182 Person characteristics 个人特征183 Personnel selection 人员甄选184 Piecework计件工资185 Position analysis questionnaire, PAQ 职位分析问卷调查186 Power distance 权力差距187 Predictive validation 预测效度188 Profit sharing 利润分享189 Promotion 晋升190 Psychological contract 心理契约191 Questionnaire 调查问卷192 Rapport和谐，亲善193 Readability 易读性194 Readiness for training 培训准备195 Reasoning ability 推理能力196 Reconciliation 和解197 Recognition 认可，承认198 Recruitment 招募199 Redundancy 冗余200 Reengineering 流程再造201 Reject拒绝，否决202 Reinstatement 复职203 Relational database 关联数据库204 Reliability 信度205 Remuneration 报酬206 Reputation 声誉，名声207 Retention plan (核心人员)保持计划208 Repatriation 归国准备209 Replacement charts 替换表210 Return on investment (ROI)投资回报211 Role analysis technique 角色分析技术212 Role play角色扮演213 Senior management 高级管理层214 Sick leave 病假215 Self-appraisal 自我评估216 Subcontracting 转包合同217 Substantive reason 客观存在因素218 Successor 后任219 Supply forecasting 供给预测220 Talent才能，才干221 Situational interview 情景面试222 Skill inventories 技能量表223 Skill-based pay 技能工资224 Specificity 明确性225 Spot bonus即时奖金226 Staffing tables 人员配置表227 Strategic choice 战略选择228 Strategic congruence 战略一致性229 Strategic human resource management 战略性人力资源管理230 Strategy implementation 战略执行231 Subordinate 下属232 Succession planning可持续发展计划233 Tacit knowledge 隐形知识234 Task analysis 任务分析235 Team building 团队建设236 Termination 终止237 Total quality management (TQM)全面质量管理238 Training administration 培训管理239 Training outcomes 培训结果240 Trend analysis 趋势分析241 Turnover离职，流动242 Utility 效用243 Validity 效度244 Verbal comprehension 语言理解能力245 Vesting既得利益246 Voicing 发言247 Wage and salary survey 薪资调查248 Web-based training 网上培训249 Welfare system 福利体系250 Work permit/ work certificate 就业许可证。

pmsampsize 1.1.3 包说明说明书

Package‘pmsampsize’December6,2023Version1.1.3Date2023-12-05Title Sample Size for Development of a Prediction ModelMaintainer Joie Ensor<***************.uk>Depends R(>=2.1)Suggests statsDescription Computes the minimum sample size required for the development of a new multivari-able prediction model using the criteria proposed by Ri-ley et al.(2018)<doi:10.1002/sim.7992>.pmsampsize can be used to calculate the mini-mum sample size for the development of models with continuous,binary or survival(time-to-event)outcomes.Riley et al.(2018)<doi:10.1002/sim.7992>lay out a series of criteria the sam-ple size should meet.These aim to minimise the overﬁtting and to ensure precise estima-tion of key parameters in the prediction model.License GPL(>=3)RoxygenNote7.2.3Encoding UTF-8NeedsCompilation noAuthor Joie Ensor[aut,cre]Repository CRANDate/Publication2023-12-0609:10:02UTCR topics documented:pmsampsize (2)Index71pmsampsize pmsampsize-Sample Size for Development of a Prediction ModelDescriptionpmsampsize computes the minimum sample size required for the development of a new multivari-able prediction model using the criteria proposed by Riley et al.2018.Usagepmsampsize(type,nagrsquared=NA,csrsquared=NA,rsquared=NA,parameters,shrinkage=0.9,prevalence=NA,cstatistic=NA,seed=123456,rate=NA,timepoint=NA,meanfup=NA,intercept=NA,sd=NA,mmoe=1.1)Argumentstype speciﬁes the type of analysis for which sample size is being calculated•"c"speciﬁes sample size calculation for a prediction model with a contin-uous outcome•"b"speciﬁes sample size calculation for a prediction model with a binaryoutcome•"s"speciﬁes sample size calculation for a prediction model with a survival(time-to-event)outcomenagrsquared for type="b"or type="s"this speciﬁes the expected value of the Nagelkerke’s R-squared of the new model,which is the Cox-Snell R-squared scaled to lie inthe[0,1]range.It is interpretable in the same way as the standard R-squared,i.e.the percentage of variation in outcome values explained by the model.Pleaseread the description of rsquared for additional details about specifying the ex-pected R-squared performancecsrsquared for type="b"or type="s"this speciﬁes the expected value of the Cox-Snell R-squared of the new model.The Cox-Snell R-squared is the generalised versionof the well-known R-squared for continuous outcomes,based on the likelihood.Please read the description of rsquared for additional details about specifyingthe expected R-squared performance.The papers by Riley et al.(see references)outline how to obtain the Cox-Snell R-squared value from published studiesif they are not reported,using other information(such as the C-statistic[seecstatistic()option below]).rsquared for type="c"this speciﬁes the expected value of the R-squared of the new model,where R-squared is the percentage of variation in outcome values ex-plained by the model.For example,the user may input the value of the R-squared reported for a previous prediction model study in the sameﬁeld.If tak-ing a value from a previous prediction model development study,users shouldinput the model’s adjusted R-squared value,not the apparent R-squared value,as the latter is optimistic(biased).However,if taking the R-squared value froman external validation of a previous model,the apparent R-squared can be used(as the validation data was not used for development,and so R-squared appar-ent is then unbiased).Users should be conservative with their chosen R-squaredvalue;for example,by taking the R-squared value from a previous model,evenif they hope their new model will improve performance.parameters speciﬁes the number of candidate predictor parameters for potential inclusion in the new prediction model.Note that this may be larger than the number ofcandidate predictors,as categorical and continuous predictors often require twoor more parameters to be estimated.shrinkage speciﬁes the level of shrinkage desired at internal validation after developing the new model.Shrinkage is a measure of overﬁtting,and can range from0to1,with higher values denoting less overﬁtting.We recommend a shrinkage=0.9(the default in pmsampsize),which indicates that the predictor effect(beta coef-ﬁcients)in the model would need to be shrunk by10%to adjust for overﬁtting.See references below for further information.prevalence(type="b"option)speciﬁes the overall outcome proportion(for a prognostic model)or overall prevalence(for a diagnostic model)expected within the modeldevelopment dataset.This should be derived based on previous studies in thesame population.cstatistic(type="b"option)speciﬁes the C-statistic reported in an existing prediction model study to be used in conjunction with the expected prevalence to approxi-mate the Cox-Snell R-squared using the approach of Riley et al.2020.Ideally,this should be an optimism-adjusted C-statistic.The approximate Cox-Snell R-squared value is used as described above for the csrsquared()option,and sois treated as a baseline for the expected performance of the new model.seed(type="b"option)speciﬁes the initial value of the random-number seed used by the random-number functions when simulating data to approximate the Cox-Snell R-squared based on reported C-statistic and expect prevalence as describedby Riley et al.2020rate(type="s"option)speciﬁes the overall event rate in the population of interest, for example as obtained from a previous study,for the survival outcome of in-terest.NB:rate must be given in time units used for meanfup and timepointoptions.timepoint(type="s"option)speciﬁes the timepoint of interest for prediction.NB:time units must be the same as given for meanfup option(e.g.years,months).meanfup(type="s"option)speciﬁes the average(mean)follow-up time anticipated for individuals in the model development dataset,for example as taken from a pre-vious study in the population of interest.NB:time units must be the same asgiven for timepoint option.intercept(type="c"options)speciﬁes the average outcome value in the population of interest e.g.the average blood pressure,or average pain score.This could bebased on a previous study,or on clinical knowledge.sd(type="c"options)speciﬁes the standard deviation(SD)of outcome values in the population e.g.the SD for blood pressure in patients with all other predictorsset to the average.This could again be based on a previous study,or on clinicalknowledge.mmoe(type="c"options)multiplicative margin of error(MMOE)acceptable for cal-culation of the intercept.The default is a MMOE of10%.Conﬁdence intervalfor the intercept will be displayed in the output for reference.See referencesbelow for further information.Detailspmsampsize can be used to calculate the minimum sample size for the development of models with continuous,binary or survival(time-to-event)outcomes.Riley et y out a series of criteria the sample size should meet.These aim to minimise the overﬁtting and to ensure precise estimation of key parameters in the prediction model.For continuous outcomes,there are four criteria:i)small overﬁtting deﬁned by an expected shrinkage of predictor effects by10%or less,ii)small absolute difference of0.05in the model’s apparent and adjusted R-squared value,iii)precise estimation of the residual standard deviation,andiv)precise estimation of the average outcome value.The sample size calculation requires the user to pre-specify(e.g.based on previous evidence) the anticipated R-squared of the model,and the average outcome value and standard deviation of outcome values in the population of interest.For binary or survival(time-to-event)outcomes,there are three criteria:i)small overﬁtting deﬁned by an expected shrinkage of predictor effects by10%or less,ii)small absolute difference of0.05in the model’s apparent and adjusted Nagelkerke’s R-squared value,andiii)precise estimation(within+/-0.05)of the average outcome risk in the population for a key timepoint of interest for prediction.With thanks to Richard D.Riley,Emma C Martin,Gary Collins,Glen Martin&Kym Snell for helpful input&feedbackValueA list including a matrix of calculated sample size requirements for each criteria deﬁned under’De-tails’,and a series of vectors of parameters used in the calculations as well as theﬁnal recommended minimum sample size and number of events required for model development.Author(s)Joie Ensor(University of Birmingham,***************.uk),ReferencesRiley RD,Ensor J,Snell KIE,Harrell FE,Martin GP,Reitsma JB,et al.Calculating the sample size required for developing a clinical prediction model.BMJ(Clinical research ed).2020Riley RD,Snell KIE,Ensor J,Burke DL,Harrell FE,Jr.,Moons KG,Collins GS.Minimum sample size required for developing a multivariable prediction model:Part I continuous outcomes.Statistics in Medicine.2018(in-press).doi:10.1002/sim.7993Riley RD,Snell KIE,Ensor J,Burke DL,Harrell FE,Jr.,Moons KG,Collins GS.Minimum sample size required for developing a multivariable prediction model:Part II binary and time-to-event outcomes.Statistics in Medicine.2018(in-press).doi:10.1002/sim.7992Riley,RD,Van Calster,B,Collins,GS.A note on estimating the Cox-Snell R2from a reportedC statistic(AUROC)to inform sample size calculations for developing a prediction model with abinary outcome.Statistics in Medicine.2020Examples##Examples based on those included in two papers by Riley et al.##published in Statistics in Medicine(2018).##NB:Survival example based on Riley et al.BMJ paper(2020).##Binary outcomes(Logistic prediction models)#Use pmsampsize to calculate the minimum sample size required to develop a#multivariable prediction model for a binary outcome using24candidate#predictor parameters.Based on previous evidence,the outcome prevalence is#anticipated to be0.174(17.4%)and a lower bound(taken from the adjusted#Cox-Snell R-squared of an existing prediction model)for the new model s#R-squared value is0.288pmsampsize(type="b",csrsquared=0.288,parameters=24,prevalence=0.174)#Now lets assume we could not obtain a Cox-Snell R-squared estimate from an existing #prediction model,but instead had a C-statistic(0.89)reported for the existing prediction #model.We can use this C-statistic along with the prevalence to approximate the Cox-Snell #R-squared using the approach of Riley et al.(2020).Use pmsampsize with the cstatistic() #option instead of rsquared()option.pmsampsize(type="b",cstatistic=0.89,parameters=24,prevalence=0.174)##Survial outcomes(Cox prediction models)#Use pmsampsize to calculate the minimum sample size required for developing#a multivariable prediction model with a survival outcome using30candidate#predictors.We know an existing prediction model in the same field has an#R-squared adjusted of0.051.Further,in the previous study the mean#follow-up was2.07years,and overall event rate was0.065.We select a#timepoint of interest for prediction using the newly developed model of2#yearspmsampsize(type="s",csrsquared=0.051,parameters=30,rate=0.065, timepoint=2,meanfup=2.07)##Continuous outcomes(Linear prediction models)#Use pmsampsize to calculate the minimum sample size required for developing#a multivariable prediction model for a continuous outcome(here,FEV1say),#using25candidate predictors.We know an existing prediction model in the#same field has an R-squared adjusted of0.2,and that FEV1values in the#population have a mean of1.9and SD of0.6pmsampsize(type="c",rsquared=0.2,parameters=25,intercept=1.9,sd=0.6)Indexpmsampsize,27。

头颅CTA造影联合磁共振血管成像诊断急性缺血性脑卒中的价值

临床和实验医学杂志2021年、月第20卷第、期-81-活检标本可能因为取材部位和肿瘤异质性存在会低估肿瘤的分级。

基于CT放射组学模型可于术前对GIVTs 危险度分级进行有效鉴别,对临床医师指导治疗，评估预后具有很大帮助,也作为一种无创的预测方法能够为术前治疗方案的制定和患者预后提供参考。

参考文献[1]Keupg EZ,Raut CP.Managemen-of Gastrointestinal Stromal Tumors[J].Surg Clin NoVk Am,2017,97(2):257-452.[2]Lieyl-Atznanger B,Fletcher JA,Fletcher CD.GastoUtesPnai stomaltumors[J].Virchows Aoh,205,450(2):81-127.[3]DeMattec RP,Levis PJ,Leung D,et al.Two yundred gastrointestinalstromal tumors:ocuaence patterus and prognostio factors Ur survival [J].Ann Surg,240,251(1)：51-53.[4]Joensuu H.RisU stra/dcatiox of patien-s diagncued with gas W o i ntestinalstromal tumor[J].Hum Pathol:2003,36(10)：1411-55.[5]Wang JK.Predictive value and modelUg analysis of MSCT signs in gas-kointestinal stromal tumors(GISTs-to pathodgicai UsP deyoe[J].Ear Rev Med Phaoiacol SR,2017,21(5):992-1005.[7]DaRonch T,Modestc A,Bazzocchi M.Gastrointestinal stromal tumour：spiral computed tomography Uaturcs and patkologio correla/ox[J].Radiol Med,247,81(5):601-075.J]冯秋霞，孙娜娜，刘畅，等.MSCT评估胃肠道间质瘤转移风险[]•中国医学影像技术，204,34(12)：1832-1835.[3]Sandrasegaran K,Rfesh A,RusPing DA,e#P GukoRtestRL stromalUmov:CT and MRV fUdRupJ].Eur Radiol,205,15(7):14/7-88.[2]Zhou C,Duan X,Zhang X,et al.Predictive Uaturcs of CT Ur UsPstratkicakoxs in patien-s with primay g a strointestin a l stromal tumour [J].Eur Radiol:205,20(9)：3980-3225.[5]Gillies R],Kinaban PE,Hricab H.Radiomicr:i mages are more thanpictures,they are data[J].Radiology,205,273(2)：545-577. [8]CoroUer TP,Grossmann P,Hon Y,et al.CT-based radiomic signature podicW distan-metastasis in lung adenocaoRoma[J].Radiother Oncol,205,84(5):345-350.[12]Liang CS,Huang YQ,He L,et al.The devedpment and validation of aCT-based radiomicr signature Ur the poopeoUve dischmUatUx of stage I--I and stage VII-ID colorectal Cancer[J].Oncotayek225, 7(2、):5571-51412.[3]Zhou Y,He L,Huang YQ,et al.CT-based radiomicr signature: apotenUai biomarder Ur yreoperative podicUcx of early ycur o nce in hepaUce/um、caoinoma[J].Abbom Radiol(NY),205,22(0):1025-502.[5]Lu W,Chen W.Positron emission tomoyraphy/comyuterized tomo-gophy Ur tumor respoxso assessment-a review of clinical practices and radiomicr stuPics[].Transl Cancer Res,205,5(4):302-577. [4]Aerts HJ,Velazquez ER,LeijenaarRT,et al.Decoding tumour pPeno-tyyc by ncxinvasive imaging using a quantiUtive radiomicr appoach [J].Nat Commun,2014,5:4700.[5]ChalkiPox A,ODodeVy MJ,MarsPen PK.Fade Discovey Rates inPET and CT StuPics with Texture Features:A Systematic Reviev[J].PRO One,204,10(5):e012/55.[17]Cui Z,Xia Z,Su M,et al.Disrupted white matter ccxnectUdy undeVy-ing devedpmenUi dyslexia:A machine leaving appoach[J].Hum Brain Mapp,205,57(4)：1425-1453.[4]Blay JY,LevardA.Adjuvant imatinib treatment in gastoUtesPnai stromal tumor:which UsP stratifUatiox chteria and Ur how long?A case report[J].Anti Cancer Drugs:2017,27(、):71-75.[5]RutkowsPi P,PrzyCyC J,ZdzienichiM.Extenden Adjuvant Therapy withImatinib in Patients with Gastrointestinal Stromal Tumors:ycommenda-tioxs Ur pa/ent selection,UsP assessment:and molecular response mo-ndoring[J].Mol DiagnTheo204,17(4：9-19.[22]Jones RL.Practical Aspects of RisU Assessment in GastrointesPnal Stromal Tumors[J].J Gastrointes)Cano,2014,45(5)：222-207. [21]Joensuu H,Vedtah A,Rihimaki J,et al.RisU of gastrointestinal stromal tumour ocuaence after surgeo:an analysis based ox poodd poau-latiox-based codorts[J].Lancet Oncol,2012,4(5)：205-274. [22]张文华，陈韬，张明慧，等.基于放射组学的胃肠道间质瘤分类模型[]•南方医科大学学报，204,33(1)：55-00[25]Kang TW,Kim SH,Jang KM,et al.Gastrointestinal stromal tumours:Correlation of modided NIH UsP stratkica/oa with diUusioa-weighted MR imaging as an imaging biomarder[J].Eur J Radiol:204,84(、): 35-40.[27]ODeili AC,ShUagao AB,Kurra V,2al.Assessment of metasta/oUsP of gastUe GIST based on treatment-na?ve CT features[J].Eur J Sag Onud205,42(3)：1222-1223.[25]Ng F,KozarsUi R,GanesUan B,God V.Assessment of tumor heUoge-neiR by CT texture analysis:Can Uc largest cross-sectional area be used as an alteruative to whole tumor analysis[J].Eur J Radiol:225, 32(2):342-343.[26]Xu Y,van Beed EJ,Hwanje Y,et puter-aided classidcatUxof interstitial lung diseases via MDCT:3D adaptive multiple feature method(3D AMFM)[J].Acad Radiol,247,4(3):922-273.(收稿日期：2929-5-79)DOI：19.3969/pUsn.1071-4695.292090924文章编号：1671-4695（2921）91-0981-95头颅CTA造影联合磁共振血管成像诊断急性缺血性脑卒中的价值韩春靖曾明彬陈泽胜（资阳市第一人民医院医学影像科四川资阳021894）【摘要】目的探究头颅CT血管造影（CTA）联合磁共振血管成像（MRA）在诊断急性缺血性脑卒中（AIS）中的应用价值。

汽车制造业英文专业术语整理(非常好)

汇众专业技术词汇（英文）汇总
序号
1 2 3 4
术
3G 3M 5M AAR
语
5
Action plan
6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
ADV ADV–DV A/D/V P&R ADV–PV AEM AIAG Andon AP APQP APO AS ASQE ATT Audit Bidlist
152
Prototype Vehicle （Sample）
153 154 155 156
QCC QCD QCOS QFD
157
QRK
158 159 160
QSA QSDT QTC
161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176
Problem Reporting & Resolution 问题报告和解决程序 Supplier Requirement for Match Check Material 供应商材料认可程序 Supplier Requirement for Comp. Verification & Trace. 供应商零部件认可和追溯要求程序 Continuous Improvement Procedure 持续改进程序 Run @ Rate Process 节拍生产 Evaluation & Accreditation of Supplier Testing Facility 供应商实验/试验室认可程序 Prototype Material Certification Procedure 手工样件认可程序 Early Production Containment Procedure 早期生产遏制程序 Global Product Description System 全球产品描述系统 Global Purchasing System 全球采购系统 Global Quality Tracking System 全球质量跟踪系统 Gage Repeatability and Reproducibility 量具的重复性与再现性 Global Vehicle Development Process 全球车辆开发过程 Human Resource 人力资源 Incidents per Thousand Vehicles 每千辆车缺陷数 GP12程序中使用的自检记录表 Industry Engineering 工业工程 Inspection Operator Method 操作视察方法 Inspection Operator Summary 操作视察要领 Internal Rate of Return 内部收益率 Index Time 指数时间 Job Element Sheet 工作要素单 Just In Time Manufacturing Process 拉动式（及时）生产

人力资源管理师2、3级人力资源英语词汇表

人力资源管理师2级词汇表1 360-degree feedback process 360度反馈过程2 Absence 缺席3 Acceptability 可接受性4 Achievement tests 成就测试5 Action plan 行动计划6 Accountability 有责任7 Adolescent 青少年8 Adverse impact 负面影响9 Aggressive 有闯劲的，敢做敢为的10 Allowance 津贴，补助11 Ambition 野心，雄心12 Analytic approach 分析法13 Announcement 公告14 Applicant 求职者15 Application 申请16 Appraisal 评价,评估17 Appoint 任命18 Arbitrary 仲裁19 Assessment center 评价中心20 Attitude awareness and change program 态度认知与改变计划21 Attitudinal structuring 态度构建22 Authority 权威23 Audiovisual instruction 视听教学24 Audit approach 审计法25 Balanced scorecard 综合评价卡，平衡计分法26 Bargain 商谈27 Behavior modeling 行为模拟28 Behavior-based program 行为改变计划29 Benchmarks 基准30 Benefits 福利31 Bonus 奖金32 Business planning 企业规划33 Business division 事业部34 Business integration 业务整合35 Candidate 候选人36 Career anchor 职业锚37 Career counseling 职业咨询38 Career curves 职业曲线39 Career management system 职业管理系统40 Career development 职业发展41 Centralization 集权化42 Coach 教练43 Cognitive ability 认知能力44 Cognitive outcomes 认知性结果45 Collective bargaining process 劳资谈判过程46 Commitment 承诺，义务47 Communication skill 沟通技巧48 Compa-ratio 比较比率49 Compensable factors 报酬要素50 Compensation 报酬，补偿51 Competency assessment 能力评估52 Competency model 能力模型53 Competitive advantage 竞争优势54 Compromise 妥协55 Concentration strategy 集中战略56 Consultation 商量，请教57 Consumer price index, CPI 消费者价格指数58 Continuous learning 持续学习59 Coordination training 合作培训60 Core competencies 核心竞争力61 Cost structure 成本结构62 Critical incident method 关键事件法63 Cross-cultural preparation 跨文化准备64 Cross-training 交叉培训65 Cultural environment 文化环境66 Cultural shock 文化冲击67 Customer appraisal 顾客评估68 CV (curriculum vitae) 简历69 Data flow diagram 数据流程图70 Decentralization 分散化71 Decision making 决策72 Decision support systems 决策支持系统73 Deficiency 缺乏74 Defined-benefit plan 养老金福利计划75 Defined-contribution plan 资方养老金投入计划76 Delayering 扁平化77 Demand forecasting 需求预测78 Depression 沮丧79 Development planning system 开发规划系统80 Differential piece rate 差额计件工资81 Direct costs 直接成本82 Discipline 纪律83 Disparate impact 差别性影响84 Disparate treatment 差别性对待85 Diversity training 多元化培训86 Dividends 红利87 Discrimination 歧视88 Dismiss 开除，解雇89 Downsizing 精简90 Downward move 降级91 Efficiency wage theory 效率工资理论92 Egalitarian 平等主义93 Earnings 所得，收入94 Efficiency 效率95 Employee empowerment 员工授权96 Employee leasing 员工租借97 Employee survey research 雇员调查与研究98 Entrepreneur 企业家99 Equal employment opportunity (EEO) 公平就业机会100 Ethics 道德101 Exit interview 离职面谈102 Expatriate 外派雇员103 Expert systems 专家系统104 Explicit knowledge 显性知识105 External analysis 外部分析106 External growth strategy 外边成长战略107 External labor market 外部劳动力市场108 Face to face discussion 当面讨论109 Factor comparison system 因素比较法110 Feedback 反馈111 Flat hourly rate 小时工资率112 Flexible benefits plans (cafeteria plans) 灵活的福利计划（自助福利方案）113 Flextime 灵活的时间114 Flowchart 流程图115 Follow up 跟随，追随116 Formal education programs 正规教育计划117 Frame of reference 参照系118 Functional job analysis, FJA 职能工作分析119 Gain sharing plans 收益分享计划120 Globalization 全球化121 Goals and timetables 目标和时间表122 Graphic rating-scale method 图式评估法123 Grievance 委屈124 Group mentoring program 群体指导计划125 Guidelines 指导方针126 Head hunter 猎头127 Healthy and safety 健康安全128 Handover 工作交接129 High-performance work systems 高绩效工作系统130 Hourly work 计时工资制131 Human capital 人力资本132 Human resource information system (HRIS) 人力资源信息系统133 Human resource management 人力资源管理134 Human resources planning, HRP 人力资源计划135 Income 收入，收益136 Indirect costs 间接成本137 Individualism/collectivism 个人主义/集体主义138 Inflation 通货膨胀139 Input 投入140 Insurance 保险141 Intellectual asset 知识资产142 Internal analysis 内部分析143 Internal growth strategy 内部成长战略144 Internal labor force 内部劳动力145 Internet 互联网146 Internship programs 实习计划147 Interview 面试148 Industrialization 产业化149 IT(Information Technology) 信息技术150 Invest 投资151 Job analysis 工作分析152 Job classification system 工作分类法153 Job description 工作描述154 Job design 工作设计155 Job enlargement 工作扩大化156 Job enrichment 工作丰富化157 Job evaluation 工作评价158 Job commitment 工作认同159 Job ranking system 工作重要性排序法160 Job rotation 工作轮换161 Job satisfaction 工作满意度162 Job specification 工作规范163 Joint venture company 合资公司164 Key performance indicator,KPI 关键业绩指标165 Labor relations process 劳动关系进程166 Leaderless group discussion 无领导小组讨论法167 Learning organization 学习型组织168 Line manager 直线经理169 Maintenance of membership 会员资格维持170 Management by objectives, MBO 目标管理171 Management forecasts 管理预测172 Management process 管理过程173 Manager appraisal 经理评估174 Managing diversity 管理多元化175 Manpower 人力，劳动力176 Material incentive 物质奖励177 Mediation 调解178 Mentor 导师179 Merit guideline 绩效指南180 Minimum wage 最低工资181 Morale 士气182 Mobility 流动性183 Motivation to learn 学习的动机184 Needs assessment （培训）需要评价185 Night shift 夜班186 Nonprofit organization 非营利组织187 Occupation 职业188 On-the-job training, OJT 在职培训189 Open culture 开放文化190 Opportunity to perform 实践的机会191 Organization desgin and development 组织设计与发展192 Organizational analysis 组织分析193 Organizational capability 组织能力194 Organiztion chart 组织结构图195 Organization code 组织代码196 Orientation 入职培训197 Outlay 费用198 Outplacement counseling 重新谋职咨询199 Output 产出200 Outsourcing 外包201 Overpay 超额工资202 Panel interview 小组面试203 Pay claim 加薪要求204 Pay grade 工资等级205 Pay structure 工资结构206 Pay-for-performance standard 按绩效的报酬标准207 Pay-policy line 工资政策线208 Payroll 职工薪水册209 Pension 养老金，退休金210 Peer appraisal 同事评估211 Pep talk 鼓舞动员谈话212 Performance appraisal 绩效评价213 Performance feedback 绩效反馈214 Performance management 绩效管理215 Performance planning and evaluation (PPE) 绩效规划与评价系统216 Post 岗位，职位217 Potential 潜在的，可能的218 Priority 优先219 Probation 试用220 Person characteristics 个人特征221 Personnel selection 人员甄选222 Piecework 计件工资223 Position analysis questionnaire, PAQ 职位分析问卷调查224 Power distance 权力差距225 Predictive validation 预测效度226 Profit sharing 利润分享227 Promotion 晋升228 Psychological contract 心理契约229 Questionnaire 调查问卷230 Rapport 和谐，亲善231 Readability 易读性232 Readiness for training 培训准备233 Reasoning ability 推理能力234 Reconciliation 和解235 Recognition 认可，承认236 Recruitment 招募237 Redundancy 冗余238 Reengineering 流程再造239 Reference 参考240 Reject 拒绝，否决241 Reinstatement 复职242 Relational database 关联数据库243 Reliability 信度244 Remuneration 报酬245 Reputation 声誉，名声246 Retention plan (核心人员)保持计划247 Repatriation 归国准备248 Replacement charts 替换表249 Return on investment (ROI) 投资回报250 Role ambiguity 角色模糊251 Role analysis technique 角色分析技术252 Role play 角色扮演253 Senior management 高级管理层254 Settlement 解决，决定255 Sick leave 病假256 Simulation 仿真，模拟257 Self-appraisal 自我评估258 Subcontracting 转包合同259 Substantive reason 客观存在因素260 Successor 后任261 Supply forecasting 供给预测262 Survey 调查263 Target 目标，目的264 Talent 才能，才干265 Sick note 病假条266 Situational interview 情景面试267 Skill inventories 技能量表268 Skill-based pay 技能工资269 Specificity 明确性270 Spot bonus 即时奖金271 Staffing tables 人员配置表272 Strategic choice 战略选择273 Strategic congruence 战略一致性274 Strategic human resource management 战略性人力资源管理275 Strategy formulation 战略形成276 Strategy implementation 战略执行277 Subordinate 下属278 Succession planning 可持续发展计划279 Tacit knowledge 隐形知识280 Task analysis 任务分析281 Team leader training 团队领导培训282 Team building 团队建设283 Top stratum 高层284 Termination 终止285 Total quality management (TQM) 全面质量管理286 Training administration 培训管理287 Training outcomes 培训结果288 Transaction processing 事务处理289 Trend analysis 趋势分析290 Turnover 离职，流动291 Utility analysis 效用分析292 Validity 效度293 Verbal comprehension 语言理解能力294 Vesting 既得利益295 Voicing 发言296 Wage and salary survey 薪资调查297 Wage freeze 冻结工资增长298 Web-based training 网上培训299 Welfare system 福利体系300 Work permit/ work certificate 就业许可证3级词汇表1 Absence 缺席2 Acceptability 可接受性3 Achievement tests 成就测试4 Action plan 行动计划5 Adverse impact 负面影响6 Allowance 津贴，补助7 Announcement 公告8 Applicant 求职者9 Application 申请10 Appraisal 评价,评估11 Appoint 任命12 Arbitrary 仲裁13 Assessment center 评价中心14 Authority 权威15 Audiovisual instruction 视听教学16 Audit approach 审计法17 Balanced scorecard 综合评价卡，平衡计分法18 Behavior modeling 行为模拟19 Behavior-based program 行为改变计划20 Benchmarks 基准21 Benefits 福利22 Bonus 奖金23 Business planning 企业规划24 Candidate 候选人25 Career anchor 职业锚26 Career counseling 职业咨询27 Career curves 职业曲线28 Career development 职业发展29 Centralization 集权化30 Coach 教练31 Cognitive ability 认知能力32 Commitment 承诺，义务33 Communication skill 沟通技巧34 Compensable factors 报酬要素35 Compensation 报酬，补偿36 Competency assessment 能力评估37 Competency model 能力模型38 Competitive advantage 竞争优势39 Compromise 妥协40 Concentration strategy 集中战略41 Consultation 商量，请教42 Continuous learning 持续学习43 Coordination training 合作培训44 Core competencies 核心竞争力45 Cost structure 成本结构46 Critical incident method 关键事件法47 Cross-cultural preparation 跨文化准备48 Cross-training 交叉培训49 Cultural environment 文化环境50 Cultural shock 文化冲击51 Customer appraisal 顾客评估52 Data flow diagram 数据流程图53 Decentralization 分散化54 Decision making 决策55 Deficiency 缺乏56 Delayering 扁平化57 Demand forecasting 需求预测58 Depression 沮丧59 Development planning system 开发规划系统60 Differential piece rate 差额计件工资61 Direct costs 直接成本62 Discipline 纪律63 Dismiss 解雇64 Disparate treatment 差别性对待65 Diversity training 多元化培训66 Dividends 红利67 Discrimination 歧视68 Downsizing 精简69 Downward move 降级70 Efficiency wage theory 效率工资理论71 Egalitarian 平等主义72 Earnings 所得，收入73 Efficiency 效率74 Employee empowerment 员工授权75 Employee leasing 员工租借76 Employee survey research 雇员调查与研究77 Entrepreneur 企业家78 Equal employment opportunity (EEO) 公平就业机会79 Ethics 道德80 Exit interview 离职面谈81 Expatriate 外派雇员82 Expert systems 专家系统83 Explicit knowledge 显性知识84 External growth strategy 外边成长战略85 External labor market 外部劳动力市场86 Face to face discussion 当面讨论87 Factor comparison system 因素比较法88 Feedback 反馈89 Flat hourly rate 小时工资率90 Flextime 灵活的时间91 Flowchart 流程图92 Formal education programs 正规教育计划93 Frame of reference 参照系94 Functional job analysis, FJA 职能工作分析95 Gain sharing plans 收益分享计划96 Globalization 全球化97 Goals and timetables 目标和时间表98 Group mentoring program 群体指导计划99 Head hunter 猎头100 Healthy and safety 健康安全101 High-performance work systems 高绩效工作系统102 Hourly work 计时工资制103 Human capital 人力资本104 Human resource information system 人力资源信息系统105 Human resource management 人力资源管理106 Human resources planning, HRP 人力资源计划107 Income 收入，收益108 Indirect costs 间接成本109 Inflation 通货膨胀110 Input 投入111 Insurance 保险112 Intellectual asset 知识资产113 Internal analysis 内部分析114 Internal growth strategy 内部成长战略115 Internal labor force 内部劳动力116 Internet 互联网117 Internship programs 实习计划118 Interview 面试119 Industrialization 产业化120 IT(Information Technology) 信息技术121 Invest 投资122 Job analysis 工作分析123 Job classification system 工作分类法124 Job description 工作描述125 Job design 工作设计126 Job enlargement 工作扩大化127 Job enrichment 工作丰富化128 Job evaluation 工作评价129 Job ranking system 工作重要性排序法130 Job rotation 工作轮换131 Job satisfaction 工作满意度132 Job specification 工作规范133 Job structure 工作结构134 Labor relations process 劳动关系进程135 Leaderless group discussion 无领导小组讨论法136 Learning organization 学习型组织137 Line manager 直线经理138 Maintenance of membership 会员资格维持139 Management by objectives, MBO 目标管理140 Management forecasts 管理预测141 Management process 管理过程142 Manager appraisal 经理评估143 Managing diversity 管理多元化144 Manpower 人力，劳动力145 Markov analysis 马克夫分析法146 Material incentive 物质奖励147 Mediation 调解148 Mentor 导师149 Merit guideline 绩效指南150 Minimum wage 最低工资151 Morale 士气152 Motivation to learn 学习的动机153 Needs assessment （培训）需要评价154 Night shift 夜班155 Nonprofit organization 非营利组织156 Occupation 职业157 On-the-job training, OJT 在职培训158 Opportunity to perform 实践的机会159 Organization desgin and development 组织设计与发展160 Organizational analysis 组织分析161 Organiztion chart 组织结构图162 Organization code 组织代码163 Orientation 入职培训164 Outplacement counseling 重新谋职咨询165 Outsourcing 外包166 Overpay 超额工资167 Panel interview 小组面试168 Pay claim 加薪要求169 Pay grade 工资等级170 Pay structure 工资结构171 Pay-for-performance standard 按绩效的报酬标准172 Pay-policy line 工资政策线173 Payroll 职工薪水册174 Pension 养老金，退休金175 Peer appraisal 同事评估176 Performance appraisal 绩效评价177 Performance feedback 绩效反馈178 Performance management 绩效管理179 Performance planning and evaluation 绩效规划与评价系统180 Post 岗位，职位181 Priority 优先182 Person characteristics 个人特征183 Personnel selection 人员甄选184 Piecework 计件工资185 Position analysis questionnaire, PAQ 职位分析问卷调查186 Power distance 权力差距187 Predictive validation 预测效度188 Profit sharing 利润分享189 Promotion 晋升190 Psychological contract 心理契约191 Questionnaire 调查问卷192 Rapport 和谐，亲善193 Readability 易读性194 Readiness for training 培训准备195 Reasoning ability 推理能力196 Reconciliation 和解197 Recognition 认可，承认198 Recruitment 招募199 Redundancy 冗余200 Reengineering 流程再造201 Reject 拒绝，否决202 Reinstatement 复职203 Relational database 关联数据库204 Reliability 信度205 Remuneration 报酬206 Reputation 声誉，名声207 Retention plan (核心人员)保持计划208 Repatriation 归国准备209 Replacement charts 替换表210 Return on investment (ROI) 投资回报211 Role analysis technique 角色分析技术212 Role play 角色扮演213 Senior management 高级管理层214 Sick leave 病假215 Self-appraisal 自我评估216 Subcontracting 转包合同217 Substantive reason 客观存在因素218 Successor 后任219 Supply forecasting 供给预测220 Talent 才能，才干221 Situational interview 情景面试222 Skill inventories 技能量表223 Skill-based pay 技能工资224 Specificity 明确性225 Spot bonus 即时奖金226 Staffing tables 人员配置表227 Strategic choice 战略选择228 Strategic congruence 战略一致性229 Strategic human resource management 战略性人力资源管理230 Strategy implementation 战略执行231 Subordinate 下属232 Succession planning 可持续发展计划233 Tacit knowledge 隐形知识234 Task analysis 任务分析235 Team building 团队建设236 Termination 终止237 Total quality management (TQM) 全面质量管理238 Training administration 培训管理239 Training outcomes 培训结果240 Trend analysis 趋势分析241 Turnover 离职，流动242 Utility 效用243 Validity 效度244 Verbal comprehension 语言理解能力245 Vesting 既得利益246 Voicing 发言247 Wage and salary survey 薪资调查248 Web-based training 网上培训249 Welfare system 福利体系250 Work permit/ work certificate 就业许可证。

基于临床影像学参数构建列线图模型在术前预测胃癌淋巴结转移中的价值

[收稿日期]2022-10-29　[修回日期]2023-03-16[作者单位]安徽省安庆市立医院医学影像科,246004[作者简介]陈　平(1979-),男,副主任医师.[文章编号]1000⁃2200(2023)12⁃1721⁃05㊃影像医学㊃基于临床影像学参数构建列线图模型在术前预测胃癌淋巴结转移中的价值陈　平,代国坡,史恒峰[摘要]目的:建立并验证基于临床影像学参数构建列线图模型在术前预测进展期胃癌(AGC)淋巴结转移中的价值㊂方法:回顾性分析216例经病理证实的胃癌病人资料,随机分为训练组158例和验证组58例㊂收集病人临床资料及计算机断层成像(CT)影像学征象进行单因素㊁多因素logistic 回归分析,用验证组进行验证,应用R 3.5.3软件包构建列线图模型,采用受试者工作特征(ROC)曲线评估列线图的预测效能,校准曲线及决策曲线验证模型的临床实用性㊂结果:216例病人中,130例淋巴结转移阳性,86例淋巴结转移阴性㊂在训练组和验证组中,饮酒史㊁瘤周脂肪浸润㊁强化程度㊁CT⁃淋巴结状态和血小板与淋巴细胞比率(PLR)在术前胃癌病人发生淋巴结转移的预测中差异有统计学意义(P <0.05~P <0.01)㊂多因素logistic 回归分析显示,病人饮酒史㊁瘤周脂肪浸润㊁CT 强化程度㊁CT⁃淋巴结状态㊁PLR >161是术前预测胃癌病人发生淋巴结转移的独立影响因素(P <0.05)㊂基于饮酒史㊁瘤周脂肪浸润㊁CT 强化程度㊁CT⁃淋巴结状态及PLR 构建预测胃癌病人发生淋巴结转移的列线图模型,模型ROC 曲线下面积在训练组和验证组分别为0.789(95%CI :0.719~0.860)㊁0.791(95%CI :0.678~0.905)㊂模型的敏感度及特异度在训练组分别为67.4%㊁78.3%,在验证组中分别为62.5%㊁84.6%,校准曲线和决策曲线证实了模型的临床实用性㊂结论:饮酒史㊁瘤周脂肪浸润㊁CT 强化程度㊁CT⁃淋巴结状态及PLR 是胃癌病人发生淋巴结转移的独立影响因素,以此构建的列线图模型预测效能较好,在一定程度上可以协助临床决策㊂[关键词]胃肿瘤;淋巴结转移;计算机断层成像;列线图[中图法分类号]R 735.2 [文献标志码]A DOI :10.13898/ki.issn.1000⁃2200.2023.12.022Value of constructing nomogram model based on clinical⁃radiologicalparameters in preoperative prediction of lymph node metastasis in gastric cancerCHEN Ping,DAI Guo⁃po,SHI Heng⁃feng(Department of Radiology ,The Municipal Hospital of Anqing ,Anqing Anhui 246004,China )[Abstract ]Objective :To construct and validate the value of constructing nomogram model based on clinical⁃radiological parameters in preoperative prediction of lymph node metastasis in advanced gastric cancer (AGC).Methods :A retrospective analysis was conductedon 216gastric cancer patients confirmed by pathology,who were randomly divided into a training group (n =158)and a validation group (n =58).The clinical data and computed tomography (CT)imaging features of patients were collected for univariate and multivariate logistic regression analysis,the validation group was used to validate,the nomogram model was constructed with R 3.5.3software package,the prediction efficacy of the nomogram model was evaluated using receiver operating characteristic (ROC)curve,and the clinical practicality of the model was validated by calibration curve and decision curve.Results :Among the 216patients,130cases were positive for lymph node metastasis and 86cases were negative for lymph node metastasis.In the training group and validationgroup,there were statistically significant differences in alcohol consumption history,peritumoral fat infiltration,degree of enhancement,CT⁃lymph node status,and platelet to lymphocyte ratio (PLR)in preoperative prediction of lymph node metastasis in gastric cancer patients (P <0.05to P <0.01).Multivariate logistic regression analysis showed that alcohol consumption history,peritumoral fat infiltration,CT enhancement degree,CT⁃lymph node status,and PLR >161were independent influencing factors for preoperative prediction of lymph node metastasis in gastric cancer patients(P <0.05).A namogram model was constructed to predict lymph node metastasis in gastric cancer patients based on alcohol consumption history,peritumoral fat infiltration,[12]　WU Y,CHEN B,SU L,et al .Diagnostic value of double low⁃dosetargeted perfusion CT imaging for the diagnosis of invasive and preinvasive pulmonary ground⁃glass nodules:systematic review and meta⁃analysis[J].Transl Cancer Res,2022,11(8):2823.[13]　HE W,GUO G,DU X,et al .CT imaging indications correlate withthe degree of lung adenocarcinoma infiltration[J].Front Oncol,2023,13:1108758.[14]　LE X,NILSSON M,GOLDMAN J,et al .Dual EGFR⁃VEGFpathway inhibition:a promising strategy for patients with EGFR⁃mutant NSCLC[J].J Thorac Oncol,2021,16(2):205.(本文编辑　周洋)CT enhancement degree,CT⁃lymph node status,and PLR.The area under the ROC curve of the model was0.789(95%CI:0.719-0.860)in the training group and0.791(95%CI:0.678-0.905)in the validation group,respectively.The sensitivity and specificity of the model were67.4%and78.3%in the training group,and62.5%and84.6%in the validation group,respectively.The calibration curve and decision curve confirmed the clinical practicality of the model.Conclusions:Alcohol consumption history, peritumoral fat infiltration,CT enhancement degree,CT⁃lymph node status,and PLR are independent influencing factors for the occurrence of lymph node metastasis in gastric cancer patients.The namogram model constructed based on them has good prediction efficacy and can assist clinical decision⁃making to some extent.[Key words]gastric neoplasms;lymph node metastasis;computed tomography;namogram 胃癌是目前消化系统最常见的恶性肿瘤之一,是全球癌症相关死亡的第三大原因[1]㊂淋巴结转移决定了淋巴结的清扫程度,是判定胃癌病人临床分期及预后的重要依据[2]㊂此外,在其范围有限的情况下,通过适当的淋巴结清扫可以治愈胃癌的淋巴结转移,预测胃癌病人的淋巴结转移可能性,对于保证胃癌病人的准确诊断和恰当的手术治疗具有重要意义[3]㊂然而,对于淋巴结状态的评估可以依赖于原发肿瘤的综合特征,但大多数需要通过术后病理检查来获得,这可能给病人带来更多的身体痛苦和经济负担[4]㊂目前很少有术前无创的方法对进展期胃癌(advanced gastric cancer,AGC)的分化程度进行术前诊断㊂计算机断层成像(computed tomography,CT)是目前临床术前评估肿瘤病人淋巴结状态最常用的成像手段,但是其诊断准确率达不到临床要求,准确率仅60%[5]㊂列线图作为一种直观的预测方法,能够通过将传统的临床及影像学参数进行分析来构建预测模型,已被应用于疾病的诊断㊁预后评估和肿瘤治疗等方面的评价[6-8]㊂目前国内通过构建列线图在胃癌淋巴结转移方面的预测研究较少,因此,本研究将建立并验证基于临床指标及影像学征象构建的预测模型,用于胃癌病人术前淋巴结转移的评估㊂1　资料与方法1.1　一般资料　选取2020年1月至2022年9月安庆市立医院216例经病理确诊的胃癌病人作为研究对象,其中男154例,女62例㊂使用SPSS软件将病人随机分为训练组158例和验证组58例㊂本研究将年龄㊁肿瘤浸润深度㊁癌胚抗原(carcino⁃embryonic antigen,CEA)㊁中性粒细胞与淋巴细胞比值(neutrophil to lymphocyte ratio,NLR)㊁血小板与淋巴细胞比率(platelet to lymphocyte ratio,PLR)进行二分类㊂纳入标准:(1)术后病理结果中有明确淋巴结分期;(2)术前2周内进行增强CT检查;(3)术前未进行放化疗或其他治疗方法㊂排除标准:(1)病人缺乏相应的临床信息;(2)病灶在CT图像上显示不清或者胃部充盈欠佳无法进行影像学评估㊂1.2　仪器及方法　采用西门子公司第二代DSCT (SOMATOM Definition Flash,Siemens,Germany),先行常规平扫,然后利用高压注射器以3.5mL/s速率经肘静脉注射非离子型对比剂碘海醇(1mL/kg),分别于延时25s和70s进行双能量动脉期及静脉期扫描㊂常规平扫参数:管电压120kV,参考管电流190mAs,准直器32×1.2mm,螺距0.9,旋转时间0.5s㊂双能量扫描参数:A球管管电压100kV,参考管电流230mAs;B球管管电压140kV,参考管电流178mAs,开启实时动态曝光剂量调节(combined application reduce exposure,CARE Dose 4D),准直器128×0.6mm,螺距0.7,旋转时间0.33s,图像重建层厚0.75mm㊂1.3　临床病史收集及影像学征象评估　通过医院系统收集病人的临床病史(年龄㊁性别㊁饮酒史㊁CEA 指标㊁中性粒细胞计数㊁淋巴细胞计数及血小板计数),年龄以60岁分组;饮酒史以临床病史为准; NLR和PLR分别以2.45㊁161分为临界值[9]㊂病人CT图像征象分析由2位具有5年诊断经验的放射科医生完成,意见不一致时询问上级医生㊂影像征象分析包括肿瘤发生部位(贲门㊁胃体㊁胃窦㊁发生于两部位之上),瘤周脂肪浸润(为肿瘤周围脂肪内的毛细血管㊁腹膜周围索条状影和肠壁周围斑片状或结节状的突起),强化程度(为增强CT检查肿块静脉期CT值与平扫CT值差值,>40HU为明显强化,≤40HU为轻至中度强化),肿瘤浸润深度(肿瘤最大层面的厚度),CT⁃淋巴结状态(为当淋巴门部结构消失,淋巴门移位,局部偏心性皮质增厚,淋巴结短直径>1cm,或长轴与短轴比值≤2)[10]㊂1.4　统计学方法　采用χ2检验㊁logistic回归分析㊁列线图预测模型和ROC曲线分析㊂2　结果2.1　2组临床影像学参数分析　216例病人中,130例淋巴结转移病人,86例淋巴结转移阴性㊂在训练组和验证组中,饮酒史㊁瘤周脂肪浸润㊁强化程度㊁CT⁃淋巴结状态和PLR 在术前胃癌病人发生淋巴结转移的预测中差异有统计学意义(P <0.05~P <0.01),但年龄㊁性别㊁发病部位㊁肿瘤浸润深度㊁CEA 指标和NLR 差异均无统计学意义(P >0.05)(见表1)㊂2.2　典型病例　病人1,男,年龄57岁,平扫发现病灶位于胃小弯侧(见图1A),增强扫描呈明显不均质强化(见图1B㊁C),经病人术后病理证实(HE 染色,×400)为中分化腺癌(见图1D㊁E),淋巴结活检证实胃小弯淋巴结见癌转移㊂病人2,女,年龄65岁,病灶位于胃角(见图1F),增强扫描呈明显不均质强化(见图1G㊁H),病理证实(HE 染色,×400)为低分化腺癌(见图1I㊁J),淋巴结活检证实无周围淋巴结转移㊂2.3　胃癌淋巴结转移多因素logistic 分析　多因素logistic 回归分析显示,病人饮酒史㊁瘤周脂肪浸润㊁CT 强化程度㊁CT 显示淋巴结状态㊁PLR >161是术前预测胃癌病人发生淋巴结转移的独立影响因素(P <0.05)(见表2)㊂表1　2组临床影像学参数分析[n ;百分率(%)]项目训练组　淋巴结转移(-) 淋巴结转移(+)　χ2P验证组　淋巴结转移(-) 淋巴结转移(+)　χ2P年龄/岁　<60　≥6012(20.0)48(80.0)19(19.4)79(80.6)0.01>0.056(23.1)20(76.9)10(31.3)22(68.7)0.48>0.05性别　女　男20(33.3)40(66.7)28(28.6)70(71.4)0.40>0.057(26.9)19(73.1)7(21.9)25(78.1)0.20>0.05饮酒史　无　有42(70.0)18(30.0)50(51.0)48(49.0) 5.51<0.0515(57.7)11(42.3)9(28.1)23(71.9) 5.17<0.05部位　贲门16(26.7)19(19.4)10(38.5)11(34.4) 2.12>0.05　胃体　胃窦11(18.3)29(48.3)18(18.4)45(45.9) 3.68>0.054(15.4)11(42.3)7(21.9)10(31.2)　两部位及以上4(6.7)16(16.3)1(3.8)4(12.5)瘤周脂肪浸润　无　有34(56.7)26(43.3)25(25.5)73(74.5)15.44<0.0113(50.0)13(50.0)7(21.9)25(78.1) 5.02<0.05强化程度　轻至中度　明显23(38.3)37(61.7)16(16.3)82(83.7)9.70<0.0111(42.3)15(57.7)5(15.6)27(84.4) 5.11<0.05CT⁃淋巴结状态　无　有41(68.3)19(31.7)34(34.7)64(65.3)16.89<0.0116(61.5)10(38.5)10(31.2)22(68.8) 5.32<0.01肿瘤浸润深度/cm <2　≥256(93.3)4(6.7)85(86.7)13(13.3) 1.69>0.0523(88.5)3(11.5)23(71.9)9(28.1)2.41>0.05CEA /(ng /mL)　≤5　>542(70.0)18(30.0)78(79.6)20(20.4) 1.87>0.0522(84.6)4(15.4)22(68.7)10(31.3) 1.97>0.05NLR ≤2.45　>2.4538(63.3)22(36.7)61(72.2)37(37.8)0.02>0.0514(53.8)12(46.2)19(59.4)13(40.6)0.18>0.05PLR ≤161　>16133(55.0)27(45.0)31(31.6)67(68.4)8.43<0.0119(73.1)7(26.9)13(40.6)19(59.4)6.11<0.052.4　列线图模型构建及评估　基于饮酒史㊁瘤周脂肪浸润㊁CT 强化程度㊁CT⁃淋巴结状态及PLR 构建预测胃癌病人发生淋巴结转移的列线图模型(见图2)㊂饮酒史㊁瘤周脂肪浸润㊁CT 增强程度㊁CT⁃淋巴结状态㊁PLR 5个变量在轴上相应点对应不同的分值,各项评分相加,获得总分,不同总分对应相应胃癌病人发生淋巴结转移的可能性㊂列线图模型ROC 曲线下面积(area under the curve,AUC)在训练组和验证组分别为0.789(95%CI :0.719~0.860)㊁0.791(95%CI :0.678~0.905)㊂模型的敏感度及特异度在训练组分别为67.4%㊁78.3%,在验证组中分别为62.5%㊁84.6%,校准曲线显示预测准确性较好,决策曲线显示预测的标准净收益较高(训练组,阈值:0.1~0.65,标准净收益>0;验证组,阈值:0.1~0.7,标准净收益>0),表明该列线图预测模型效能较好㊂表2　训练组胃癌淋巴结转移多因素logistic 分析变量BSEWaldχ2POR (95%CI )饮酒史0.8860.393 5.07<0.05　2.425(1.121~5.243)瘤周脂肪浸润0.9290.408 5.18<0.05 2.532(1.137~5.639)强化程度0.9640.430 5.02<0.05 2.622(1.128~6.093)CT⁃淋巴结状态0.9940.407 5.97<0.052.702(1.217~5.996)PLR0.8900.3825.43<0.052.434(1.152~5.145)3　讨论常规检查(胃镜㊁CT 或者磁共振成像等)诊断胃癌发生淋巴结转移具有一定的局限性,目前病理活检仍作为淋巴结转移的金标准[11-12]㊂胃部淋巴结通常与相应血管分布一致,是胃癌发生转移的重要途径[13],因此,准确预测术后病人发生淋巴结转移的可能性,进行预防性的淋巴结区域的清扫对于临床决策和改善病人预后至关重要[14]㊂本研究中,笔者构建了基于临床相关病史及影像学评估参数的列线图预测模型,用于预测胃癌病人术后发生淋巴结转移的可能性㊂列线图模型AUC 在训练组和验证组为分别为0.789㊁0.791,模型的敏感度及特异度在训练组分别为67.4%㊁78.3%,在验证组中分别为62.5%㊁84.6%,校准曲线及决策曲线显示该模型临床实用性较好㊂本研究中通过单因素和多因素logistic 回归分析得出病人饮酒史㊁PLR >161㊁瘤周脂肪浸润㊁CT 病灶强化程度及CT⁃淋巴结状态是胃癌术后发生淋巴结转移的独立影响因素㊂本研究发现病人有饮酒史发生淋巴结转移的可能性就会增加,LI 等[15]研究发现饮酒与日本男性患胃癌的风险增加有关,作者认为饮酒后会导致周围血管扩张,导致血液流动更快,间接引起肿瘤细胞等的播散,而胃部淋巴结多与血管走形一致,所以饮酒后病人发生淋巴结转移的可能性就会增加㊂PLR 可以反映机体的炎症反应,能间接地反映多种恶性肿瘤的预后,WANG 等[16]研究发现PLR 作为局部晚期GC 病人NCT 疗效的独立预测指标,本研究中PLR 作为淋巴结转移的独立影响因素,可能与淋巴细胞数量减少,相当于身体抗肿瘤免疫力下降,阻碍了识别和应对肿瘤抗原突变的能力,导致肿瘤细胞增值和转移创造了有利的环境㊂有研究[17]发现,瘤周脂肪侵犯是结直肠癌病人无复发生存时间的独立预测因子,笔者认为这可能与肿瘤的浸润能力相关,瘤周脂肪浸润的病灶,说明病灶侵袭能力较强,发生淋巴结转移的可能就越大㊂CT强化程度与胃癌病人病理特征相关联[18],这可能是因为强化程度高与肿瘤富血供有关,更容易出现新生血管,肿瘤内诱导能力更强,病人发生淋巴结转移的可能性大㊂CT检查中胃癌病人出现淋巴结肿大,发生淋巴结转移的概率大,但不排除炎性淋巴结可能㊂朱芸等[19]研究证实MRI显示淋巴结肿大是肿块型乳腺浸润性导管癌前哨淋巴结转移的独立预测因素,这可能与肿瘤通过淋巴管浸润,导致淋巴结形态和大小失常有关㊂本研究存在以下局限性:(1)CT图像评估中可能存在一定的争议,对最终结果可能存在一定的影响;(2)本研究是单中心研究,样本量相对有限;(3)研究中纳入的临床和影像学参数,未进行影像组学的分析㊂未来将进一步扩大样本量,联合临床㊁影像学参数及影像组学分析构建联合模型进行预测,提升诊断效能㊂综上所述,病人饮酒史㊁PLR>161㊁瘤周脂肪浸润㊁CT病灶强化程度及CT⁃淋巴结状态是胃癌术前发生淋巴结转移的独立影响因素,基于这些因素构建的列线图预测模型具有较好的准确性及临床实用性,在一定程度上协助临床医生决策㊂[参考文献][1]　AJANI JA,D′AMICO TA,BENTREM DJ,et al.Gastric cancer,version2.2022,NCCN clinical practice guidelines in oncology[J].J Natl Compr Canc Netw,2022,20(2):167. [2]　马灼宇.胃癌病人No.8p淋巴结转移的危险因素及其预后的多因素回归分析[J].蚌埠医学院学报,2021,46(12):1707.[3]　KINAMI S,SAITO H,TAKAMURA H.Significance of lymphnode metastasis in the treatment of gastric cancer and currentchallenges in determining the extent of metastasis[J].FrontOncol,2022,11:806162.[4]　TIAN H,NING Z,ZONG Z,et al.Application of machine learningalgorithms to predict lymph node metastasis in early gastric cancer[J].Front Med(Lausanne),2022,8:759013.[5]　王小雷,高玉青,徐鹤,等.基于能谱CT纹理分析在预测胃癌术前淋巴结转移中的价值[J].蚌埠医学院学报,2021,46(1):21.[6]　SHAN Y,YU X,YANG Y,et al.Nomogram for the preoperativeprediction of the macrotrabecular⁃massive subtype ofhepatocellular carcinoma[J].J Hepatocell Carcinoma,2022,9:717.[7]　CHEN D,LIU Z,LIU W,et al.Predicting postoperative peritonealmetastasis in gastric cancer with serosal invasion using a collagennomogram[J].Nat Commun,2021,12(1):179.[8]　TONELLO AS,CAPELLI G,BAO QR,et al.A nomogram topredict overall survival and disease⁃free survival after curative⁃intent gastrectomy for gastric cancer[J].Updates Surg,2021,73(5):1879.[9]　仓慧,高昳,刘兵团,等.术前中性粒细胞与淋巴细胞比值及血小板与淋巴细胞比值对胃癌患者淋巴结转移联合诊断模型的优势[J].中国临床医生杂志,2022,50(9):1047. [10]　QIU Y,ZHANG X,WU Z,et al.MRI⁃based radiomics nomogram:prediction of axillary non⁃sentinel lymph node metastasis inpatients with sentinel lymph node⁃positive breast cancer[J].Front Oncol,2022,12:811347.[11]　洪晴,荆明,林姗,等.胃癌根治术后淋巴结阳性患者的复发转移分析及Nomogram预测模型的建立[J].河南医学研究,2022,31(10):1729.[12]　石琳娜,隋红.早期胃癌淋巴结转移评估方法的研究现状[J].实用肿瘤学杂志,2020,34(3):286.[13]　夏云,王日玮.MRI与MSCT诊断胃癌淋巴结转移的应用价值及其影像学特点[J].微创医学,2022,17(3):336. [14]　WANG J,WANG L,LI S,et al.Risk factors of lymph nodemetastasis and its prognostic significance in early gastric cancer:amulticenter study[J].Front Oncol,2021,11:649035. [15]　LI Y,ESHAK ES,SHIRAI K,et al.Alcohol consumption and riskof gastric cancer:the japan collaborative cohort study[J].JEpidemiol,2021,31(1):30.[16]　WANG W,TONG Y,SUN S,et al.Predictive value of NLR andPLR in response to preoperative chemotherapy and prognosis inlocally advanced gastric cancer[J].Front Oncol,2022,12:936206.[17]　AHN H,WON LEE J,JANG SH,et al.Prognostic significance ofimaging features of peritumoral adipose tissue in FDG PET/CT ofpatients with colorectal cancer[J].Eur J Radiol,2021,145:110047.[18]　孙国臣.胃癌患者腹部多层螺旋CT瘤周低密度带厚度㊁强化程度及其与病理学特征的关联性[J].河南医学研究,2021,30(15):2844.[19]　朱芸,张书海,王小雷,等.基于MRI㊁钼靶和病理的列线图预测肿块型乳腺浸润性导管癌前哨淋巴结转移的价值[J].磁共振成像,2022,13(5):45.(本文编辑　赵素容)。

第三章生态模型的建模步骤-15

控制因素：

3.2 定义研究问题
可用数据的数量与质量，确定模型的复杂性

哪些子系统有资料数据且质量较好：藻类子系统、溶解营养物子系统哪些子系统没有资料数据或数据质量很差：其他子系统哪些控制因素有数据：入流、出流水量、入流、出流中P的浓度、太阳辐射，温度在模型中应包括哪些子系统：藻类子系统、溶解营养物子系统在模型中应包括哪些控制因素：选择有数据的因素
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图3.1.2 系统建模的一般步骤
3.8 参数估计和校准（calibration）（续）

测定法：
选择敏感的参数进行实验室测定，主要是一些生物参数，因为生物参数的实验值与自然值之间的差别比化学或物理参数的大得多。必要性如下：生物参数一般对环境因子更敏感。如低浓度的有毒物质能显著改变生长率。生物参数受许多环境因子的影响。例如，浮游植物的生长率取决于营养物浓度，但局部的营养物浓度又取决于水的涡流，这又取决于风的应力等。影响生物参数的环境因子是相互作用的，这使得实验室的测量来预测自然参数的实际值几乎不可能。
3.4 建立生态过程的数学方程（续）
实例：状态方程： dPS/dt = (流入-流出-被藻类吸收+藻类释放) = (PIN-PS)*(Q/V)-(R)*PA dPA/dt = (藻类吸收-流出-藻类释放) =( - R – (Q/V))*PA 率、系数方程： = S * PA/（PS+K） S = Smax *（1 + Sin(0.008603 * t)）式中：：藻类生长率 R：藻类释放速率常数 (0.1 d-1) K：藻类吸收P的米氏常数（1.0g P/m3） S：太阳辐射函数 Smax：最大太阳辐射强度 Q：出入流流量（m3） V：湖泊体积（m3）

可见-近红外光谱测定血红蛋白的等效波段选择

可见-近红外光谱测定血红蛋白的等效波段选择刘振尧;潘涛【摘要】The VIS-NIR spectroscopy combined with the improved Moving Window Partial Least-square (MWPLS) method was applied to a high accurate waveband selection for the rapid no-reagent determination of Hemoglobin (HGB) in human whole blood. A new modeling evaluation system was proposed to avoid the evaluation distortion. First, seventy samples were randomly selected from a total of 205 samples as the validation set,the remaining 135 samples were used as the modeling set, and the modeling set was divided into similar calibration (80 samples) and prediction (55 samples) sets for a total of 50 times. Then, modeling and optimization were performed in each division to get stable model. Finally, the optimized model was validated again using the validation set. Experimental results indicate that the VIS-short NIR region 400-1 100 nm can be used as the information waveband of HGB in human whole blood, the global optimal waveband 492-890 nm is further selected from 400-1100 nm with MWPLS method, and a model space including 77 equivalent wavebands is obtained. By taking the 492-890 nm for an example, validation effects V-SEP, V-RP, and V-RSEP are 2. 58 g L-1, 0.988, and 1. 97%, respectively. It concludes that HGB prediction values of the samples are highly close to the clinic measured values, which may be used in clinical diagnosis.%将可见-近红外光谱和改进的移动窗口偏最小二乘(MWPLS)方法应用于人类血红蛋白(HGB)无试剂快速检测的高精度波段优选.为了避免模型评价失真,提出了一种新的模型评价体系.首先,从全体205个样品中随机抽取70个作为检验集,余下的135个作为建模集,并划分为具有相似性的定标集(80个样品)和预测集(55个样品)共50次;其次,对每一次划分都分别建模和优化,使得模型具有稳定性;最后,利用检验集对优选出的模型进行再次检验.实验结果表明:可见-短波近红外波段400～1100 nm可以作为人体全血HGB的信息波段;进一步采用MWPLS 方法从400～1100nm中选出全局最优波段为492～890 nm,并得到包含77个等效波段的模型空间.以492～890 nm为例,检验效果预测均方根偏差(V-SEP)、预测相关系数(V-RP)和相对预测均方根偏差(V-RSEP)分别为2.58 g L-1、0.988和1.97％,得到的样品的HGB预测值与临床实测值吻合精度很高,可望应用于临床.【期刊名称】《光学精密工程》【年(卷),期】2012(020)010【总页数】6页(P2170-2175)【关键词】人类全血;血红蛋白;VIS-NIR光谱;波段选择;等效模型空间【作者】刘振尧;潘涛【作者单位】暨南大学光电信息与传感技术广东普通高校重点实验室,广东广州510632;暨南大学光电信息与传感技术广东普通高校重点实验室,广东广州510632【正文语种】中文【中图分类】O657.331 引言血红蛋白（HGB）是红细胞中负责运载氧的蛋白质，HGB含量可用于判断贫血和体内铁的营养状况，是重要的临床生化指标。

基于响应面法的Hybrid III假人头颈部有限元模型的验证

基于响应面法的Hybrid III假人头颈部有限元模型的验证赖兴华林喆周青汽车安全与节能国家重点实验室清华大学汽车工程系北京100084摘要:在混三假人模型开发领域，头-颈部模型的验证仍然具有一定的挑战性。

本文利用HyperStudy软件研究了混三假人头-颈部有限元模型的材料参数标定。

基于实验设计方法（11因子2水平部分析因试验）评估不同材料参数的主效应，确定了该模型的主要影响因子。

根据主要影响因子，结合响应面理论和方法，对混三假人头-颈部模型进行优化计算。

优化过程获得了材料参数的最佳值组合，基于此，假人头-颈部模型响应和试验数据吻合较好。

关键词:有限元，混三假人，HyperStudy，响应面法，优化Response surface-based optimization for validation of a finite element model of Hybrid IIIhead-neck sub-assemblyXinghua Lai Zhe Lin Qing ZhouState Key Laboratory of Automotive Safety and Energy, Department of AutomotiveEngineering, Tsinghua University, Beijing 100084, ChinaAbstract:Finite element modeling of Hybrid III head-neck sub-assembly remains a big challenge in the field of the dummy modeling. This paper deals with the material calibration study in validation of a Hybrid III head-neck FE model against typical certification test using HyperStudy. Through assessment of main effects of different design variables by design of experiments (DOE), major influencing factors on the head-neck response are identified. Response surface-based optimization methodology is then employed for validation of the head-neck model based on determined influencing material parameters. The optimization process has yielded optimal set of material parameters values with which the head-neck modelcan reasonably correlate with the experimental data.Keywords: Finite element, Hybrid III, HyperStudy, Response Surface, Optimization1. IntroductionFinite element method (FEM) has shown a significant increase in use in the field of automotive passive safety research. Dummy model, which allows efficient evaluation of restraint-system effectiveness and prediction of the injury risk to occupants, has become an indispensible tool in car crash simulation.In the practice of dummy modeling, the most challenging work lies in material parameters calibration through validation of the model[1][2][3]. Traditional experience is a manually repeated parameter value-adjustment and model-calculation iteration, which is proceeding relatively slowly and extremely labor intensive. This method may be suitable for application to the simulation case in which not many design parameters get involved. The process, however, becomes highly inefficient when the number of model design parameters remarkably increases. Taking Hybrid III head-neck modeling for example, there are two loading conditions (flexion and extension), more than six design variables, and up to six performance requirements which are extremely challenging to satisfy through validation by traditional mean. Although optimization methodology is increasingly used in dummy modeling, the difficulty in validation of the Hybrid III neck bending response remains frequently observed by some authors[4][5] [6].Response surface methodology is currently one of the most prevalent optimization technologies. The method is capable of capturing globally optimal regions because of its smoothness and global approximation properties[7]. Therefore, the RSM is particularly applicable to the problem with many design variables to find the global optimal solution.In this paper, the response surface-based optimization is applied to the validation of a Hybrid III head-neck sub-assembly FE model by combined use of PAM-CRASH (ESI Group, Paris, France) and HyperStudy (Altair Engineering, Inc., USA). In the study, a total of twelve design variables are properly defined for assessment of their possible impacts to the head-neck response. Design of experiment is then carried out to screen out influencing factors, followed by optimization of the model under flexion loading condition. Results show that, response of the head-neck model with the optimal set of variable values correlates well with the experimental results.2. Response surface methodology (RSM)Response surface methodology (RSM) is the process, in which the construction of polynomial approximation function (response surface) based on response values calculated from a sequence of previous designed experiments is continued until an optimal response along with corresponding optimal set of factor levels are determined. A typical example of response surface is shown in Figure 1. The relationship between response value yand design variables x is given by1(,...,)n y f x x e=+ (1)Where1(,...,)n f x x , a function of independent design variables, is referred to as theresponse surface. The predictive capability of the response surface for optimization is largely dominated by the mathematical approximation model. Least squares fitting algorithm is commonly used to build the approximation model. Generally, first-order polynomial model (Eq. (2)) is used to estimate the response value which is far away from extrema, while the response value nearby the extrema is estimated by a higher accuracy model, second-order polynomial model (Eq. (3)). 1011(,...,)...n n nf x x x x βββ≈+++ (2)21011(,...,)nnnn j j jj j ij i jj j i jf x x x x x x ββββ==<≈+++∑∑∑ (3)RSM-based optimization engine in HyperStudy is called Adaptive Response Surface Method (ARSM)[8]. In this approach, a second-order polynomial function is used to approximate the objective and constraint functions, as given by 0()() 1, (1)nnj j ji i jii i k j iikg x g x x x x j m βββ∧≈=++=+∑∑∑ (4)where m is the number of constraints, n is the number of design variables, and 0j ji jii βββare the polynomial coefficients. The principle of determining an optimal design with the ARSM is schematically described by taking a one-dimensional optimization problem as example, as illustrated in Figure 1. In this case, the target is to determine the minima of the unknown function()f x through successiveapproximation within a given design variables space. At the start of the optimization process, a first-order response surface (RS1) is first constructed based on the starting response value (0) and a neighboring response value (1). A new response value (2) is then calculated with the design variable determined from the maxima on RS1. This is followed by construction of a second-order polynomial approximation (RS2) through fitting to the previously calculated response values. Then, the optimum on the RS2 as well as corresponding design variable value is determined and a new responsevalue (3) is calculated. Construction of second-order response surface based on the previously calculated response values is then repeated (RS3, RS4, RS5, etc) in each iteration until the solution of the function()f xconverges.R e s p o n s eResponse surfaceD e s i gnv a r i ab l e 1D es ig n v a r i a b l e 2ResponseX l Xu 2346 51Xf(X)Figure 1 Response surface (left) and principle of Adaptive Response Surface Method (right)3. RSM-based Optimization of the Head-Neck FF Model3.1Model description and problem definitionThe head-neck model was built in great detail to represent the hardware as realistically as possible. The total entities in the model comprise 28 parts, 7547 nodes and 5186 elements. Element size is controlled to maintain the time step larger than 1 microsecond for the purpose of computational efficiency. The model geometry properties comply with the specifications of the hardware in terms of external dimension, mass and inertia. Material properties of flexible parts are defined using appropriate material types available in PAM-CRASH [9][10].Linear visco-elastic material (type 5) and non-linear tension only bar (type 205) are used to model rubber material (head vinyl skin and the neck rubber) and neck cable, respectively. Upper neck load cell and occipital condyle (OC) joint are modeled with locked spherical joint (type 221) and revolutional joint (type 230), respectively. Among those material types, a number of material parameters which have tendency to affect the head-neck response are defined as design variables (Table 1).The head-neck model is properly setup for simulation of neck flexion (Table 2). Simulation starts from the lowest point when the pendulum in test first comes into contact with the honeycomb. An initial angular velocity is applied to the whole model while the pendulum’s motion is constrained by a given angular velocity time history, which is derived from the linear acceleration pulse. Head pot and pendulum pot arerealistically modeled to capture the rotational angle of the D-plane with respect to the pendulum. Upper neck load cell and OC joint outputs related variables, like force along x-axis, moment around y-axis and resultant moment of OC joint. All signals are filtered properly in accordance with the specifications of the dummy regulation for comparison with the experimental results [11].Five responses are defined in this optimization case, namely peak rotational angle of D-plane, time to peak P-plane rotational angle, peak moment of OC joint, time to peak OC moment and peak integral area of D-plane angle vs. time. Performance requirements to above former four responses (r1, r2, r3, r4) are defined as constraints while the remaining response (r5) is defined as the objective. The task of the optimization process is to approach a given target value while satisfying constraints functions through system identification.Table 1 Design variables for design of experiments (DOE)Design variablesOC jointNeck cable and neck rubber discs OC_H Hysterisis for OC unloadingCA_HHysterisis for cable unloading OC_DE Damping curve slope_extension(Nm/ms)*CA_KCable stiffness (kN)** OC_DFDaming curve slope_flexion (Nm.ms)NE_BDecay constantOC_FM Friction moment (Nm) NE_G0 Short time shear modulus (GPa) OC_SF Loading stiffness_flexion (Nm/deg) NE_GF Long time shear modulus (GPa) OC_SELoading stiffness_extension (Nm/deg)NE_KBulk modulus (GPa)*OC joint damping stiffness is defined as resistant moment per unit angular velocity **Cable stiffness is defined as resistance force per unit strainTable 2 Model setup and the problem definitionModel setupResponse definitionObjective andconstraintHead PotNeck rubberSym. unitExplanationLowerboundUpper bound r1degPeak rotational angleof D-plane6478r2 msTime to peak D-planerotational angle57 64 r3NmPeak moment of OC joint88108.4r4 ms Time to peak OC moment 47 58r5* deg.msPeak integral area ofD-plane angle vs. time Objective target**4422* response r5 is intended for evaluation of the time for D-plane decaying to zero**The target value 4422 is calculated from experimental curve3.2 Design of experiment (DOE) and main effects analysisSince a large number of design variables are involved, design of experiments byfull factorial design would be far too expensive. Therefore, two-level fractional factorial design is conducted for screening experiments, regardless of interaction effects. Table 3gives the experiments design, in which variable values increase to varying degrees from level “1” to level “2”.Key results from DOE are main effects of factors, which are defined as average variation in response ((2)(1)y y−) with change of design variable value. Influence of design variables on each response are schematically illustrated in Figure 2.It is observed from Figure 2 (a), (b) and (e) that, neck rubber short time shear modulus, decay constant and the cable stiffness are the top three influencing factorson the maximum D-plane rotation angle and the timing as well as time for D-plane decay to zero, which are insignificantly impacted by OC joint material properties. However, the maximum moment of OC joint is clearly varied with material propertiesof OC joint as well as the neck rubber and the neck cable, by observation of Figure 2 (c). In more detail, neck rubber short time shear modulus, decay constant and OC joint flexion stiffness are the top three influencing factors, followed by friction moment,joint damping stiffness and the cable stiffness. As for time related responses, as canbe seen from Figure 2 (b), (d) and (e), the stiffer the head-neck system, the earlier the time.Through screening experiments, significant influencing factors are identified, such as short time shear modulus, neck cable stiffness, OC joint damping stiffness, etc. Controlling these design variables is the top priority in the neck modeling. Insignificant influencing factors including hysteresis for OC unloading, hysteresis for cable unloading and neck rubber bulk modulus are defined as constant in subsequent optimization.Table 3 Experiments with two-level fractional factorial design (L16)Run OC_H OC_DE OC_DF OC_FM OC_SF OC_SE CA_H CA_K NE_B NE_G0* NE_K1 1** 1 1 12 1 1 2 1 2 2 2 2 1 1 1 1 1 2 2 2 2 13 1 2 1 1 1 2 1 2 2 1 24 2 2 1 1 2 2 2 2 1 1 15 1 1 2 1 1 2 2 1 1 2 26 2 1 2 1 2 2 1 1 2 2 17 1 2 2 1 2 1 2 1 2 1 28 2 2 2 1 1 1 1 1 1 1 19 1 1 1 2 1 2 2 1 2 1 1 10 2 1 1 2 2 2 1 1 1 1 2 11 1 2 1 2 2 1 2 1 1 2 1 12 2 2 1 2 1 1 1 1 2 2 2 13 1 1 2 2 2 1 1 2 2 1 1 14 2 1 2 2 1 1 2 2 1 1 2 15 1 2 2 2 1 2 1 2 1 2 1 1622222222222*NE_GF is linked to NE_G0 whose value must be larger than NE_GF according to visco-elastic behavior** Level “1” represents typical values with reference to public sourceM a i n e f f e c t s (d e g )M a i n e f f e c t s (m s )(a) r1(b) r2M a i n e f f e c t s (k N .m m )M a i n e f f e c t s (m s )(c) r3(d) r4M a i n e f f e c t s (d e g .m s )(e) r5Figure 2 Main effects of factors for each response3.3 Model optimization and validation resultsBased on the results of DOE, an optimization of the neck model is launched. Solution converges after 87 iterations. The objective and constraints time histories are shown in Figure 3. Figure 4 gives the kinematics of the head-neck sub-assembly with the optimal set of material parameters. Validation results, as displayed in Figure 5, indicate that the model with optimal design correlates reasonably with the experimental results. Note that experimental and modeling results are presented normalized.204060801002000250030003500400045005000Number of iterations r5Number of iterationsFigure 3 Iteration histories of the objective function (left) and the constraints functions (right)Figure 4 Kinematics of the head-neck model under flexion loadingNormalizedrotationalangleNormalized timeNormalizedupperneckloadcellFxNormalized time(a) D-plane rotation angle (b) Upper neck load cell force in x axisNormalizedupperneckloadcellMyNormalized timeNormalizedmomentofOCjointNormalized time(c) Upper neck load cell moment around y axis (d) Resultant moment of OC jointFigure 5 Correlation of the neck flexion modeling to experimental results*Corridor is indicated by shaded area4. ConclusionsIn this paper, the current status of model validation of the Hybrid III head-neck sub-assembly is presented. Through screening experiments and multi-objective optimization, design variables effects are assessed and reasonable correlation of the model under flexion loading to the certification test results is achieved. Next step,reliability of currently obtained optimal set of material parameters setting for other loading conditions, say extension and low velocity loading, will be evaluated. This is then followed by an integral optimization process in which multiple models with the same material parameters setting are simultaneously calculated in each iteration.The response surface-based optimization capability of HyperStudy has proven to be very effective and of high efficiency for the optimization of the dummy head-neck validation, which is highly complex as many design variables and a number of constraints are involved. The optimization practice in this study demonstrates that the response surface-based optimization is a very promising methodology in dummy model development.5. References[1] Khalil T B, Lin T C. Simulation of the Hybrid III dummy response to impact by nonlinearfinite element analysis. SAE Technical Paper No. 942227, 1994[2] Arnoux P J and Joonekindt S, et al. RADIOSS finite element model of the Thor dummy.International Journal of Crashworthiness 8(6): 529-541, 2003[3] Yu H, Zhou Q, Neat G W. Three-dimensional finite element modelling of the torso of theanthropomorphic test device THOR. International Journal of Vehicle Safety 2(1-2):116-140, 2007[4] Yang K H. and Le J L. Finite element modeling of Hybrid III head-neck complex. SAETechnical Paper No. 922526.1992[5] Noureddine A, Eskandarian A, Digges K. Computer modeling and validation of a Hybrid IIIdummy for crashworthiness simulation. Mathematical and Computer Modelling (35):885-893, 2002[6] Mohan P and Marzougui D, et al. Development and Validation of Hybrid III Crash TestDummy. SAE Technical Paper No. 2009-01-0473, 2009[7] LSTC, Inc. LS-OPT 4.1 User's Manual, 2010[8] Altair Engineering, Inc. HyperStudy 10.0 User's Guide, 2010[9] ESI Group. Virtual Performance Solutions-Explicit Solver Notes Manual, 2008[10] ESI Group. Virtual Performance Solutions- Explicit Solver Reference Manual, 2008[11] NHTSA 49 CFR 49 572.31 Subpart E-Hybrid III Test Dummy。

生物制药下游工艺及验证策略

4 mm
Sartobind Jumbo
POLISHING/B&E
8 mm
Void volume optimized
Sartobind System
Sartobind Direct
Selection guide Membrane Chromatography
Low salt
Q, S
Polishing
冻融系统
CELSIUS® CFT Controlled freeze and thaw Qualified shippers
CELSIUS®-Pak Pre-sterilized, single-use containers S71 film
CELSIUS® FFT Flexible freeze and thaw S71 film, Qualified shippers
Secondary Recovery Recovery using Sartoclear® P Technologies
Secondary Recovery Cell Clarification
The Potential of Depth Filtration – Case Study 1
Post Centrifuge Application: turbidity= 205 NTU, titter= 1,5 mg/ml
Turbidity filtrate Pressure increase Processed Volume Prot. A Eluate Neutralised Prot. A Eluate CHOP
Filter A 1,7 NTU 1,2 bar 200 L/m2 140 NTU 700 NTU 32000 ppm

（翻译）关节镜下半月板部分切除术与假手术治疗退行性半月板撕裂

（翻译）关节镜下半⽉板部分切除术与假⼿术治疗退⾏性半⽉板撕裂患者的特点有205个患者符合标准，其中的59个患者被排除；图⼀给出了排除原因。

将146患者进⾏随机分配，70位患者进⾏关节镜下半⽉板部分切除术，76位患者进⾏假性⼿术。

两组成员的基本情况相似（在增补附录的表⼀和表⼆）。

谢绝参加的患者与那些通过年龄、性别、体重指数进⾏随机分配的患者情况相似，以及与参与关节镜下半⽉板部分切除术的全部患者情况相似。

后续随访中患者数量没有减少。

图1 主要结果虽然从术后开始到术后12个⽉两组中的三个主要评分结果都有很明显的提⾼（图2和表2），但是两组之间在其他⽅⾯的变化没有明显的差别。

两组之间的Lysholm膝关节评分的平均数差别-1.6分（95%的置信区间），WOMET评分的平均数差别-2.5分，运动后膝关节疼痛分数的平均分相差-0.1分（表2）。

这些结果在随机分组的基线分数和分层变量调整后没有实质性的变化。

（表S3）第⼆阶段和其他的结果在第⼆阶段两组之间的结果、随后的膝盖⼿术需要的频率和严重的不利事件没有明显的差别（表2和表S4）。

同时在亚组分析时，在术后12个⽉即研究⼩组通过Kellgren–Lawrence分级分层以后的主要评分结果中没有发现明显的组间差异，对分级也没有明显的影响。

此外，在事后亚组分析中我们同样发现对于症状突然发作的患者进⾏关节镜下半⽉板部分切除术和假性⼿术，结果表明关节镜下半⽉板部分切除术的作⽤效果相⽐于假性⼿术没有明显的益处(表S6)。

在半⽉板部分切除术组的2位患者和假性⼿术组的5位患者认为在术后症状持续存在，由于症状⾜够严重以致不得将研究⼩组的分配⽅法揭露（平均在术后8个⽉），同时对他们进⾏另外的⼿术。

两位关节镜半⽉板部分切除术患者进⾏格外膝关节⼿术；其中的⼀位患者在术后10个⽉进⾏了全膝关节置换术，因为MRI证实股⾻内侧髁存在⽆菌坏疽；其余患者因为症状持续存在，在⼿术后5个⽉进⾏了第⼆次半⽉板切除术。

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