Decomposing Cross-Sectional Volatility

运筹学英汉词汇(0,1) normalized ――0-1规范化Aactivity ――工序additivity――可加性adjacency matrix――邻接矩阵adjacent――邻接aligned game――结盟对策analytic functional equation――分析函数方程approximation method――近似法arc ――弧artificial constraint technique ――人工约束法artificial variable――人工变量augmenting path――增广路avoid cycle method ――避圈法Bbackward algorithm――后向算法balanced transportation problem――产销平衡运输问题basic feasible solution ――基本可行解basic matrix――基阵basic solution ――基本解basic variable ――基变量basic ――基basis iteration ――换基迭代Bayes decision――贝叶斯决策big M method ――大M 法binary integer programming ――0-1整数规划binary operation――二元运算binary relation――二元关系binary tree――二元树binomial distribution――二项分布bipartite graph――二部图birth and death process――生灭过程Bland rule ――布兰德法则branch node――分支点branch――树枝bridge――桥busy period――忙期Ccapacity of system――系统容量capacity――容量Cartesian product――笛卡儿积chain――链characteristic function――特征函数chord――弦circuit――回路coalition structure――联盟结构coalition――联盟combination me――组合法complement of a graph――补图complement of a set――补集complementary of characteristic function――特征函数的互补性complementary slackness condition ――互补松弛条件complementary slackness property――互补松弛性complete bipartite graph――完全二部图complete graph――完全图completely undeterministic decision――完全不确定型决策complexity――计算复杂性congruence method――同余法connected component――连通分支connected graph――连通图connected graph――连通图constraint condition――约束条件constraint function ――约束函数constraint matrix――约束矩阵constraint method――约束法constraint ――约束continuous game――连续对策convex combination――凸组合convex polyhedron ――凸多面体convex set――凸集core――核心corner-point ――顶点（角点）cost coefficient――费用系数cost function――费用函数cost――费用criterion ; test number――检验数critical activity ――关键工序critical path method ――关键路径法（CMP ）critical path scheduling ――关键路径cross job ――交叉作业curse of dimensionality――维数灾customer resource――顾客源customer――顾客cut magnitude ――截量cut set ――截集cut vertex――割点cutting plane method ――割平面法cycle ――回路cycling ――循环Ddecision fork――决策结点decision maker决――策者decision process of unfixed step number――不定期决策过程decision process――决策过程decision space――决策空间decision variable――决策变量decision决－－策decomposition algorithm――分解算法degenerate basic feasible solution ――退化基本可行解degree――度demand――需求deterministic inventory model――确定贮存模型deterministic type decision――确定型决策diagram method ――图解法dictionary ordered method ――字典序法differential game――微分对策digraph――有向图directed graph――有向图directed tree――有向树disconnected graph――非连通图distance――距离domain――定义域dominate――优超domination of strategies――策略的优超关系domination――优超关系dominion――优超域dual graph――对偶图Dual problem――对偶问题dual simplex algorithm ――对偶单纯形算法dual simplex method――对偶单纯形法dummy activity――虚工序dynamic game――动态对策dynamic programming――动态规划Eearliest finish time――最早可能完工时间earliest start time――最早可能开工时间economic ordering quantity formula――经济定购批量公式edge ――边effective set――有效集efficient solution――有效解efficient variable――有效变量elementary circuit――初级回路elementary path――初级通路elementary ――初等的element――元素empty set――空集entering basic variable ――进基变量equally liability method――等可能性方法equilibrium point――平衡点equipment replacement problem――设备更新问题equipment replacing problem――设备更新问题equivalence relation――等价关系equivalence――等价Erlang distribution――爱尔朗分布Euler circuit――欧拉回路Euler formula――欧拉公式Euler graph――欧拉图Euler path――欧拉通路event――事项expected value criterion――期望值准则expected value of queue length――平均排队长expected value of sojourn time――平均逗留时间expected value of team length――平均队长expected value of waiting time――平均等待时间exponential distribution――指数分布external stability――外部稳定性Ffeasible basis ――可行基feasible flow――可行流feasible point――可行点feasible region ――可行域feasible set in decision space――决策空间上的可行集feasible solution――可行解final fork――结局结点final solution――最终解finite set――有限集合flow――流following activity ――紧后工序forest――森林forward algorithm――前向算法free variable ――自由变量function iterative method――函数迭代法functional basic equation――基本函数方程function――函数fundamental circuit――基本回路fundamental cut-set――基本割集fundamental system of cut-sets――基本割集系统fundamental system of cut-sets――基本回路系统Ggame phenomenon――对策现象game theory――对策论game――对策generator――生成元geometric distribution――几何分布goal programming――目标规划graph theory――图论graph――图HHamilton circuit――哈密顿回路Hamilton graph――哈密顿图Hamilton path――哈密顿通路Hasse diagram――哈斯图hitchock method ――表上作业法hybrid method――混合法Iideal point――理想点idle period――闲期implicit enumeration method――隐枚举法in equilibrium――平衡incidence matrix――关联矩阵incident――关联indegree――入度indifference curve――无差异曲线indifference surface――无差异曲面induced subgraph――导出子图infinite set――无限集合initial basic feasible solution ――初始基本可行解initial basis ――初始基input process――输入过程Integer programming ――整数规划inventory policy―v存贮策略inventory problem―v货物存储问题inverse order method――逆序解法inverse transition method――逆转换法isolated vertex――孤立点isomorphism――同构Kkernel――核knapsack problem ――背包问题Llabeling method ――标号法latest finish time――最迟必须完工时间leaf――树叶least core――最小核心least element――最小元least spanning tree――最小生成树leaving basic variable ――出基变量lexicographic order――字典序lexicographic rule――字典序lexicographically positive――按字典序正linear multiobjective programming――线性多目标规划Linear Programming Model――线性规划模型Linear Programming――线性规划local noninferior solution――局部非劣解loop method――闭回路loop――圈loop――自环（环）loss system――损失制Mmarginal rate of substitution――边际替代率Marquart decision process――马尔可夫决策过程matching problem――匹配问题matching――匹配mathematical programming――数学规划matrix form ――矩阵形式matrix game――矩阵对策maximum element――最大元maximum flow――最大流maximum matching――最大匹配middle square method――平方取中法minimal regret value method――最小后悔值法minimum-cost flow――最小费用流mixed expansion――混合扩充mixed integer programming ――混合整数规划mixed Integer programming――混合整数规划mixed Integer ――混合整数规划mixed situation――混合局势mixed strategy set――混合策略集mixed strategy――混合策略mixed system――混合制most likely estimate――最可能时间multigraph――多重图multiobjective programming――多目标规划multiobjective simplex algorithm――多目标单纯形算法multiple optimal solutions ――多个最优解multistage decision problem――多阶段决策问题multistep decision process――多阶段决策过程Nn- person cooperative game ――n人合作对策n- person noncooperative game――n人非合作对策n probability distribution of customer arrive――顾客到达的n 概率分布natural state――自然状态nature state probability――自然状态概率negative deviational variables――负偏差变量negative exponential distribution――负指数分布network――网络newsboy problem――报童问题no solutions ――无解node――节点non-aligned game――不结盟对策nonbasic variable ――非基变量nondegenerate basic feasible solution――非退化基本可行解nondominated solution――非优超解noninferior set――非劣集noninferior solution――非劣解nonnegative constrains ――非负约束non-zero-sum game――非零和对策normal distribution――正态分布northwest corner method ――西北角法n-person game――多人对策nucleolus――核仁null graph――零图Oobjective function ――目标函数objective( indicator) function――指标函数one estimate approach――三时估计法operational index――运行指标operation――运算optimal basis ――最优基optimal criterion ――最优准则optimal solution ――最优解optimal strategy――最优策略optimal value function――最优值函数optimistic coefficient method――乐观系数法optimistic estimate――最乐观时间optimistic method――乐观法optimum binary tree――最优二元树optimum service rate――最优服务率optional plan――可供选择的方案order method――顺序解法ordered forest――有序森林ordered tree――有序树outdegree――出度outweigh――胜过Ppacking problem ――装箱问题parallel job――平行作业partition problem――分解问题partition――划分path――路path――通路pay-off function――支付函数payoff matrix――支付矩阵payoff――支付pendant edge――悬挂边pendant vertex――悬挂点pessimistic estimate――最悲观时间pessimistic method――悲观法pivot number ――主元plan branch――方案分支plane graph――平面图plant location problem――工厂选址问题player――局中人Poisson distribution――泊松分布Poisson process――泊松流policy――策略polynomial algorithm――多项式算法positive deviational variables――正偏差变量posterior――后验分析potential method ――位势法preceding activity ――紧前工序prediction posterior analysis――预验分析prefix code――前级码price coefficient vector ――价格系数向量primal problem――原问题principal of duality ――对偶原理principle of optimality――最优性原理prior analysis――先验分析prisoner’s dilemma――囚徒困境probability branch――概率分支production scheduling problem――生产计划program evaluation and review technique――计划评审技术(PERT) proof――证明proper noninferior solution――真非劣解pseudo-random number――伪随机数pure integer programming ――纯整数规划pure strategy――纯策略Qqueue discipline――排队规则queue length――排队长queuing theory――排队论Rrandom number――随机数random strategy――随机策略reachability matrix――可达矩阵reachability――可达性regular graph――正则图regular point――正则点regular solution――正则解regular tree――正则树relation――关系replenish――补充resource vector ――资源向量revised simplex method――修正单纯型法risk type decision――风险型决策rooted tree――根树root――树根Ssaddle point――鞍点saturated arc ――饱和弧scheduling (sequencing) problem――排序问题screening method――舍取法sensitivity analysis ――灵敏度分析server――服务台set of admissible decisions(policies) ――允许决策集合set of admissible states――允许状态集合set theory――集合论set――集合shadow price ――影子价格shortest path problem――最短路线问题shortest path――最短路径simple circuit――简单回路simple graph――简单图simple path――简单通路Simplex method of goal programming――目标规划单纯形法Simplex method ――单纯形法Simplex tableau――单纯形表single slack time ――单时差situation――局势situation――局势slack variable ――松弛变量sojourn time――逗留时间spanning graph――支撑子图spanning tree――支撑树spanning tree――生成树stable set――稳定集stage indicator――阶段指标stage variable――阶段变量stage――阶段standard form――标准型state fork――状态结点state of system――系统状态state transition equation――状态转移方程state transition――状态转移state variable――状态变量state――状态static game――静态对策station equilibrium state――统计平衡状态stationary input――平稳输入steady state――稳态stochastic decision process――随机性决策过程stochastic inventory method――随机贮存模型stochastic simulation――随机模拟strategic equivalence――策略等价strategic variable, decision variable ――决策变量strategy (policy) ――策略strategy set――策略集strong duality property ――强对偶性strong ε-core――强ε-核心strongly connected component――强连通分支strongly connected graph――强连通图structure variable ――结构变量subgraph――子图sub-policy――子策略subset――子集subtree――子树surplus variable ――剩余变量surrogate worth trade-off method――代替价值交换法symmetry property ――对称性system reliability problem――系统可靠性问题Tteam length――队长tear cycle method――破圈法technique coefficient vector ――技术系数矩阵test number of cell ――空格检验数the branch-and-bound technique ――分支定界法the fixed-charge problem ――固定费用问题three estimate approach一―时估计法total slack time――总时差traffic intensity――服务强度transportation problem ――运输问题traveling salesman problem――旅行售货员问题tree――树trivial graph――平凡图two person finite zero-sum game二人有限零和对策two-person game――二人对策two-phase simplex method ――两阶段单纯形法Uunbalanced transportation problem ――产销不平衡运输问题unbounded ――无界undirected graph――无向图uniform distribution――均匀分布unilaterally connected component――单向连通分支unilaterally connected graph――单向连通图union of sets――并集utility function――效用函数Vvertex――顶点voting game――投票对策Wwaiting system――等待制waiting time――等待时间weak duality property ――弱对偶性weak noninferior set――弱非劣集weak noninferior solution――弱非劣解weakly connected component――弱连通分支weakly connected graph――弱连通图weighed graph ――赋权图weighted graph――带权图weighting method――加权法win expectation――收益期望值Zzero flow――零流zero-sum game――零和对策zero-sum two person infinite game――二人无限零和对策。

decay of correlation 数学名词Decay of correlation（相关性的衰减）refers to the decrease in correlation between two variables as the distance between them increases. It is a mathematical concept used to quantify the relationship between two variables across different spatial or temporal distances.1. The decay of correlation between rainfall and crop yield was observed as the distance between the two fields increased.雨量与农作物产量之间的相关性随着两个田地之间的距离增加而减弱。

2. The study analyzed the decay of correlation between interest rates and stock market performance over a one-year timespan.该研究分析了利率和股市表现之间的相关性在一年的时间内是如何衰减的。

3. As the distance between two cities increased, thedecay of correlation between their population sizes became more noticeable.随着两个城市之间的距离增加，它们的人口规模之间的相关性衰减变得更加明显。

4. The researchers used statistical methods to determine the decay of correlation between air pollution andrespiratory diseases in different neighborhoods.研究人员使用统计方法来确定不同社区之间空气污染和呼吸道疾病之间的相关性衰减。

SPSS术语中英文对照【常用软件】SPSS术语中英文对照Absolute deviation, 绝对离差Absolute number, 绝对数Absolute residuals, 绝对残差Acceleration array, 加速度立体阵Acceleration in an arbitrary direction, 任意方向上的加速度Acceleration normal, 法向加速度Acceleration space dimension, 加速度空间的维数Acceleration tangential, 切向加速度Acceleration vector, 加速度向量Acceptable hypothesis, 可接受假设Accumulation, 累积Accuracy, 准确度Actual frequency, 实际频数Adaptive estimator, 自适应估计量Addition, 相加Addition theorem, 加法定理Additivity, 可加性Adjusted rate, 调整率Adjusted value, 校正值Admissible error, 容许误差Aggregation, 聚集性Alternative hypothesis, 备择假设Among groups, 组间Amounts, 总量Analysis of correlation, 相关分析Analysis of covariance, 协方差分析Analysis of regression, 回归分析Analysis of time series, 时间序列分析Analysis of variance, 方差分析Angular transformation, 角转换ANOVA （analysis of variance）, 方差分析ANOVA Models, 方差分析模型Arcing, 弧/弧旋Arcsine transformation, 反正弦变换Area under the curve, 曲线面积AREG , 评估从一个时间点到下一个时间点回归相关时的误差ARIMA, 季节和非季节性单变量模型的极大似然估计Arithmetic grid paper, 算术格纸Arithmetic mean, 算术平均数Arrhenius relation, 艾恩尼斯关系Assessing fit, 拟合的评估Associative laws, 结合律Asymmetric distribution, 非对称分布Asymptotic bias, 渐近偏倚Asymptotic efficiency, 渐近效率Asymptotic variance, 渐近方差Attributable risk, 归因危险度Attribute data, 属性资料Attribution, 属性Autocorrelation, 自相关Autocorrelation of residuals, 残差的自相关Average, 平均数Average confidence interval length, 平均置信区间长度Average growth rate, 平均增长率Bar chart, 条形图Bar graph, 条形图Base period, 基期Bayes' theorem , Bayes定理Bell-shaped curve, 钟形曲线Bernoulli distribution, 伯努力分布Best-trim estimator, 最好切尾估计量Bias, 偏性Binary logistic regression, 二元逻辑斯蒂回归Binomial distribution, 二项分布Bisquare, 双平方Bivariate Correlate, 二变量相关Bivariate normal distribution, 双变量正态分布Bivariate normal population, 双变量正态总体Biweight interval, 双权区间Biweight M-estimator, 双权M估计量Block, 区组/配伍组BMDP(Biomedical computer programs), BMDP统计软件包Boxplots, 箱线图/箱尾图Breakdown bound, 崩溃界/崩溃点Canonical correlation, 典型相关Caption, 纵标目Case-control study, 病例对照研究Categorical variable, 分类变量Catenary, 悬链线Cauchy distribution, 柯西分布Cause-and-effect relationship, 因果关系Cell, 单元Censoring, 终检Center of symmetry, 对称中心Centering and scaling, 中心化和定标Central tendency, 集中趋势Central value, 中心值CHAID -χ2 Automatic Interac tion Detector, 卡方自动交互检测Chance, 机遇Chance error, 随机误差Chance variable, 随机变量Characteristic equation, 特征方程Characteristic root, 特征根Characteristic vector, 特征向量Chebshev criterion of fit, 拟合的切比雪夫准则Chernoff faces, 切尔诺夫脸谱图Chi-square test, 卡方检验/χ2检验Choleskey decomposition, 乔洛斯基分解Circle chart, 圆图Class interval, 组距Class mid-value, 组中值Class upper limit, 组上限Classified variable, 分类变量Cluster analysis, 聚类分析Cluster sampling, 整群抽样Code, 代码Coded data, 编码数据Coding, 编码Coefficient of contingency, 列联系数Coefficient of determination, 决定系数Coefficient of multiple correlation, 多重相关系数Coefficient of partial correlation, 偏相关系数Coefficient of production-moment correlation, 积差相关系数Coefficient of rank correlation, 等级相关系数Coefficient of regression, 回归系数Coefficient of skewness, 偏度系数Coefficient of variation, 变异系数Cohort study, 队列研究Column, 列Column effect, 列效应Column factor, 列因素Combination pool, 合并Combinative table, 组合表Common factor, 共性因子Common regression coefficient, 公共回归系数Common value, 共同值Common variance, 公共方差Common variation, 公共变异Communality variance, 共性方差Comparability, 可比性Comparison of bathes, 批比较Comparison value, 比较值Compartment model, 分部模型Compassion, 伸缩Complement of an event, 补事件Complete association, 完全正相关Complete dissociation, 完全不相关Complete statistics, 完备统计量Completely randomized design, 完全随机化设计Composite event, 联合事件Composite events, 复合事件Concavity, 凹性Conditional expectation, 条件期望Conditional likelihood, 条件似然Conditional probability, 条件概率Conditionally linear, 依条件线性Confidence interval, 置信区间Confidence limit, 置信限Confidence lower limit, 置信下限Confidence upper limit, 置信上限Confirmatory Factor Analysis , 验证性因子分析Confirmatory research, 证实性实验研究Confounding factor, 混杂因素Conjoint, 联合分析Consistency, 相合性Consistency check, 一致性检验Consistent asymptotically normal estimate, 相合渐近正态估计Consistent estimate, 相合估计Constrained nonlinear regression, 受约束非线性回归Constraint, 约束Contaminated distribution, 污染分布Contaminated Gausssian, 污染高斯分布Contaminated normal distribution, 污染正态分布Contamination, 污染Contamination model, 污染模型Contingency table, 列联表Contour, 边界线Contribution rate, 贡献率Control, 对照Controlled experiments, 对照实验Conventional depth, 常规深度Convolution, 卷积Corrected factor, 校正因子Corrected mean, 校正均值Correction coefficient, 校正系数Correctness, 正确性Correlation coefficient, 相关系数Correlation index, 相关指数Correspondence, 对应Counting, 计数Counts, 计数/频数Covariance, 协方差Covariant, 共变Cox Regression, Cox回归Criteria for fitting, 拟合准则Criteria of least squares, 最小二乘准则Critical ratio, 临界比Critical region, 拒绝域Critical value, 临界值Cross-over design, 交叉设计Cross-section analysis, 横断面分析Cross-section survey, 横断面调查Crosstabs , 交叉表Cross-tabulation table, 复合表Cube root, 立方根Cumulative distribution function, 分布函数Cumulative probability, 累计概率Curvature, 曲率/弯曲Curvature, 曲率Curve fit , 曲线拟和Curve fitting, 曲线拟合Curvilinear regression, 曲线回归Curvilinear relation, 曲线关系Cut-and-try method, 尝试法Cycle, 周期Cyclist, 周期性D test, D检验Data acquisition, 资料收集Data bank, 数据库Data capacity, 数据容量Data deficiencies, 数据缺乏Data handling, 数据处理Data manipulation, 数据处理Data processing, 数据处理Data reduction, 数据缩减Data set, 数据集Data sources, 数据来源Data transformation, 数据变换Data validity, 数据有效性Data-in, 数据输入Data-out, 数据输出Dead time, 停滞期Degree of freedom, 自由度Degree of precision, 精密度Degree of reliability, 可靠性程度Degression, 递减Density function, 密度函数Density of data points, 数据点的密度Dependent variable, 应变量/依变量/因变量Dependent variable, 因变量Depth, 深度Derivative matrix, 导数矩阵Derivative-free methods, 无导数方法Design, 设计Determinacy, 确定性Determinant, 行列式Determinant, 决定因素Deviation, 离差Deviation from average, 离均差Diagnostic plot, 诊断图Dichotomous variable, 二分变量Differential equation, 微分方程Direct standardization, 直接标准化法Discrete variable, 离散型变量DISCRIMINANT, 判断Discriminant analysis, 判别分析Discriminant coefficient, 判别系数Discriminant function, 判别值Dispersion, 散布/分散度Disproportional, 不成比例的Disproportionate sub-class numbers, 不成比例次级组含量Distribution free, 分布无关性/免分布Distribution shape, 分布形状Distribution-free method, 任意分布法Distributive laws, 分配律Disturbance, 随机扰动项Dose response curve, 剂量反应曲线Double blind method, 双盲法Double blind trial, 双盲试验Double exponential distribution, 双指数分布Double logarithmic, 双对数Downward rank, 降秩Dual-space plot, 对偶空间图DUD, 无导数方法Duncan's new multiple range method, 新复极差法/Duncan新法Effect, 实验效应Eigenvalue, 特征值Eigenvector, 特征向量Ellipse, 椭圆Empirical distribution, 经验分布Empirical probability, 经验概率单位Enumeration data, 计数资料Equal sun-class number, 相等次级组含量Equally likely, 等可能Equivariance, 同变性Error, 误差/错误Error of estimate, 估计误差Error type I, 第一类错误Error type II, 第二类错误Estimand, 被估量Estimated error mean squares, 估计误差均方Estimated error sum of squares, 估计误差平方和Euclidean distance, 欧式距离Event, 事件Event, 事件Exceptional data point, 异常数据点Expectation plane, 期望平面Expectation surface, 期望曲面Expected values, 期望值Experiment, 实验Experimental sampling, 试验抽样Experimental unit, 试验单位Explanatory variable, 说明变量Exploratory data analysis, 探索性数据分析Explore Summarize, 探索-摘要Exponential curve, 指数曲线Exponential growth, 指数式增长EXSMOOTH, 指数平滑方法Extended fit, 扩充拟合Extra parameter, 附加参数Extrapolation, 外推法Extreme observation, 末端观测值Extremes, 极端值/极值F distribution, F分布F test, F检验Factor, 因素/因子Factor analysis, 因子分析Factor Analysis, 因子分析Factor score, 因子得分Factorial, 阶乘Factorial design, 析因试验设计False negative, 假阴性False negative error, 假阴性错误Family of distributions, 分布族Family of estimators, 估计量族Fanning, 扇面Fatality rate, 病死率Field investigation, 现场调查Field survey, 现场调查Finite population, 有限总体Finite-sample, 有限样本First derivative, 一阶导数First principal component, 第一主成分First quartile, 第一四分位数Fisher information, 费雪信息量Fitted value, 拟合值Fitting a curve, 曲线拟合Fixed base, 定基Fluctuation, 随机起伏Forecast, 预测Four fold table, 四格表Fourth, 四分点Fraction blow, 左侧比率Fractional error, 相对误差Frequency, 频率Frequency polygon, 频数多边图Frontier point, 界限点Function relationship, 泛函关系Gamma distribution, 伽玛分布Gauss increment, 高斯增量Gaussian distribution, 高斯分布/正态分布Gauss-Newton increment, 高斯-牛顿增量General census, 全面普查GENLOG (Generalized liner models), 广义线性模型Geometric mean, 几何平均数Gini's mean difference, 基尼均差GLM (General liner models), 一般线性模型Goodness of fit, 拟和优度/配合度Gradient of determinant, 行列式的梯度Graeco-Latin square, 希腊拉丁方Grand mean, 总均值Gross errors, 重大错误Gross-error sensitivity, 大错敏感度Group averages, 分组平均Grouped data, 分组资料Guessed mean, 假定平均数Half-life, 半衰期Hampel M-estimators, 汉佩尔M估计量Happenstance, 偶然事件Harmonic mean, 调和均数Hazard function, 风险均数Hazard rate, 风险率Heading, 标目Heavy-tailed distribution, 重尾分布Hessian array, 海森立体阵Heterogeneity, 不同质Heterogeneity of variance, 方差不齐Hierarchical classification, 组内分组Hierarchical clustering method, 系统聚类法High-leverage point, 高杠杆率点HILOGLINEAR, 多维列联表的层次对数线性模型Hinge, 折叶点Histogram, 直方图Historical cohort study, 历史性队列研究Holes, 空洞HOMALS, 多重响应分析Homogeneity of variance, 方差齐性Homogeneity test, 齐性检验Huber M-estimators, 休伯M估计量Hyperbola, 双曲线Hypothesis testing, 假设检验Hypothetical universe, 假设总体Impossible event, 不可能事件Independence, 独立性Independent variable, 自变量Index, 指标/指数Indirect standardization, 间接标准化法Individual, 个体Inference band, 推断带Infinite population, 无限总体Infinitely great, 无穷大Infinitely small, 无穷小Influence curve, 影响曲线Information capacity, 信息容量Initial condition, 初始条件Initial estimate, 初始估计值Initial level, 最初水平Interaction, 交互作用Interaction terms, 交互作用项Intercept, 截距Interpolation, 内插法Interquartile range, 四分位距Interval estimation, 区间估计Intervals of equal probability, 等概率区间Intrinsic curvature, 固有曲率Invariance, 不变性Inverse matrix, 逆矩阵Inverse probability, 逆概率Inverse sine transformation, 反正弦变换Iteration, 迭代Jacobian determinant, 雅可比行列式Joint distribution function, 分布函数Joint probability, 联合概率Joint probability distribution, 联合概率分布K means method, 逐步聚类法Kaplan-Meier, 评估事件的时间长度Kaplan-Merier chart, Kaplan-Merier图Kendall's rank correlation, Kendall等级相关Kinetic, 动力学Kolmogorov-Smirnove test, 柯尔莫哥洛夫-斯米尔诺夫检验Kruskal and Wallis test, Kruskal及Wallis检验/多样本的秩和检验/H检验Kurtosis, 峰度Lack of fit, 失拟Ladder of powers, 幂阶梯Lag, 滞后Large sample, 大样本Large sample test, 大样本检验Latin square, 拉丁方Latin square design, 拉丁方设计Leakage, 泄漏Least favorable configuration, 最不利构形Least favorable distribution, 最不利分布Least significant difference, 最小显著差法Least square method, 最小二乘法Least-absolute-residuals estimates, 最小绝对残差估计Least-absolute-residuals fit, 最小绝对残差拟合Least-absolute-residuals line, 最小绝对残差线Legend, 图例L-estimator, L估计量L-estimator of location, 位置L估计量L-estimator of scale, 尺度L估计量Level, 水平Life expectance, 预期期望寿命Life table, 寿命表Life table method, 生命表法Light-tailed distribution, 轻尾分布Likelihood function, 似然函数Likelihood ratio, 似然比line graph, 线图Linear correlation, 直线相关Linear equation, 线性方程Linear programming, 线性规划Linear regression, 直线回归Linear Regression, 线性回归Linear trend, 线性趋势Loading, 载荷Location and scale equivariance, 位置尺度同变性Location equivariance, 位置同变性Location invariance, 位置不变性Location scale family, 位置尺度族Log rank test, 时序检验Logarithmic curve, 对数曲线Logarithmic normal distribution, 对数正态分布Logarithmic scale, 对数尺度Logarithmic transformation, 对数变换Logic check, 逻辑检查Logistic distribution, 逻辑斯特分布Logit transformation, Logit转换LOGLINEAR, 多维列联表通用模型Lognormal distribution, 对数正态分布Lost function, 损失函数Low correlation, 低度相关Lower limit, 下限Lowest-attained variance, 最小可达方差LSD, 最小显著差法的简称Lurking variable, 潜在变量Main effect, 主效应Major heading, 主辞标目Marginal density function, 边缘密度函数Marginal probability, 边缘概率Marginal probability distribution, 边缘概率分布Matched data, 配对资料Matched distribution, 匹配过分布Matching of distribution, 分布的匹配Matching of transformation, 变换的匹配Mathematical expectation, 数学期望Mathematical model, 数学模型Maximum L-estimator, 极大极小L 估计量Maximum likelihood method, 最大似然法Mean, 均数Mean squares between groups, 组间均方Mean squares within group, 组内均方Means (Compare means), 均值-均值比较Median, 中位数Median effective dose, 半数效量Median lethal dose, 半数致死量Median polish, 中位数平滑Median test, 中位数检验Minimal sufficient statistic, 最小充分统计量Minimum distance estimation, 最小距离估计Minimum effective dose, 最小有效量Minimum lethal dose, 最小致死量Minimum variance estimator, 最小方差估计量MINITAB, 统计软件包Minor heading, 宾词标目Missing data, 缺失值Model specification, 模型的确定Modeling Statistics , 模型统计Models for outliers, 离群值模型Modifying the model, 模型的修正Modulus of continuity, 连续性模Morbidity, 发病率Most favorable configuration, 最有利构形Multidimensional Scaling (ASCAL), 多维尺度/多维标度Multinomial Logistic Regression , 多项逻辑斯蒂回归Multiple comparison, 多重比较Multiple correlation , 复相关Multiple covariance, 多元协方差Multiple linear regression, 多元线性回归Multiple response , 多重选项Multiple solutions, 多解Multiplication theorem, 乘法定理Multiresponse, 多元响应Multi-stage sampling, 多阶段抽样Multivariate T distribution, 多元T分布Mutual exclusive, 互不相容Mutual independence, 互相独立Natural boundary, 自然边界Natural dead, 自然死亡Natural zero, 自然零Negative correlation, 负相关Negative linear correlation, 负线性相关Negatively skewed, 负偏Newman-Keuls method, q检验NK method, q检验No statistical significance, 无统计意义Nominal variable, 名义变量Nonconstancy of variability, 变异的非定常性Nonlinear regression, 非线性相关Nonparametric statistics, 非参数统计Nonparametric test, 非参数检验Nonparametric tests, 非参数检验Normal deviate, 正态离差Normal distribution, 正态分布Normal equation, 正规方程组Normal ranges, 正常范围Normal value, 正常值Nuisance parameter, 多余参数/讨厌参数Null hypothesis, 无效假设Numerical variable, 数值变量Objective function, 目标函数Observation unit, 观察单位Observed value, 观察值One sided test, 单侧检验One-way analysis of variance, 单因素方差分析Oneway ANOVA , 单因素方差分析Open sequential trial, 开放型序贯设计Optrim, 优切尾Optrim efficiency, 优切尾效率Order statistics, 顺序统计量Ordered categories, 有序分类Ordinal logistic regression , 序数逻辑斯蒂回归Ordinal variable, 有序变量Orthogonal basis, 正交基Orthogonal design, 正交试验设计Orthogonality conditions, 正交条件ORTHOPLAN, 正交设计Outlier cutoffs, 离群值截断点Outliers, 极端值OVERALS , 多组变量的非线性正规相关Overshoot, 迭代过度Paired design, 配对设计Paired sample, 配对样本Pairwise slopes, 成对斜率Parabola, 抛物线Parallel tests, 平行试验Parameter, 参数Parametric statistics, 参数统计Parametric test, 参数检验Partial correlation, 偏相关Partial regression, 偏回归Partial sorting, 偏排序Partials residuals, 偏残差Pattern, 模式Pearson curves, 皮尔逊曲线Peeling, 退层Percent bar graph, 百分条形图Percentage, 百分比Percentile, 百分位数Percentile curves, 百分位曲线Periodicity, 周期性Permutation, 排列P-estimator, P估计量Pie graph, 饼图Pitman estimator, 皮特曼估计量Pivot, 枢轴量Planar, 平坦Planar assumption, 平面的假设PLANCARDS, 生成试验的计划卡Point estimation, 点估计Poisson distribution, 泊松分布Polishing, 平滑Polled standard deviation, 合并标准差Polled variance, 合并方差Polygon, 多边图Polynomial, 多项式Polynomial curve, 多项式曲线Population, 总体Population attributable risk, 人群归因危险度Positive correlation, 正相关Positively skewed, 正偏Posterior distribution, 后验分布Power of a test, 检验效能Precision, 精密度Predicted value, 预测值Preliminary analysis, 预备性分析Principal component analysis, 主成分分析Prior distribution, 先验分布Prior probability, 先验概率Probabilistic model, 概率模型probability, 概率Probability density, 概率密度Product moment, 乘积矩/协方差Profile trace, 截面迹图Proportion, 比/构成比Proportion allocation in stratified random sampling, 按比例分层随机抽样Proportionate, 成比例Proportionate sub-class numbers, 成比例次级组含量Prospective study, 前瞻性调查Proximities, 亲近性Pseudo F test, 近似F检验Pseudo model, 近似模型Pseudosigma, 伪标准差Purposive sampling, 有目的抽样QR decomposition, QR分解Quadratic approximation, 二次近似Qualitative classification, 属性分类Qualitative method, 定性方法Quantile-quantile plot, 分位数-分位数图/Q-Q图Quantitative analysis, 定量分析Quartile, 四分位数Quick Cluster, 快速聚类Radix sort, 基数排序Random allocation, 随机化分组Random blocks design, 随机区组设计Random event, 随机事件Randomization, 随机化Range, 极差/全距Rank correlation, 等级相关Rank sum test, 秩和检验Rank test, 秩检验Ranked data, 等级资料Rate, 比率Ratio, 比例Raw data, 原始资料Raw residual, 原始残差Rayleigh's test, 雷氏检验Rayleigh's Z, 雷氏Z值Reciprocal, 倒数Reciprocal transformation, 倒数变换Recording, 记录Redescending estimators, 回降估计量Reducing dimensions, 降维Re-expression, 重新表达Reference set, 标准组Region of acceptance, 接受域Regression coefficient, 回归系数Regression sum of square, 回归平方和Rejection point, 拒绝点Relative dispersion, 相对离散度Relative number, 相对数Reliability, 可靠性Reparametrization, 重新设置参数Replication, 重复Report Summaries, 报告摘要Residual sum of square, 剩余平方和Resistance, 耐抗性Resistant line, 耐抗线Resistant technique, 耐抗技术R-estimator of location, 位置R估计量R-estimator of scale, 尺度R估计量Retrospective study, 回顾性调查Ridge trace, 岭迹Ridit analysis, Ridit分析Rotation, 旋转Rounding, 舍入Row, 行Row effects, 行效应Row factor, 行因素RXC table, RXC表Sample, 样本Sample regression coefficient, 样本回归系数Sample size, 样本量Sample standard deviation, 样本标准差Sampling error, 抽样误差SAS(Statistical analysis system ), SAS统计软件包Scale, 尺度/量表Scatter diagram, 散点图Schematic plot, 示意图/简图Score test, 计分检验Screening, 筛检SEASON, 季节分析Second derivative, 二阶导数Second principal component, 第二主成分SEM (Structural equation modeling), 结构化方程模型Semi-logarithmic graph, 半对数图Semi-logarithmic paper, 半对数格纸Sensitivity curve, 敏感度曲线Sequential analysis, 贯序分析Sequential data set, 顺序数据集Sequential design, 贯序设计Sequential method, 贯序法Sequential test, 贯序检验法Serial tests, 系列试验Short-cut method, 简捷法Sigmoid curve, S形曲线Sign function, 正负号函数Sign test, 符号检验Signed rank, 符号秩Significance test, 显著性检验Significant figure, 有效数字Simple cluster sampling, 简单整群抽样Simple correlation, 简单相关Simple random sampling, 简单随机抽样Simple regression, 简单回归simple table, 简单表Sine estimator, 正弦估计量Single-valued estimate, 单值估计Singular matrix, 奇异矩阵Skewed distribution, 偏斜分布Skewness, 偏度Slash distribution, 斜线分布Slope, 斜率Smirnov test, 斯米尔诺夫检验Source of variation, 变异来源Spearman rank correlation, 斯皮尔曼等级相关Specific factor, 特殊因子Specific factor variance, 特殊因子方差Spectra , 频谱Spherical distribution, 球型正态分布Spread, 展布SPSS(Statistical package for the social science), SPSS统计软件包Spurious correlation, 假性相关Square root transformation, 平方根变换Stabilizing variance, 稳定方差Standard deviation, 标准差Standard error, 标准误Standard error of difference, 差别的标准误Standard error of estimate, 标准估计误差Standard error of rate, 率的标准误Standard normal distribution, 标准正态分布Standardization, 标准化Starting value, 起始值Statistic, 统计量Statistical control, 统计控制Statistical graph, 统计图Statistical inference, 统计推断Statistical table, 统计表Steepest descent, 最速下降法Stem and leaf display, 茎叶图Step factor, 步长因子Stepwise regression, 逐步回归Storage, 存Strata, 层（复数）Stratified sampling, 分层抽样Stratified sampling, 分层抽样Strength, 强度Stringency, 严密性Structural relationship, 结构关系Studentized residual, 学生化残差/t化残差Sub-class numbers, 次级组含量Subdividing, 分割Sufficient statistic, 充分统计量Sum of products, 积和Sum of squares, 离差平方和Sum of squares about regression, 回归平方和Sum of squares between groups, 组间平方和Sum of squares of partial regression, 偏回归平方和Sure event, 必然事件Survey, 调查Survival, 生存分析Survival rate, 生存率Suspended root gram, 悬吊根图Symmetry, 对称Systematic error, 系统误差Systematic sampling, 系统抽样Tags, 标签Tail area, 尾部面积Tail length, 尾长Tail weight, 尾重Tangent line, 切线Target distribution, 目标分布Taylor series, 泰勒级数Tendency of dispersion, 离散趋势Testing of hypotheses, 假设检验Theoretical frequency, 理论频数Time series, 时间序列Tolerance interval, 容忍区间Tolerance lower limit, 容忍下限Tolerance upper limit, 容忍上限Torsion, 扰率Total sum of square, 总平方和Total variation, 总变异Transformation, 转换Treatment, 处理Trend, 趋势Trend of percentage, 百分比趋势Trial, 试验Trial and error method, 试错法Tuning constant, 细调常数Two sided test, 双向检验Two-stage least squares, 二阶最小平方Two-stage sampling, 二阶段抽样Two-tailed test, 双侧检验Two-way analysis of variance, 双因素方差分析Two-way table, 双向表Type I error, 一类错误/α错误Type II error, 二类错误/β错误UMVU, 方差一致最小无偏估计简称Unbiased estimate, 无偏估计Unconstrained nonlinear regression , 无约束非线性回归Unequal subclass number, 不等次级组含量Ungrouped data, 不分组资料Uniform coordinate, 均匀坐标Uniform distribution, 均匀分布Uniformly minimum variance unbiased estimate, 方差一致最小无偏估计Unit, 单元Unordered categories, 无序分类Upper limit, 上限Upward rank, 升秩Vague concept, 模糊概念Validity, 有效性VARCOMP (Variance component estimation), 方差元素估计Variability, 变异性Variable, 变量Variance, 方差Variation, 变异Varimax orthogonal rotation, 方差最大正交旋转Volume of distribution, 容积W test, W检验Weibull distribution, 威布尔分布Weight, 权数Weighted Chi-square test, 加权卡方检验/Cochran检验Weighted linear regression method, 加权直线回归Weighted mean, 加权平均数Weighted mean square, 加权平均方差Weighted sum of square, 加权平方和Weighting coefficient, 权重系数Weighting method, 加权法W-estimation, W估计量W-estimation of location, 位置W估计量Width, 宽度Wilcoxon paired test, 威斯康星配对法/配对符号秩和检验Wild point, 野点/狂点Wild value, 野值/狂值Winsorized mean, 缩尾均值Withdraw, 失访Youden's index, 尤登指数Z test, Z检验Zero correlation, 零相关Z-transformation, Z变换。

多因素模型英语The multi-factor model is a widely used tool in finance and investment analysis. It provides a framework for understanding and evaluating the risk and return of a portfolio or investment strategy. In this article, we will explore the key concepts of the multi-factor model and its application in investment management.At its core, the multi-factor model is based on the idea that the returns of an investment can be explained by multiple factors, rather than just the overall market return. These factors can include macroeconomic variables, industry-specific trends, and company-specific characteristics. By identifying and analyzing these factors, investors can gain a deeper understanding of the sources of risk and return in their portfolios.One of the key benefits of the multi-factor model is its ability to capture the diversification benefits of different factors. By considering a range of factors, investors can build more resilient portfolios that are less sensitive to any single source of risk. This can help to reduce the overall volatility of the portfolio and improve its risk-adjusted return.In practice, the multi-factor model is often used to construct and analyze factor-based investment strategies. These strategies seek to capture the returns associated with specific factors, such as value, momentum, or quality. By tilting a portfolio towards these factors, investors can potentially enhance their returns and manage their risk more effectively.Another important application of the multi-factor model is in the evaluation of investment managers. By decomposing the returns of a portfolio into its underlying factors, investors can better understand the skill and performance of a manager. This can help to identify whether a manager's returns are driven by skill or simply by exposure to certain factors.In recent years, there has been a growing interest in the use of the multi-factor model in the context of environmental, social, and governance (ESG) investing. By incorporating ESG factors into the multi-factor framework, investors can betterunderstand the impact of sustainability considerations on the risk and return of their portfolios. This can help to align investment decisions with broader social and environmental goals.It is important to note that the multi-factor model is not without its limitations. One challenge is the identification and selection of relevant factors, as well as the estimation of their risk and return premia. Additionally, the model assumes that the relationship between factors and returns is stable over time, which may not always be the case in practice.In conclusion, the multi-factor model is a powerful tool for understanding and managing the risk and return of investment portfolios. By considering a range of factors, investors can build more resilient portfolios, enhance their returns, and better evaluate the performance of investment managers. As the use of the multi-factor model continues to evolve, it is likely to play an increasingly important role in the field of investment management.。

离子液体电解液英文Ionic liquid electrolyte is a type of electrolyte composed of ionic liquids, which are salts that exist as liquids at room temperature. Unlike traditional electrolytes, such as aqueous solutions or organic solvents, ionic liquid electrolytes have unique properties and offer several advantages in various applications.One prominent feature of ionic liquid electrolytes is their wide electrochemical window. Traditional electrolytes have limited stability at high voltages, limiting their use in certain electrochemical processes. In contrast, ionic liquid electrolytes can withstand much higher voltages without decomposing, allowing for a broader range of applications. This property makes them particularly attractive for energy storage systems, such as lithium-ion batteries and supercapacitors, where high voltage operation is required.Another advantage of ionic liquid electrolytes is their low volatility. Traditional organic solvents used in electrolytes tend to evaporate, leading to safety concerns and reduced stability. Ionic liquids, on the other hand, have significantly lower vapor pressures, making them more stable and safer to use. This property is of great importance in applications that involve high temperatures or require long-term operation.The high conductivity of ionic liquids is another benefit in terms of electrolyte performance. Traditional electrolytes often require the addition of salts to enhance their conductivity, but ionic liquids possess inherently high conductivity due to their ionic nature. This makes them more efficient in facilitating ion transport and can lead to improved overall performance in electrochemical devices.Furthermore, ionic liquid electrolytes are known for their good thermal stability. They have high boiling points and are resistant to thermal decomposition, making them suitable for applications that involve high temperatures or thermal cycling. This property is especially advantageous in devices that generate significant heat, such as fuel cells or high-power batteries.Despite all these advantages, there are still challenges and limitations associated with ionic liquid electrolytes. They tend to have higher viscosities compared to traditional electrolytes, which can affect their transport properties and hinder mass transfer. Additionally, the synthesis and purification of ionic liquids can be complex and costly.In conclusion, ionic liquid electrolytes offer unique advantages that make them appealing for various applications involving energy storage, electrochemical processes, and high-temperature environments. Their wide electrochemical window, low volatility, high conductivity, and good thermal stability make them valuable alternatives to traditional electrolytes. However, further research and development are needed to address challenges associated with their viscosity and synthesis, thereby unlocking their full potential in these applications.。

差分进化算法（Differential Evolution, DE）与紧凑差分法（Compact Differential Evolution, CDE）是两种优化算法，它们在解决复杂问题时都表现出了优秀的性能。

在本文中，我们将探讨这两种算法的联系，并分析它们在实际应用中的优势和局限性。

1. 差分进化算法的基本原理差分进化算法是一种基于种群的优化算法，最早由Storn和Price在1997年提出。

它模拟了一种群体内个体间的实数向量交叉和变异操作，通过不断地迭代和更新个体向量，寻找最优解。

DE算法的基本原理是通过变异、交叉和选择操作，在种群中不断生成新的个体，并选出适应度最高的个体作为下一代种群的父代。

这样不断迭代后，求得最优解或接近最优解。

2. 紧凑差分法的基本原理紧凑差分法是对传统差分进化算法的一种改进，它通过减少内存占用和简化算法结构，提高了算法的运行效率和收敛速度。

CDE算法主要思想是利用种群内的信息交换和共享，减少不必要的重复计算，实现更加紧凑的优化过程。

与DE算法相比，CDE算法更加注重局部搜索和收敛速度，在一些复杂问题上表现出更好的性能。

3. 差分进化算法与紧凑差分法的联系DE算法和CDE算法在基本原理上有一定的联系，它们都是基于种群的优化算法，通过变异、交叉和选择等操作来不断搜索最优解。

然而，CDE算法在优化过程中更多地考虑了信息共享和局部搜索，相对于DE 算法更加注重算法的紧凑性和高效性。

可以说，CDE算法是DE算法的一种改进和延伸，是在DE算法基础上的一次创新。

4. 个人观点和理解从个人观点来看，DE算法和CDE算法都是非常有效的优化算法，它们在解决实际问题时都表现出了很好的性能。

DE算法通过全局搜索和迭代更新，对于一些复杂的、高维度的优化问题有着较好的适应性。

而CDE算法则更加注重信息共享和局部搜索，可以更快地收敛到最优解附近。

在实际应用中，我认为可以根据具体问题的特点和要求来选择合适的算法，或者结合两种算法进行优化求解。

第 51 卷 第 3 期石 油 钻 探 技 术Vol. 51 No.3 2023 年 5 月PETROLEUM　DRILLING　TECHNIQUES May, 2023◄测井录井►doi:10.11911/syztjs.2023068引用格式：亢武臣，杨书博，赵琪琪，等. 基于优化变分模态分解和互相关的钻井液脉冲信号处理方法[J]. 石油钻探技术，2023, 51（3）：144-151.KANG Wuchen, YANG Shubo, ZHAO Qiqi, et al. A pulse signal processing method for drilling fluid based on optimal variational mode decomposition and cross-correlation [J]. Petroleum Drilling Techniques，2023, 51(3)：144-151.基于优化变分模态分解和互相关的钻井液脉冲信号处理方法亢武臣1,2， 杨书博2， 赵琪琪3， 黄豪彩1， 丁士东2（1. 浙江大学海洋学院, 浙江舟山 316021；2. 中石化石油工程技术研究院有限公司, 北京 102206；3. 中国石油大学（北京）地球物理学院, 北京102249）摘　要: 随着油气勘探开发不断深入，钻井技术逐渐向深井、超深井和小井眼方向发展，对钻井液脉冲信号处理提出了更高的要求。

通过分析脉冲位置调制编码的基本原理，提出了一种基于优化变分模态分解和互相关的钻井液脉冲信号处理方法，并利用在苏北地区某页岩油井采集的钻井液脉冲信号验证了该方法的可行性。

基于优化变分模态分解算法，实现了在低信噪比条件下有用信号的有效提取；基于同步头相关器对去噪后的信号进行互相关处理，实现了数据帧起始位置的可靠计算；基于数据块相关器对数据块内波形进行互相关处理，实现了码值的准确获取。

与传统的钻井液脉冲信号处理方法相比，上述方法具有可靠性高和误码率低的特点，能够很好地满足复杂井眼环境下钻井液脉冲信号处理的需求。

心理动力------psycho-dynamics心理分析------psychoanalysis行为论-------behaviorism心理生物观---psycho-biological perspective 认知---------cognition临床心理学家-clinical psychologist谘商--------counseling人因工程-------human factor engineering组织--------organization潜意识---------unconsciousness完形心理学---Gestalt psychology感觉------------sensation知觉--------perception实验法--------experimental method独变项-------independent variable依变项--------dependent V.控制变项------control V.生理------------physiology条件化---------conditioning学习------------learning比较心理学---comparative psy.发展-------------development社会心理学---social psy.人格--------------personality心理计量学—psychometrics受试(者)---------subject实验者预期效应—experimenter expectancy effect 双盲法-----double—blind实地实验--------field experiment相关-----------correlation调查-------------survey访谈-----------interview个案研究-------case study观察-----------observation心理测验-------psychological test纹理递变度-----texture gradient注意------------attention物体的组群---grouping of object型态辨识—pattern recognition形象-背景----figure-ground接近律--------proximity相似律--------similarity闭合律-------closure连续律--------continuity对称律-------symmetry错觉-----------illusion幻觉----------delusion恒常性--------constancy大小----------size形状-----------shape位置---------- location单眼线索-----monocular cue线性透视----linear- perspective双眼线索-----binocular cue深度---------depth调节作用-----accommodation重迭----superposition双眼融合-----binocular fusion辐辏作用-----convergence双眼像差-----binocular disparity向度--------- dimension自动效应-----autokinetic effect运动视差----- motion parallax诱发运动---- induced motion闪光运动----- stroboscopic motion上下文、脉络-context人工智能------artificial intelligence A.I. 脉络关系作用-context effect模板匹配------template matching整合分析法---analysis-by-synthesis丰富性---------redundancy选择性---------selective无意识的推论-unconscious inferences 运动后效---motion aftereffect特征侦测器—feature detector激发性---excitatory抑制性----inhibitory几何子---geons由上而下处理—up-down process由下而上处理---bottom-up process连结者模式---connectionist model联结失识症---associative agnosia脸孔辨识困难症---prosopagnosia意识--conscious（ness）意识改变状态---altered states of consciousness无意识----unconsciousness前意识---------preconsciousness内省法---introspection边缘注意---peripheral attention多重人格-----multiple personality2 心理学专有名词中英对照表午餐排队(鸡尾酒会)效应—lunch line(cocktail party) effect 自动化历程----automatic process解离----dissociate解离认同失常----dissociative identity disorder快速眼动睡眠----REM dream非快速眼动睡眠—NREM dream神志清醒的梦----lucid dreaming失眠---insomnia显性与隐性梦---manifest & latern content心理活动性psychoactive冥想------meditation抗药性---- tolerance戒断----withdrawal感觉剥夺---sensory deprivation物质滥用----substance abuse成瘾--------physical addiction物质依赖----sub. dependence戒断症状----withdrawal symptom兴奋剂--stimulant幻觉（迷幻）剂----hallucinogen镇定剂---sedative，抑制剂--depressant酒精中毒引起谵妄—delirium tremens 麻醉剂---narcotic催眠-------hypnosis催眠后暗示----posthypnotic suggestion 催眠后失忆posthypnotic amnesia超心理学---parapsychology超感知觉extrasensory perception ESP 心电感应---telepathy超感视---clairvoyance预知---precognition心理动力—psycokinesis PK受纳器-----receptor绝对阈----absolute threshold差异阈----------difference threshold恰辨差------- -JND韦伯律---------Weber’s law心理物理-----psychophysical费雪纳定律---Fechner’s law频率-----frequency振幅----------amplitude音频-------pitch基音----------fundamental tone 倍音-----overtone和谐音-------harmonic音色------timbre白色噪音----white noise鼓膜-----eardrum耳蜗----------cochlea卵形窗—oval window圆形窗-------round window前庭-----vestibular sacs半规管-------semicircular canals 角膜-------cornea水晶体-------lens虹膜------------iris瞳孔----------pupil网膜---------retina睫状肌-------ciliary muscle调节作用---accommodation脊髓---------spinal cord反射弧--------reflex arc脑干---------brain stem计算机轴性线断层扫描-- CAT 或CTPET---正子放射断层摄影MRI-----磁共振显影延脑----medulla桥脑-----pons小脑----cerebellum网状结构---reticular formation RAS----网状活化系统视丘----thalamus下视丘----hypothalamus大脑----cerebrum脑（下）垂体（腺）—pituitary gland脑半球---cerebral hemisphere皮质---cortex胼胝体----corpus callosum边缘系统------limbic system海马体----hippocampus杏仁核--------amygdala中央沟---central fissure侧沟-----------lateral fissure脑叶------lobe同卵双生子----identical twins异卵双生子—fraternal twins古典制约--classical conditioning操作制约---operant conditioning非制约刺激—(US unconditioned stimulus非制约反应—(UR)unconditioned R.制约刺激---(CS) conditioned S.制约反应----(CR)conditioned R.习(获)得-----acquisition增强作用------reinforcement消除(弱)------extinction自(发性)然恢复----spontaneous recovery前行制约—forward conditioning同时制约--simultaneous conditioning回溯制约---backward cond.痕迹制约——trace conditioning延宕制约—delay conditioning类化(梯度)---generalization (gradient)区辨------discrimination次级)增强物-------(secondary) reinforcer嫌恶刺激---aversive stimulus试误学习---trial and error learning效果率-----law of effect正(负)性增强物—positive (negative) rei.行为塑造—behavior shaping循序渐进-----successive approximation自行塑造—autoshaping部分(连续)增强—partial (continuous)R定比(时)时制—fixed ratio (interval) schedule FR或FI变化比率(时距)时制—variable ratio (interval) schedule VR或VI 逃离反应---escape R.回避反应—avoidance response习得无助----learned helplessness顿悟--------insight学习心向—learning set隐内(潜在)学习---latent learning认知地图---cognitive map生理回馈------biofeedback敏感递减法-systematic desensitization普里迈克原则—Premack’s principle洪水法----flooding观察学习----observational learning动物行为学----ethology敏感化—sensitization习惯化---habituation联结---association认知学习----cognitional L.观察学习---observational L.登录、编码----encoding保留、储存-----retention提取------retrieval回忆----free recall全现心像、照相式记忆---eidetic imagery、photographic memory . 舌尖现象（TOT）—tip of tongue再认---------recognition再学习--------relearning节省分数----savings外显与内隐记忆--explicit & implicit memory记忆广度---memory span组集--chunk序列位置效应---serial position effect起始效应---primacy effect新近效应-----recency effect心(情)境依赖学习---state-dependent L.无意义音节—nonsense syllable顺向干扰---proactive interference逆向干扰---retroactive interference闪光灯记忆---flashbulb memory动机性遗忘----motivated forgetting器质性失忆症—organic amnesia阿兹海默症---Alzheimer”s disease近事（顺向）失忆症—anterograde amnesia旧事（逆向）失忆—retrograde A.高沙可夫症候群—korsakoff”s syndrome凝固理论—consolidation th.感觉记忆（SM）—sensory memory短期记忆（STM）—short-term M.长期记忆（LTM）—long-term memory复诵---rehearsal预示(激发)----priming童年失忆症---childhood amnesia视觉编码（表征）---visual code (representation) 听觉编码—acoustic code运作记忆---working memory语意性知识—semantic knowledge记忆扫瞄程序—memory scanning procedure竭尽式扫瞄程序-exhaustive S.P.自我终止式扫瞄—self-terminated S. .程序性知识—procedural knowledge命题（陈述）性知识--propositional（declarative）knowledge 情节（轶事）性知识—episodic K.讯息处理深度—depth of processing精致化处理—elaboration登录特殊性—coding specificity记忆术—mnemonic位置记忆法—method of loci字钩法—peg word（线）探索（测）(激发)字—prime关键词---key word命题思考----propositional thought心像思考---imaginal thought行动思考---motoric thought概念---concept原型----prototype属性----property特征---feature范例策略--exemplar strategy语言相对性（假说）—linguistic relativity th.音素---phoneme词素---morpheme(字词的)外延与内涵意义—denotative & connotative meaning（句子的）表层与深层结构—surface & deep structure 语意分析法---semantic differential全句语言—holophrastic speech过度延伸---over-extension电报式语言—telegraphic speech关键期----critical period差异减缩法---difference reduction方法目的分析---means-ends analysis倒推---working backward动机---------motive自由意志------free will决定论------determinism本能-----------instinct种属特有行为-----species specific驱力----drive诱因------incentive驱力减低说---drive reduction th.恒定状态（作用）—homeostasis原级与次级动机—primary & secondary M.功能独立—functional autonomy下视丘侧部（LH）—lateral hypothalamus脂肪细胞说----fat-cell theory.下视丘腹中部（VMH）—ventromedial H定点论---set point th. CCK———第一性征---primary sex characteristic第二性征---secondary sex characteristic自我效能期望—self-efficiency expectancy内在（发）动机—intrinsic motive外在（衍）动机—extrinsic motive成就需求---N. achievement需求层级—hierarchy of needs自我实现---self actualization冲突----conflict 多项仪---polygraph肤电反应----------GSR（认知)评估---cognitive appraisal脸部回馈假说---facial feedback hypothesis(生理)激发----arousal挫折-攻击假说---frustration-aggression hy.替代学习----vicarious learning发展------development先天-----nature后天-----nurture成熟-------maturation(视觉)偏好法-----preferential method习惯法-----habituation视觉悬崖-----visual cliff剥夺或丰富(环境)---deprivation or enrichment of env. 基模----schema同化----assimilation调适-----accommodation平衡----equilibrium感觉动作期----sensorimotor stage物体永久性----objective permanence运思前期----preoperational st.保留概念----conservation道德现实主义---moral realism具体运思期-----concrete operational形式运思期----formal operational st.前俗例道德---pre-conventional moral俗例道德----conventional moral超俗例道德----post-conventional moral气质----temperament 依附---attachment 性别认定---gender identity性别配合----sex typing性蕾期---phallic stage恋亲冲突—Oedipal conflict认同-----identification社会学习----social learning情结---complex性别恒定----gender constancy青年期----adolescence青春期-- -puberty第二性征---secondary sex characteristics 认同危机---identity crisis定向统合---identity achievement早闭型统合---foreclosure未定型统合---moratorium迷失型统合---identity diffusion 传承---generativity。

小世界网络( small-world network)无标度网络( scale-free network)随机网络( random network)规则网络( regular network)无向网络( undirected network)加权网络( weighted network)图论( Graph theory)邻接矩阵( adjacency matrix)结构性脑网络( structural brain networks 或anatomical brain networks)功能性脑网络( functional brain networks)因效性脑网络( effective brain networks)感兴趣脑区( region of interest，ROI)血氧水平依赖( BOLD，blood oxygenation level depended)体素( voxel)自发低频震荡( spontaneous low-frequency fluctuations，LFF) 默认功能网络( default mode network，DMN)大范围皮层网络( Large-scale cortical network)效应连接(effective connectivity)网络分析工具箱（Graph Analysis Toolbox，GAT）自动解剖模板（automatic anatomical template，AAL）脑电图(electroencephalogram, EEG)脑磁图(magnetoencephalogram, MEG)功能磁共振成像(Functional magnetic resonance imaging, fMRI)弥散张量成像(Diffusion Tensor Imaging, DTI)弥散谱成像( diffusion spectrum imaging ，DSI)细胞结构量化映射( quantitative cytoarchitecture mapping)正电子发射断层扫描(PET, positron emisson tomography)精神疾病：阿尔茨海默症( Alzheimer’ s disease，AD)癫痫( epilepsy)精神分裂症( Schizophrenia)抑郁症( major depression)单侧注意缺失( Unilateral Neglect)轻度认知障碍（mild cognitive impairment, MCI）正常对照组（normal control, NC）MMSE (Mini-mental state examination) 简易精神状态检查量表CDR (Clinic dementia rating) 临床痴呆量表边( link，edge)节点(vertex 或node)节点度(degree)区域核心节点(provincial hub)度分布(degree distribution)节点强度( node strength)最短路径长度(shortest path length)特征路径长度( characteristic path length) 聚类系数( clustering coefficient)中心度(centrality)度中心度(degree centrality)介数中心度( betweenness centrality)连接中枢点( connector hub)局部效率(local efficiency)全局效率( global efficiency)相位同步( phase synchronization)连接密度(connection density/cost)方法：互相关分析( cross-correlation analysis) 因果关系分析( Causality analysis)直接传递函数分析( Directed Transfer Function，DTF)部分定向相干分析( Partial Directed Coherence，PDC)多变量自回归建模( multivariate autoregressive model，MV AR) 独立成分分析( independent component analysis，ICA)同步似然性(synchronization likelihood, SL)结构方程建模(structural equation modeling, SEM)动态因果建模(dynamic causal modeling, DCM)心理生理交互作用模型(Psychophysiological interaction model)非度量多维定标(non-metric multidimensional scaling)体素形态学(voxel-based morphometry, VBM)统计参数映射（statistical parametric mapping，SPM）皮尔逊相关系数（Pearson correlation）偏相关系数（Partial correlation）DTI指标：MD (Mean diffusivity) 平均扩散率ADC (Apparent diffusion coefficient) 表观弥散系数FA (Fractional anisotropy) 部分各向异性DCavg (Average diffusion coefficient) 平均弥散系数RA (Relative anisotropy) 相对各项异性VR (V olume ratio) 体积比AI (Anisotrop index) 各项异性指数TBSS (Tract-based Spatial Statistics) 基于纤维追踪束体素的空间统计DWI (Diffusion Weight Imaging) 弥散加权成像。

非等位基因概述非等位基因是指同一基因座上的不同等位基因。

等位基因是指在某个给定的基因座上，可以存在多种不同的变体。

每个个体继承了一对等位基因，一对等位基因可能会导致不同的表型表达。

非等位基因的存在使得遗传学研究更加复杂，因为不同的等位基因会对个体的表型产生不同的影响。

背景在生物学中，基因座是指染色体上一个特定的位置，该位置上的基因决定了某个特征的表达方式。

每个基因座上可以有多种不同的等位基因。

等位基因是指在某个特定基因座上的不同基因变体。

每个个体都会继承一对等位基因，通过这对等位基因的不同组合，决定了个体的表型。

然而，并非所有基因座上的等位基因都具有相同的表现型。

非等位基因的影响非等位基因的存在导致不同等位基因会对个体表型产生不同的影响。

有些非等位基因会表现出显性效应，也就是说，当个体继承了一个突变的等位基因时，即使同时继承了一个正常的等位基因，但显性效应会使得突变的等位基因的表型表达得到体现。

相反，有些非等位基因会表现出隐性效应，当个体继承了两个突变的等位基因时，才会表现出突变的表型。

除了显性和隐性效应之外，非等位基因还可能发生两种其他类型的表型效应。

一种是共显效应，当个体继承了两个不同的突变等位基因时，在表型表达上会表现出一种新的特征，这个特征并不是单个突变等位基因所能导致的。

另一种是部分显性效应，当个体继承了两个不同的突变等位基因时，表型表达将介于两个单独突变等位基因的表型之间。

重组和非等位基因重组是指两个不同的染色体交换部分基因序列的过程。

在重组的过程中，非等位基因可能会发生改变，导致新的等位基因组合形成。

这一过程使得非等位基因的表型效应更加复杂，因为新的等位基因可能将不同基因座的效应组合起来。

非等位基因的重要性非等位基因对生物的适应性和多样性起着重要作用。

通过对等位基因的各种组合的研究，人们可以更好地理解基因与表型之间的关系，并揭示遗传变异对物种适应环境的重要性。

总结非等位基因是指同一基因座上的不同等位基因。

化学词汇电容器油Capacitor oil 抗氧化剂antioxidant 防锈剂antirust黏度viscosity 透明度transparency 抗氧化安定性antioxidation stability运动黏度中心值Kinematic viscosity center value 凝点solidifying point 防锈性rust preventing characteristic 伺服阀servo valve 闪点flash point 抗乳化性能anti-emulsification performance 减压阀depressurized valve抗泡沫性能foam preventing performance 外观颜色appearance color绝缘液体介质insulating liquid media 界面张力interfacial tension水溶性酸碱water-soluble acid or alkali 活性硫activated sulfur苛性钠caustic soda(NaOH) 击穿电压breakdown(striking) voltage 体积电阻率volume resistivity 芳香烃aromatic hydrocarbon抗燃油fire resistant oil 馏分fraction 液压系统hydraulic system 自燃点auto-ignition temperature 抗燃液压油fire resistant hydraulic oil合成型(磷酸脂,芳香脂,卤化物) Synthetic (Phosphate esters, aromatic esters, halides)水垫层water stratum 水合型(水-乙二醇)Hydrate (water-glycol)酯化产物esterifying production 腐蚀corrosion(corrosive) 挥发性V olatility 芳基aryl 烷基alkyl 介电性能Dielectrical performance 润滑剂lubricant电阻率resistivity 抗磨性anti-wear characteristic 热氧化安定性thermal oxidation stability 热安定性Thermal stability 热分解Thermolysis 脱气性Degasification 中性酯Neutral ester 热氧化分解thermal oxidation decomposing铜合金copper alloy 起泡沫性foaming characteristic 相容性Compatibility体积弹性模数V olume elastic modulus 可压缩体积compressible volume 溶剂效应solvent effect 有机化合物organic compound聚合材料polymeric material 橡胶、涂料Rubber and paint 精滤装置meticulous filtering device水解安定性Hydrolyzing stability 介质medium 合成液synthetic liquid 分子量molecular weight 分子结构molecular structure 石油基petroleum base 辐射安定性Radiation stability 比热specific heat 受辐射的设备irradiated equipment 对流特性convection characteristic 可燃性combustibility 溶解度solubility闭式齿轮油closed gear oil 压缩机油compressor oil 叶片泵vane pump润滑脂grease 中负荷开式齿轮油medium load open gear oil柱塞泵piston pump 蜗轮蜗杆油worm wheel and worm shaft oil过滤性filterability 滴油回转式oil drop rotary 倾向性orientation增压油泵boost oil pump 喷射泵jet pump 杂质impurity排烟风机Smoke exhaust fan 雾化atomization 劣化deterioration悬浮状suspension 悬浮物suspended matters 乳化状emulsion溶解状Dissolution 有机酸organic acid 皂化物sponified substances催化作用catalysis 挥发性可燃物volatile combustible substances不可缺少indispensable 低分子碳氢化合物low molecular hydrocarbons倾点pour point 水溶性酸water-soluble acid 相对密度relative density水的存量water stock 结晶crystallize 导电性electric conductivity无机酸inorganic acid 固体纤维纸solid fiber paper 流动性fluidity模拟试验simulation test 实验室laboratory 油泥oil sludge试油testing oil 介质损耗角正切值tangent value of media loss angle余切cotangent 吸附剂sorbent 余角complement angle热虹吸过滤器thermosiphon filter 隔膜密封装置diaphragm seal device电容率capacitivity 炭黑carbon black 胶体colloid 乙炔C2H2虫胶shell-lac 鱼腥味Fishy smell 霉气味Moldy smell散热heat radiation 相角phase angle 四球试验机Four-ball tester磨痕直径grinding crack diameter 无卡咬负荷nonseizure load烧结负荷sintering load 曲轴箱crankcase 剪切速率shear rate边界泵送温度borderline pumping temperature 下降率dropping rate聚合物polymer 轮轴承wheel and axle 高扭矩后桥high torque back axle线性linearity 原水Raw water 除盐demineralization, desalting预处理pretreatment 后置处理post-treatment 再生regeneration澄清池clarification basin 环形annular 絮凝（剂）flocculant重力无阀过滤器Gravity valveless filter 虹吸siphon 滤层filter layer联通管connecting pipe 冲洗水箱flushing tank 无烟煤anthracite石英砂quartz sand 浊度Turbidity 反洗强度Backwash intensity水头损失head loss 排水阀drainage valve 排气阀exhaust valve 正洗Washing 透明transparency 活性碳Activated carbon 吸附过滤器absorbent filter游离free 氯chlorine 铯cesium(Cs) 溴bromine 有机物organic matter玻璃钢fiber glass reinforced plastic (FRP) 筒体Cylinder 碳钢carbon steel斗形bucket shape 支管branch pipe 电渗析Electrodialysis (ED)半透膜semipermeable membrane 选择透过性selective permeability溶质粒子solute granule 冶金metallurgy 电解质electrolyte 凝聚剂flocculant自来水tap water 电渗析器electrodialyzer 交换膜exchange membrane电镀plating 隔室compartment 提纯purification 黄泥yellow mud 泡花碱(硅酸钠)sodium silicate聚丙烯酰胺polyacrylamide 助凝剂coagulant aid 渗透Osmosis矾花（铝钒絮凝剂）alum floc 细菌bacteria 孔径aperture反渗透Reverse osmosis 苦咸水brackish water 补给水make-up water 淡水fresh water 晶体crystal 柠檬酸C6H8O7 (citric acid)清洗混合液cleaning mixture阻垢剂scale inhibitor硅垢Silicate scale消毒（杀菌）液disinfectant按比例pro rata压力容器pressure vessel清洗剂cleaning agent 四钠盐(乙二胺四乙酸)EDTA 黏泥slime氢氧化铁Fe(OH)3 压脂层compact layer 放空管vent pipes三聚磷酸钠Na5P3O10(STP) 窥视孔inspection hole 污垢fouling结晶crystallization 平衡equilibrium 重碳酸盐bicarbonate 石灰lime(Ca(OH)2) 石灰石limestone(CaCO3) 石膏gypsum(CaSO4) 软化softening 藻类algae黏液Viscous liquid 分泌excrete 养分nutrient电化学腐蚀electro-chemical corrosion 软垢mild scale 铁细菌iron bacteria 碳酸氢盐hydrocarbonate厌氧菌anaerobe 水垢附着Scale adhesion冷却水池cooling basin碳酸盐carbonate 浓缩水concentrated water浓缩倍数concentrated cycles 溶解固体dissolved solid 超滤ultrafiltration直流式循环水冷却系统once-through circulating water cooling system可溶性气体soluble gas 截留retention, retain 微滤microfiltration纳滤Nano filtration 透过permeate 碳酸氢盐hydrocarbonate 分散剂dispersant 金属腐蚀metallic corrosion 添加剂additive 腐蚀介质corrosive medium缓蚀剂corrosion inhibitor 富营养化eutrophication 磷phosphor氧化性保护膜protective oxide film 钝化passivation 处理剂treating agent配方formula 除垢descaling 耐蚀材料corrosion resistant material聚丙烯polypropylene (PP) 防腐anticorrosion 石墨graphite阴极保护cathodic protection 屏蔽shield 缓冲作用buffer action 锡tin噬菌体bacteriophage 零排污(放)zero blowdown(discharge)非氧化性杀生剂Non-oxidizing biocide 硫化物sulfide 絮凝flocculation 静压差static pressure difference 铜盐copper salt 结合力binding force回收率recovery rate 含盐的saline 蛋白质protein 挂片coupon离子化ionization 液化liquefaction 型煤briquette洗涤液scrubber liquor 洗涤塔scrubbing tower硫磺sulfur除尘器dust collector 装机容量installed capacity电子束electron beam 未反应的unreacted 吸收塔absorption tower吸收剂absorbent 消石灰lime hydrate 溶解度solubility 碱度alkalinity露点dew point 自由基free radical 中间产物intermediate product中和反应neutralizing reaction 次氯酸盐hypochlorite 硫化物sulfide溴化物bromide 裂解cracking 氢氧化物hydroxide 盐酸hydrochloric acid硫酸sulfuric acid 碳酸carbonic acid 磷酸phosphoric acid 絮凝物floc扬程delivery head 曲轴箱crankcase 剪切速率shear rate 滚筒roller高温分解pyrolysis 线端line terminal 负载分接点load tap电容率,介电常数permittivity (capacitivity) 析气性gassing properties放电electric discharge 白土activated clay 导流室diversion chamber活性(度) activity 脱水dewater 水白色water-white 视镜sight glass最大反洗膨胀高度处maximum height of bed expansion during backwash穹弧形arch 钢衬胶steel with rubber lining 多孔板perforated plate压差differential pressure 药剂drug 加药量，剂量dosage, dose降解degradation 分解decompose 溶解dissolve 酸根acidic radical铝aluminum(Al) 钙calcium(Ca) 铜copper(Cu) 铁iron(Fe)铅lead(Pb) 钠sodium(Na) 锡tin(Sn) 锌zinc(Zn)银silver(Ag) 镁magnesium(Mg) 铯cesium(Cs) 锰manganese(Mn)镍nickel(Ni) 碘iodine(I)磷phosphor(P) 硅silicon(Si) 硫sulfur(S) 溴bromine(Br)碳carbon(C) 汞mercury(Hg) 金gold(Au) 氧oxygen(O)氯chlorine(Cl) 氢hydrogen(H) 氮nitrogen(N) 帕斯卡Pascal（Pa）氟fluorine(F) 试纸test paper 全固形物total solid 化合价valence matter可再生的regenerable 酸洗acid cleaning 过饱和supersaturation, oversaturation）微生物microorganism & microbiology 一氧化物oxide 二氧化物dioxide 三氧化物trioxide 四氧化物quadroxide, tetroxide 五氧化物pentoxide六氧化物hexoxide 七氧化物heptoxide 二价的divalent三价的trivalent 四价的quadrivalent 五价的pentavalent六价的hexavalent 七价的heptavalent化学制水Chemical water making清水池Clear water pond加絮凝剂Add flocculation加还原剂Add reducing agent阻垢剂antisludging agent高效纤维球过滤器High-efficiency fiber ball filter活性炭过滤器Active carbon filter反渗透（RO）装置Reverse osmosis (RO) device 除碳器Decarbonizator 混合离子交换器Mixed ion exchanger除盐水箱Demineralization water tank 工业水泵Industrial pump菌类funguses氯的化合物hydride氧的化合物oxide 渗透膜osmotic membrane反渗透装置reverse osmosis device脱盐设备demineralization equipment.除碳口decarburization opening混床mixing bed阴阳离子anions and cations电子率electron rate浊度Turbidity回收率Recovery rate电导率Conductivity集中式水汽取样装置Centralized steam sampling device加氯处理装置Chlorination Plants水除盐装置Water Demineralisation Plant除油系统Oil removal system蓄水池/集水池Retention / Collecting basin蓄水罐Retention tank反应堆净化器Reactor clarifier污泥稠化器Sludge thickener污泥脱水滤池Sludge dewatering filter重力砂滤池Gravity Sand FiltersPH值调解池PH adjustment tanks腐蚀剂供给系统Caustic feed system酸供给系统Acid feed system 凝结剂供给系统Coagulant feed system生活废水处理装置Sanitary Waste Water Treatment Plant厂饮用水系统Potable Service Water System化学加药系统Chemical Dosing System 热回收蒸汽发生器HRSG磷酸盐Phosphonate消防系统Fire Protection System消防栓Hydrants消火栓箱hose cabinets消防立管Standpipes软管系统hose systems喷水灭火系统Sprinkler systems固定水喷雾系统Water spray fixed systems气体灭火系统Gaseous extinguishing systems推车式灭火器Wheeled 手提式灭火器portable fire extinguishers防火涂料Fire retardant coatings密封垫gaskets排放监测系统Emission Monitoring System水预处理装置The water pre-treatment plant排水Aqueous Discharges疏水、放气和疏水器Drains, Vents and Traps。

酶(Enzyme)专一性(substrate specificity)底物(substrate)结构专一性( structure specificity)绝对专一性( absolute specificity)相对专一性(relative specificity)键专一性(bond specificity)基团专一性(group specificity)立体化学专一性(stereochemical specificity)旋光异构专一性(optical specificity)几何异构专一性(geometrical specificity)锁钥学说( lock and key theory）诱导契合学说(Induced-fit theory)酶蛋白（apoenzyme）辅助因子（cofactor)辅酶（coenzyme）辅基（prosthetic group）全酶(holoenzyme)活性中心（active center ）活性部位(active site) 必需基团(essential group)酶原(zymogen)酶原的激活(zymogen activation)激活剂（activator）抑制剂(inhibitor)不可逆抑制作用（irreversible inhibition）竞争抑制剂（competitive inhibition）非竞争抑制剂（noncompetitive inhibition）反竞争抑制剂（uncompetitive inhibition）假底物（pseudosubstrate）自杀底物（suicide substrate）活化能（activation energy）过渡态（transition）趋近效应（approximation）定向效应（orientation）张力作用（strain）变形（distortion）共价催化(covalent catalysis)亲核催化（nucleophilic catalysis）亲电催化（electrophilic catalysis）酸碱催化作用（acid-base catalysis）酶活力（enzyme activity)酶活力单位(active unit,U)国际单位（international unit,IU）寡聚酶(oligomeric enzyme)同工酶（isoenzyme）诱导酶(induced enzyme)共价调节酶(covalent regulatory enzyme)变构酶（allosteric enzyme)变构效应（allosteric effect )协同指数（cooperative index, CI)核酶（ribozyme）抗体酶（abzyme)（catalytic antibody）固定化酶（immobilized enzyme)新陈代谢（metabolism）同化作用（assimilation）异化作用（dissimilation ）氮平衡(nitrogen balance)必需氨基酸(essential amino acid)非必需氨基酸(non-essential amino acid)蛋白质的互补作用complementary action）胃蛋白酶(pepsin)内肽酶(endopeptidase)外肽酶(exopeptidase)蛋白质的腐败作用(putrefaction)主动转运（active transport）γ-谷氨酰基循环（γ-glutamyl cycle）假神经递质(false neurotransmitter)泛素（ubiquitin Ub）蛋白质的泛素化(ubiquitination)蛋白酶体(proteasome)氨基酸代谢库(metabolic pool)转氨基作用（transamination）高氨血症( hyperammonemia)一碳单位（one carbon unit）从头合成途径(de novo synthesis pathway)补救合成途径(salvage synthesis pathway)生物氧化(biological oxidation电子传递链（eclctron transfer chain)呼吸链(respiratory chain)黄素蛋白（flavoproteins,FP)铁硫蛋白（iron sulfur proteins,Fe-S)泛醌（Ubiquinone,Q或UQ）细胞色素体系(Cytochromes,Cyt)底物水平磷酸化(substrate level phosphorylation) 氧化磷酸化(oxidative phosphorylation)偶联部位(coupling site)化学渗透假说(chemiosmotic theory)糖(carbohydrates)单糖(monosacchride)寡糖(oligosacchride)多糖(polysacchride)结合糖(glycoconjugate)葡萄糖（glucose）果糖（frutcose）淀粉(starch)糖原(glycogen)纤维素(cellulose)糖脂(glycolipid)糖蛋白(glycoprotein)糖的无氧分解（Glycolysis）糖酵解(glycolysis)糖酵解途径(glycolytic pathway)三羧酸循环(tricarboxylic acid cycle)磷酸戊糖途径（Pentose Phosphate Pathway）糖异生（gluconeogenic pathway）胰岛素(insulin)脂类(lipids)脂肪(fat)类脂(lipoid)脂肪酸（fatty acid FA）不饱和脂肪酸（unsatyrated FA）微团(micelles)激素敏感性脂肪酶（HSL）还原酶（reductase)水解酶（hydrolase）固醇载体蛋白（sterol carrier protein, SCP）脂酰基载体蛋白（acyl carrier protein ACP)脂蛋白（lipoprotein）乳糜微粒（chylomicrons）合成酶（synthetase）肉碱（carnitine）酮体（ketone dodies）鲨烯（squalene）MV AHMG-CoA丙酮酸脱羧酶：由三种酶组成：丙酮酸脱氢酶、二氢硫辛酰胺脱氢酶、二氢硫辛酰胺转乙酰酶五种辅助因子：TPP硫胺素焦磷酸酯、NAD+（Vpp）尼克酰胺腺嘌呤二磷酸、硫辛酸、FAD（VB2）黄素腺嘌呤二核苷酸、HSCoA 辅酶A必需氨基酸（共8种）：赖氨酸（Lys）色氨酸（Trp）苯丙氨酸（Phe）蛋氨酸（Met）苏氨酸（Thr）亮氨酸（Leu）异亮氨酸（Ile）缬氨酸（Val）ATP循环：ADP接受高能化合物的磷酸基团和能量,或从呼吸链中直接获取能量,用无机磷酸合成ATP;生成的ATP又可以水解变成ADP,释放的能量合成代谢和其它需要能量的生理活动.化学渗透假说：（1）复合体I, III,IV在传递电子的同时将H+泵出到内膜外（2）跨膜的电化学梯度（3）贮存在电化学梯度中的能量推动H+通过ATP合成酶回到内侧，同时使ADP磷酸化生成ATP（4）总共泵出的6H+可以驱动3分子ATP的合成。

第一章绪论高分子化合物（High Molecular Compound）：所谓高分子化合物，系指那些由众多原子或原子团主要以共价键结合而成的相对分子量在一万以上的化合物。

单体（Monomer）：合成聚合物所用的-低分子的原料。

如聚氯乙烯的单体为氯乙烯。

重复单元（Repeating Unit）：在聚合物的大分子链上重复出现的、组成相同的最小基本单元。

如聚氯乙烯的重复单元为。

单体单元（Monomer Unit）：结构单元与原料相比，除了电子结构变化外，其原子种类和各种原子的个数完全相同，这种结构单元又称为单体单元。

结构单元（Structural Unit）：单体在大分子链中形成的单元。

聚氯乙烯的结构单元为。

聚合度（DP、X n）(Degree of Polymerization) ：衡量聚合物分子大小的指标。

以重复单元数为基准，即聚合物大分子链上所含重复单元数目的平均值，以表示；以结构单元数为基准，即聚合物大分子链上所含结构单元数目的平均值，以表示。

聚合物是由一组不同聚合度和不同结构形态的同系物的混合物所组成，因此聚合度是一统计平均值，一般写成、。

聚合物分子量（Molecular Weight of Polymer）：重复单元的分子量与重复单元数的乘积；或结构单元数与结构单元分子量的乘积。

数均分子量(Number-average Molecular Weight)：聚合物中用不同分子量的分子数目平均的统计平均分子量。

，Ni ：相应分子所占的数量分数。

重均分子量（Weight-average Molecular Weight）：聚合物中用不同分子量的分子重量平均的统计平均分子量。

，Wi ：相应的分子所占的重量分数。

粘均分子量（Viscosity-average Molecular Weight）：用粘度法测得的聚合物的分子量。

分子量分布（Molecular Weight Distribution, MWD ）：由于高聚物一般由不同分子量的同系物组成的混合物，因此它的分子量具有一定的分布，分子量分布一般有分布指数和分子量分布曲线两种表示方法。

Decomposition Procedures for Distributional Analysis:A Unified Framework Based on the Shapley ValueAnthony F. ShorrocksUniversity of EssexandInstitute for Fiscal StudiesFirst draft, June 1999Mailing Address:Department of EconomicsUniversity of EssexColchester CO4 3SQ, UKshora@1. IntroductionDecomposition techniques are used in many fields of economics to help disentangle and quantify the impact of various causal factors. Their use is particularly widespread in studies of poverty and inequality. In poverty analysis, most practitioners now employ decomposable poverty measures — especially the Foster et al. (1984) family of indices —which enable the overall level of poverty to be allocated among subgroups of the population, such as those defined by geographical region, household composition, labour market characteristics or education level. Recent examples include Grootaert (1995), Szekely (1995), Thorbecke and Jung (1996). Other dynamic decomposition procedures are used to examine how economic growth contributes to a reduction in poverty over time, and to assess the extent to which the impact of growth is reinforced, or attenuated, by changes in income inequality: see for example, Ravallion and Huppi (1991), Datt and Ravallion (1992) and Tsui (1996). In the context of income inequality, decomposition techniques enable researchers to distinguish the “between-group” effect due to differences in average incomes across subgroups (males and females, say), from the “within-group” effect due to inequality within the population subgroups. (See ???). Decomposition techniques have also been developed in order to measure the importance of components of income such as earnings or transfer payments.Despite their widespread use, these procedures have a number of shortcomings which have become increasingly evident as more sophisticated models and econometrics are brought to bear on distributional questions. Four broad categories of problems can be distinguished. First, the contribution assigned to a specific factor is not always interpretable in an intuitively meaningful way. As Chantreuil and Trannoy (1997) and Morduch and Sinclair (1998) point out, this is particularly true of the decomposition by income components proposed by Shorrocks (1982). In other cases, the interpretation commonly given to a component may not be strictly accurate. Foster and Shneyerov (1996), for example, question the conventional interpretation of the between-group term in the decomposition of inequality by subgroups.The second problem with conventional procedures is that they often place constraints on the kinds of poverty and inequality indices which can be used. Only certain forms of indices yield a set of contributions that sum up to the amount of poverty or inequality thatX k ,k '1,2,...,m I 'f (X 1,X 2,...,X m )f (@)requires explanation. Similar methods applied to other indices require the introduction of a vaguely defined residual or “interaction” term in order to maintain the decomposition identity. The best known example is the subgroup decomposition of the Gini coefficient,which has exercised the minds of many authors including Pyatt (1976) and Lambert and Aronson (1993).A less familiar, but potentially much more serious, problem concerns the limitations placed on the types of contributory factors which can be considered. Subgroupdecompositions can handle situations in which the population is partitioned on the basis of a single attribute, but have difficulty identifying the relevant contributions in multi-variate decompositions. Nor is there any established method of dealing with mixtures of factors,such as a simultaneous decomposition by subgroups (into, say, males and females) and income components (say, earnings and unearned income). As more sophisticated models are used to analyse distributional issues, these limitations have become increasingly evident.The studies by Cowell and Jenkins (1995), Jenkins (1995), Bourguignon et al. (1998), and Bouillon et al. (1998) illustrate the range of problems faced by those trying to apply current techniques to complex distributional questions.The final criticism of current decomposition methods is that the individual applications are viewed as different problems requiring different solutions. No attempt has been made to integrate the various techniques within a common overall framework. This is the mainreason why it is impossible at present to combine decompositions by subgroups and income components. Yet the individual applications share certain features and objectives which enable a common structure to be formulated. Let I represent an aggregate statistical indicator, such as the overall level of poverty or inequality, and let ,denote a set of contributory factors which together account for the value of I . Then we can write(1.1),where is a suitable aggregator function representing the underlying model. The objective in all types of decomposition exercises is to assign contributions C to each of the k factors X , ideally in a manner that allows the value of I to be expressed as the sum of the k factor contributions.The aim of this paper is to offer a solution to this general decomposition problem and to compare the results with the specific procedures currently applied to a number of distributional questions. In broad terms, the proposed solution considers the marginal effect on I of eliminating each of the contributory factors in sequence, and then assigns to each factor the average of its marginal contributions in all possible elimination sequences. This procedure yields an exact additive decomposition of I into m contributions.Posing the decomposition issue in the general way indicated by (1.1) highlights formal similarities with problems encountered in other areas of economics and econometrics. Of particular relevance to this paper is the classic question of cooperative game theory, which asks how a certain amount of output (or costs) should be allocated among a set of contributors (or beneficiaries). The Shapley value (Shapley, 1953) provides a popular answer to this question. The proposed solution to the general decomposition problem turns out to formally equivalent to the Shapley value, and is therefore referred to as the Shapley decomposition. Rongve (1995) and Chantreuil and Trannoy (1997) have both applied the Shapley value to the decomposition of inequality by income components, but fail to realise that a similar procedure can be used in all forms of distributional analysis, regardless of the complexity of the model, or the number and types of factors considered. Indeed, the procedure can be employed in all areas of applied economics whenever one wishes to assess the relative importance of the explanatory variables.The paper begins with a description of the general decomposition problem and the proposed solution based on the Shapley value. Section 3 shows how the procedure may be applied to three issues concerned with poverty: the effects of growth and redistribution on changes in poverty; the conventional application of decomposable poverty indices; and the impact of population shifts and changes in within-group poverty on the level of poverty over time.Section 4 looks in more detail at the features of the Shapley decomposition in the context of a hierarchical model in which groups of factors may be treated as single units. This leads to a discussion of the two-stage Shapley procedure associated with the Owen value (Owen, 1977). A number of results in this section establish the conditions under which the Shapley and Owen decompositions coincide, and indicate several ways of simplifying the calculation of the factor contributions. These results are then used tok0K'{1,2,...,m}I'f(X1,X2,...,Xm)f(@)XkX k ,kóSgenerate the Shapley solution to the multi-variate decomposition of poverty by subgroups, a problem which has not been solved before.In Sections 5 and 6, attention turns to inequality analysis, beginning with decomposition by subgroups using the Entropy and Gini measures of inequality. This is followed by a discussion of the application of the Shapley rule to decomposition by source of income.The main purpose of these applications is to see how the Shapley procedure compares with existing techniques in the context of a variety of standard decomposition problems. The overall results are encouraging. In all cases, the Shapley decomposition either replicates current practice or (arguably) provides a more satisfactory method of assigning contributions to the explanatory factors. However, the greatest attraction of the procedure proposed in this paper is that it overcomes all four of the categories of problems associated with present techniques. As a consequence, it offers a unified framework capable of handling any type of decomposition exercise. After summarising the principal findings of this paper, Section 8 briefly discusses the wide range of potential applications to issues which have not previously been considered candidates for decomposition analysis.2. A General Framework for Decomposition AnalysisConsider a statistical indicator I whose value is completely determined by a set of m contributory factors, X, indexed by , so that we may writek(2.1),where describes the underlying model. In the applications examined later, the indicator I will represent the overall level of poverty or inequality in the population, or the change in poverty over time. The factor may refer to a conventional scalar or vector variable, but other interpretations are possible and often desirable; for the moment it is best regarded as a loose descriptive label capturing influences like “uncertain returns to investments”,“differences in household composition” or “supply-side effects”.In what follows, we imagine scenarios in which some or all of the factors are eliminated, and use F(S) to signify the value that I takes when the factors , have been dropped. As each of the factors is either present or absent, it is convenient to characterise+K ,F ,F :{S |S f K }6ú+K ,F ,C k ,k 0K ,C k 'C k (K ,F ),k 0K ,+K ,F ,j k 0KC k (K ,F )'F (K ),+K ,F ,M k (K ,F )'F (K )&F (K \{k }),k 0K .F '(F 1,F 2,...,F m )S (F r ,F )'{F i |i >r }F r If F (·) is derived from an econometric model, this constraint will usually mean that one of the factors 1represents the unexplained residuals. When the constraint is not satisfied automatically, I can always be renormalised so that it measures the “surplus” due to the identified factors.the model structure in terms of the set of factors (or, more accurately, “factor indices”), K , and the function . Since that the set of factors completely accounts for I , it will also be convenient to assume throughout that F (i ) = 0: in other words, that I is zero when all the factors are removed.1A decomposition of is a set of real values indicating thecontribution of each of the factors. A decomposition rule C is a function which yields a set of factor contributions(2.2)for any possible model .In seeking to construct a decomposition rule, several desiderata come to mind. First,that it should be symmetric (or anonymous ) in the sense that the contribution assigned to any given factor should not depend on the way in which the factors are labelled or listed.Secondly, that the decomposition should be exact (and additive), so that(2.3) for all .When condition (2.3) is satisfied, it is meaningful to speak of the proportion of observed inequality or poverty attributable to factor k .It is also desirable that the contributions of the factors can be interpreted in an intuitively appealing way. In this respect, the most natural candidate is the rule which equates the contribution of each factor to its (first round) marginal impact(2.4)This decomposition rule is symmetric, but will not normally yield an exact decomposition. A second possibility is to consider the marginal impact of each of the factors when they are eliminated in sequence. Let indicate the order in which the factors are removed, and let be the set of factors that remain after factorB (|S |,|M |)|M |C Fk 'F (S (k ,F )^{k })&F (S (k ,F ))')k F (S (k ,F )),k 0K ,)k F (S )/F (S ^{k })&F (S ),S f K \{k },S (F r ,F )S (F r %1,F )^{F r %1}r '1,2,...,m &1j k 0K C F k 'j m r '1C F F r 'j mr '1[F (S (F r ,F )^{F r })&F (S (F r ,F ))]'F (S (F 1,F )^{F 1})&F (S (F m ,F ))'F (K )&F (i )'F (K ).C F k C S k (K ,F )'1m !j F 0E C F k '1m !j F 0E)k F (S (k ,F ))'j m &1s '0j S f K \{k }|S |'s 1m !j F 0E S (k ,F )'S )k F (S )'j m &1s '0j S f K \{k }|S |'s (m &1&s )!s !m !)k F (S ).B (s ,m &1)'(m &1&s )!s !/m !C S k (K ,F )'j S K \{k }B (|S |,|K \{k }|))k F (S )'õS f K \{k })k F (S ),k 0K ,õS f L is the probability of randomly selecting the subset S from M , given that all subset sizes from 0 to 2 are equally likely.has been eliminated. Then the marginal impacts are given by(2.5)where(2.6)is the marginal effect of adding factor k to the set S . Using the fact that = for , we deduce(2.7) The decomposition (2.5) is therefore exact. However, the value of the contribution assigned to any given factor depends on the order in which the factors appear in the elimination sequence F , so the factors are not treated symmetrically. This “path dependence” problem may be remedied by considering the m ! possible elimination sequences, denoted here by the set E , and by computing the expected value of when the sequences in E are chosen at random. This yields the decomposition rule C given byS (2.8)Using to indicate the relevant probability, equation (2.8)2is expressed more succinctly as(2.9) where is the expectation taken with respect to the subsets of L .From (2.7) it is clear that C is an exact decomposition rule, and also one which treats SC k (K ,F ){)k F (S )|S f K \{k }}P (µt ,L t )G 'µ2/µ1&1 Several characterisations of the Shapley value are available, and may be reinterpreted in the present3framework. For example, (2.8) is the only symmetric and exact decomposition rule which, for each k , yields contributions that depend only on the set of marginal effects relating to factor k (Young, 1985).the factors symmetrically. Furthermore, the contributions can be interpreted as the expected marginal impact of each factor when the expectation is taken over all the possibleelimination paths.Expression (2.8) will be familiar to many readers, since it corresponds to the Shapley value for the cooperative game in which “output” or “surplus” F (K ) is shared amongst the set of “inputs” or “agents” K (see, for example, Moulin (1988, Chapter 5)). The application to distributional analysis is quite different from the context in which the Shapley value was conceived, and the results therefore need to be reinterpreted. Nevertheless, it seems convenient and appropriate to refer to (2.8) as the Shapley decomposition rule .33. Applications of the Shapley Decomposition to Poverty AnalysisTo illustrate how the Shapley decomposition operates in practice, this section looks at three simple applications to poverty analysis.3.1. The Impact of Growth and Redistribution on the Change in PovertyAn important issue in development economics concerns the extent to which economic growth helps to alleviate poverty. With a fixed real poverty standard, growth is normally expected to raise the incomes of some of the poor, thereby reducing the value of anyconventional poverty index. However, thus tendency can be moderated, or even reversed,if economic growth is accompanied by redistribution in the direction of increased inequality.Datt and Ravallion (1992) suggest a method for separating out the effects of growth and redistribution on the change in poverty between two points of time. Given a fixed poverty line, the poverty level at time t (t = 1, 2) may be expressed as a function of mean income µ and the Lorenz curve L . Denoting the growth factor by , and the t tR 'L 2&L 1)P 'P (µ2,L 2)&P (µ1,L 1)'P (µ1(1%G ),L 1%R )&P (µ1,L 1)'F (G ,R ).F (G ,R )F (R )F (G )F (G ,R )&F (R )F (R ) This is a slight abuse of notation, as the growth and redistribution factors ought to be distinguished from the 4variables representing growth and redistribution. However, in this example, the growth and redistribution factors are eliminated by setting G and R equal to zero, so no serious confusion arises. Factors and variables are distinguished more carefully in later sections.redistribution factor by , the problem becomes one of identifying the4contributions of growth G and redistribution R in the decomposition of(3.1) Figure 1: The Shapley decomposition for the growth andredistribution components of the change in poverty'(('F (i ) = 0 Figure 1 illustrates the basic structure of the Shapley decomposition for this example,which is particularly simple given that there are just two factors,G and R , and hence only two possible elimination sequences. Eliminating G before R produces the path portrayed on the left, with the marginal contribution for the growth factor, and the contribution for the redistribution effect. Repeating the exercise for the right-handC S G '12[F(G,R)&F(R)%F(G)]C S R '12[F(G,R)&F(G)%F(R)]F(R)P(µ1,L2)&P(µ1,L1)DLP(µ1,L)DL P(µ,L)P(µ,L2)&P(µ,L1)F(G)P(µ2,L1)&P(µ1,L1)DµP(µ,L1)DµP(µ,L)P(µ2,L)&P(µ1,L)C SG'12[F(G,R)&F(R)%F(G)]'12[P(µ2,L2)&P(µ1,L2)%P(µ2,L1)&P(µ1,L1)]'12[DµP(µ,L1)%DµP(µ,L2)]C SR'12[DLP(µ1,L)%DLP(µ2,L)].C SGC SRpath, and then averaging the results, yields the Shapley contributions(3.2)When growth is absent, G takes the value 0 and the change in poverty becomes(3.3) = = ,where = indicates the rise in poverty due to a shift in theLorenz curve from L to L, holding mean income constant at µ. Conversely, eliminating12the redistribution factor by setting R = 0 yields(3.4) = = ,where = is the rise in poverty due to a change in meanincome from µ to µ, with the fixed Lorenz curve L. The Shapley contributions for the 12growth and redistribution effects are therefore given by(3.5)These contributions sum up, as expected, to the overall change in poverty, and have intuitively appealing interpretations. The growth component, , indicates the rise inpoverty due to a shift in mean income from µ to µ, averaged with respect to the Lorenz12curves prevailing in the base and final years, while the redistribution effect, , represents the average impact of the change in the distribution of relative incomes, with the average taken with respect to the mean income levels in the two periods.Despite the attractions of the Shapley decomposition values given in (3.5), these are not the contributions proposed by Datt and Ravallion (1992). Instead, they associate the growth and redistribution effects with the marginal change in poverty starting from the base yearC G 'M G ({G ,R },F )C R 'M R ({G ,R },F )C G 'D µP (µ,L 1)C R 'D L P (µ1,L ))P 'C G %C R %E P 'j mk '1<k P k k 0K '{1,2,...,m } These terms correspond to the first round marginal effects; in other words, and 5 in the notation of (2.4).situation. This yields the contributions and . These do 5not sum to the observed change in poverty, so Datt and Ravallion are obliged to introduce a residual term E into their decomposition equation(3.6).They acknowledge the criticisms which can be levelled against the residual component, and note that it can be made to vanish by averaging over the base and final years, as is done in (3.5). However, this solution is rejected as being arbitrary (Datt and Ravallion, 1992,footnote 3). Far from being arbitrary, the above analysis suggests that this is exactly the outcome which results from applying a systematic decomposition procedure to the growth-redistribution issue. Furthermore, the general framework outlined in Section 2 offers the chance of extending the analysis to cover not only changes in the poverty line, but also more disaggregated influences such as changes in mean incomes and income inequality within the modern and traditional sectors.3.2 Decomposable Poverty IndicesAnother standard application of decomposition techniques involves the use ofdecomposable poverty indices. When assigning contributions to subgroups of the population, such indices enable the overall degree of poverty, P , to be written(3.7) ,where < and P respectively indicate the population share and poverty level associated with k k subgroup . Indices with this propoerty — especially the family of measures proposed by Foster et al. (1984) — are nowadays used routinely to study theway in which differences according to region, household size, age, and education attainment contribute to the overall level of poverty.In many respects, this is the simplest and most clear-cut application of decomposition techniques. Suppose, for instance, that the population is partitioned into m regions. Then factor k can be interpreted as “poverty within region k ”, and the question of interest is the+K ,F ,F (S )'j k 0S<k P k )k F (S )'<k P k for all S f K \{k }<k P kC S k (K ,F )'M k (K ,F )'<k P k ,k 0K .P 'j k 0K j R 0L<k R P k R m 1%m 2+K ^L ,F ,F (S ^T )'j k 0S j R 0T <k R P k R S f K ,T f L contribution which this factor makes to poverty in the whole country. Adopting the notation of Section 2, the model structure is defined by(3.8),so(3.9).Since eliminating poverty in region k reduces aggregate poverty by the amount regardless of the order in which the regions are considered, it follows from (2.4) and (2.9)that these values yield both the first round marginal effects and the terms in the Shapley decomposition; in other words(3.10)Not surprisingly, this allocation of poverty contributions to population subgroups accords exactly with common practice.A more complex situation emerges if we wish to perform a simultaneous decomposition by more than one attribute. In fact there is no recognised procedure at present for dealing with this problem. Suppose, for instance, that the population is subdivided into m regions 1indexed by K and m age groups indexed by L . This yields a total of m m region-age cells 2 1 2which, if treated separately, can be assigned contributions as before, by replacing equation (3.7) with(3.11),where the subscripts k R refer to region k and age group R . However, we are more likely to be interested in the overall impact of poverty in region k , or in age group R , rather than the contribution of the subgroup corresponding to region k and age group R . In other words,what we really seek are the contributions associated with the regional and age factors.The Shapley procedure offers a solution to this problem by defining the model structure where(3.12).)k F (S ^T )'j R 0T <k R P k R ')k F (T ),S f K \{k },T f L {S |S f M }{M \S |S f M }B (|S |,|M |)B (|M \S |,|M |)õS f M F (S )'12õS f M [F (S )%F (M \S )])k F (S )%)k F (M \S )'j R 0S _L <k R P k R %j R 0(M \S )_L<k R P k R ')k F (M _L ),S f M f K ^L k 0K M 'L ^K \{k }C S k (K ^L ,F )'õS f M )k F (S )'12õS f M [)k F (S )%)k F (M \S )]'12õS f M )k F (L )'12)k F (L )'12j R 0L <k R P k R ,C S k (K ^L ,F )'12<k P kSee Proposition 4 in the next section.6Eliminating poverty in region k now yields(3.13).In contrast to (3.9) above, equation (3.13) shows that the factors no longer operateindependently: the marginal impact of removing poverty in region k depends on whether poverty has already been eliminated in one or more of the age groups.To obtain the Shapley contributions for the regions, first note that = and = , so(3.14).Note also that (3.13) implies(3.15)for all . So choosing any and setting yields(3.16)or equivalently, in the notation of (3.7),(3.17)Thus, in this two attribute example, each region is assigned exactly half the contribution that would be obtained in a decomposition by region alone. A similar result applies to the age group factors. More generally, in a simultaneous decomposition by n attributes, each factor is allocated one n th of the contribution obtained in the single attributedecomposition.6This result will be comforting to those who use decomposable poverty indices, for it shows that nothing is lost by looking at each attribute in isolation; the outcomes of multi-attribute decompositions can be calculated immediately from the series of single attribute< k t P k t)P'j k0K[<k2P k2&<k1P k1])Pk 'Pk2&Pk1,k0K)<k '<k2&<k1,k0KKp'{pk,k0K}Ks+Kp ^{Ks},F,F(S^T)'j k0K[<k(T)P k(S)&<k1P k1],S f K p,T f{K s}Pk(S)'9P k2if p k0SPk1if pkóS<k(T)'9<k2if T'Ks<k1if T'ipk0Kp)pkF(S^T)'<k(T)Pk(S^{pk})&<k(T)Pk(S)'<k(T))Pk,S f Kp\{pk},T f{Ks},M'{Ks}^Kp\{pk})pkF(S)%)pkF(M\S)'<k(S_{Ks}))Pk%<k((M\S)_{Ks}))Pk'(<k1%<k2))Pk,results, and the relative importance of different subgroups remains the same, regardless of the number of attributes considered.3.3 Changes in Poverty Over TimeDecomposable poverty indices can also be used to identify the subgroup contributions to poverty changes over time. If and represent the population share and poverty level of subgroup k0K at time t (t = 1, 2), equation (3.7) yields(3.18).The aim here is to account for the overall change in poverty, )P, in terms of changes in poverty within subgroups, , and the population shifts between subgroups, .The subgroup poverty values can be changed independently, so the poverty change factors may be indexed by the set . However the population shifts necessarily sum to zero. To avoid complications at this stage, the population shift factors will be treated as a single composite factor, denoted by . The model structureis then given by(3.19),where(3.20) ;For each we have(3.21)and setting , it follows that(3.22)S f MC S pk 'õS f M)pkF(S)'12õS f M[)pkF(S)%)pkF(M\S)]'12õS f M(<k1%<k2))Pk'<k1%<k22)Pk.)KsF(S)'j k0K[<k(K s)&<k(i)]P k(S)'j k0K P k(S))<k,S f K p.C S Ks 'õS f Kp)KsF(S)'12õS f Kp[)KsF(S)%)KsF(Kp\S)]'12õS f Kpjk0K[Pk(S)%Pk(Kp\S)])<k'12õS f Kpjk0K[Pk1%Pk2])<k'j k0K P k1%P k22)<k )P'j k0K<k1%<k22)P k%j k0K P k1%P k22)<kfor all . So, using (3.14), the Shapley contribution associated with the change in poverty within subgroup k is given by(3.23)Conversely(3.24)So(3.25)This is a very natural allocation of contributions given that we seek a decomposition which treats the factors in a symmetric way, and given that (3.18) may be rewritten(3.26).4. Hierarchical StructuresDespite its attractive properties, the Shapley decomposition has one major drawback for distributional analysis: the contribution assigned to any given factor is usually sensitive to the way in which the other factors are treated. In many applications, certain groups of factors naturally cluster together. This leads to a hierarchical structure comprising a set of primary factors, each of which is subdivided into a (possibly single element) group of secondary factors. For example, when income inequality is decomposed by source of income (see Section 6 below), one may first wish to regard income as the sum of labour income, investment income and transfers. Then investment income, say, might be split into interest, dividends, capital gains and rent. The Shapley decomposition does not guarantee。