Short-timescale Variability in the Broadband Emission of the Blazars Mkn421 and Mkn501
Brown运动的极限定理
摘 要Brown 运动的极限定理(Limit Theorem of Brownian Motion)是概率论极限理论的一个重要分支,对Brown 运动以及与Brown 运动相关随机过程轨道的性质的研究是一个广泛研究的课题.本文目的是研究布朗运动增量在一定条件下的极限定理,推广了前人的一些主要结果.本文的研究内容组织如下:第一章为绪论,介绍了Brown 运动的有关发展历史及已有的研究结果. 第二章为预备知识,介绍了一些记号与基本概念.第三章至第四章为本文研究的主要结果.在第三章,我们研究了布朗运动增量在Hölder 范数下的C-R 型局部泛函极限定理.第四章,研究了Brown 运动增量在Hölder 范数下关于,..r p C q s -容度的收敛速率.第五章,对本文工作总结及展望.关键词:Brown 运动;Hölder 范数;容度;收敛速率;AbstractLimit Theorem of Brownian motion is an important branch of Probability Limit Theorem. The topic on the path properties of Brownian motion and its relative stochastic process is widely researched. The purpose of this paper is to study Limit Theorem of Brownian motion and increments of a Brownian sheet under certain conditions. Some important results of predecessors are extended and improved. The content of this paper is organized as follows:The first chapter is an introduction which presents the development history and the existing research results of Brownian motion.The second chapter is preliminary knowledge which introduces signs and some basic concepts.The main results can be seen from chapter three to chapter four. The third chapter studies the theorem of C-R local functional limit for increments of a Brownian motion in Hölder norm. The forth chapter discusses the convergence rate of increments of a Brownian motion about the capacity of ,..r p C q s -in Hölder norm.The fifth chapter summarizes the content of this paper and makes the prospect.Key words: Brownian motion; Hölder norm; Capacity; The rate of convergence;第一章引言§1.1 Brown运动发展过程及有关应用Brown运动(Brownian motion,简称BM)在数学学科上也可以称为维纳过程,Brown运动作为具有连续时间参数和连续状态空间的一个随机过程,是随机过程学科中的最简单的、最基本的、最常见的随机过程之一.许多随机过程可看成Brown运动的推广或者泛函.Brown运动作为物理现象,首先由英国生物学家Robert Brown在1827年观察花粉微粒在液面上的“无规则运动”而提出,后来才由德耳索作出了正确的定性分析.在1905年,爱恩斯坦首次对这种“无规则运动”现象的物理规律,建立了一种数学模型,这一模型的问世使这一理论有了明显的发展.最后在Smoluchowski,Fokker,Planck,Burger,Furth,Ublenbeck等著名学者的努力下,这方面的理论工作得以迅速发展起来了.但在数学方面,由于缺少精确描述,因而进展较为缓慢,一直到了1918年才由Wiener提出了在Brown运动空间上定义测度与积分的精确且严格的数学定义,定义表明了Brown运动是一种独立增量过程,是一个具有连续时间参数和连续状态空间的随机过程.它是随机过程中最简单,最重要的特例,许多不同类型的重要随机过程都可以看做它的泛函或某种意义下的推广,这些工作推动了Brown运动研究的快速发展,并逐渐令其渗透到概率论的各个分支中,使之成为现代概率理论的重要篇章.在当今迅猛发展的时期,伴随着科学技术的快速发展和普及,又特别是计算机科学的广大应用,对Brown运动性质的探讨和研究意义深远且已取得了质的飞跃,从应用角度来看,工程技术,经济管理等广泛领域中都有“噪声”与涨落现象存在,它们往往涉及Brown运动,也就需要Brown运动的理论;又由于Brown运动与热传导方程有密切联系,使它成为概率论与分析联系的重要纽带.目前,六十年代中以来发展起来的Brown运动的极限定理已广泛地出现在多个领域中,如物理学、经济数学、通信理论、金融学、与数理统计等等学科.比如最经典的,也是较为突出的贡献就是将Brown运动与股票价格行为联系在一起,进而建立起Brown运动的股票价格数学理论模型,这是二十一世纪的一项具有突破性的重要意义的创新课题,给历史翻开了崭新的一页.当代资本市场理论的核心假设之一是Brown运动假设,市场理论认为证券期货价格具有随机性上下波动的特性,因此对Brown运动性质的研究在现代金融数学中起举足轻重的作用.因此诸多专家和学者对Brown运动及其相关的轨道性质进行了深入的研究.可见,对Brown运动的性质进行深入研究意义非常重大,这不仅极大的丰富了概率论的知识体系,而且为其实际应用提供了强而有力的理论指导.Brown运动的极限定理已经成为概率论极限理论学科中的一个比较热门的研究课题.国内外许多的概率论工作者纷纷对Brown运动的轨道的极限性质进行了广泛深入地研究.一些与Brown 运动有关的随机过程,比如广义Brown 运动[1-2]、Guass 过程[3-4]、扩散过程[5-6]、稳定过程[7]等的极限性质亦被大量研究.对Brown 运动轨道性质的研究是Brown 运动的极限定理主要研究内容之一,比如研究Brown 运动在一定的假设条件下的连续模定理[8-9]、Brown 运动Strassen 重对数律[10-11],或者Brown 运动增量有多大[12]以及增量有多小[13]的问题,还有一些泛函极限定理问题[14-15]等等.布朗运动的极限定理作为一门广泛研究的课题,随着人们的不断深入研究,将会有更多新结果.§1.2 与本文研究有关的结果对Brown 运动的轨道性质的研究是Brown 运动极限定理的重要内容,发展至今已有几十年的历史.在这几十年里,随着研究的不断深入,研究成果不断丰富和完善起来,对它的研究既深化和丰富了极限理论学科中经典理论的重要的基本结果,同时也开拓了对其他随机过程重对数律的研究.后来,对Brown 运动增量的极限定理也做了许多研究,其中最重要的内容是Csörgö-Révész 有关研究结果,我们介绍如下:设(){}0B B t t =≥;是d -维标准Brown 运动,记[][]()(){}0000d d C T f C T R f =∈=,,;;.设不减函数()():00u a ∞→∞,,,满足: (1) ()0u a u u ≤∈∞,,; (2)uua 非减; (3) ()log /limlog log u u u a u→∞=∞. 对1-维Brown 运动,Csörgö和Révész [12-13]得到如下结果. 如果u a 满足(1)、(2),则得到()()00limsup sup sup 1u uu u t u a s a B t s B t β→∞≤≤-≤≤+-=, ..a s , (1-2-1)与()()00liminf sup sup 1u uu u t u a s a B t s B t γ→∞≤≤-≤≤+-=, ..a s , (1-2-2)其中1/2log 2log u u u u u a a β-⎛⎫= ⎪⎝⎭且1/22log 8log u u u u u a a γπ-⎛⎫ ⎪⎪= ⎪ ⎪⎝⎭.若(3)也成立,则(1-2-1)与(1-2-2)的上下极限可换为极限.(1-2-1)泛函版本已被Révész [10]给出.Révész 结果如下:命题1.2.1 若条件(1)、(2)成立,则[]()0,1/limsup sup,0u u u t a u t u K β→∞∈-∆-=, ..a s (1-2-3)且对所有K ϕ∈,有[]()0,1/limsupinf,0u u t a u u t u βϕ∈-→∞∆-=, ..a s (1-2-4)其中:()()()()u t u s B ut a s B ut ∆=+-,,01t ≤≤,[]01s ∈,[](){}12'001d 1dK C t t ϕϕ=∈≤⎰,;后来,危启才将结果推广到Hölder 范数情形[15],结论如下:命题1.2.2若条件(1)、(2)成立,则 []()0,1/l i m s u ps u p ,0u u u t a ut u K αβ→∞∈-∆-=,..a s (1-2-5) 且对任意K ϕ∈,有[]()0,1/limsupinf,0u u t a u u t u αβϕ∈-→∞∆-=, ..a s (1-2-6)若条件(3)成立,则有[]()0,1/lim inf,0u u u t a u t u αβϕ→∞∈-∆-=, ..a s (1-2-7)后来,高付清、王清华[16]研究了命题1.2.1的收敛速率,其结果如下.命题1.2.3 设条件(1)、(2)成立,则对任意K ϕ∈,()1'0d 1t t ϕ<⎰,有[]()()0,1/log liminf 2loginf ,u u u t a u u u ut u b a βϕϕ→∞∈-∆-=, ..a s , (1-2-8) 其中()()1/212'0/21d d c b t t ϕϕ⎛⎫⎪= ⎪ ⎪-⎝⎭⎰,d c 是正常数,精确值见Ciesielski 和Taylor [17]. 更进一步,若条件(3)成立,则[]()()0,1/log lim 2loginf ,u u u t a u uu ut u b a βϕϕ→∞∈-∆-=, ..a s (1-2-9)特别在(1)、(2)成立时,()()1/200log log liminf infsup 1u uu u t u a s ad u u u a B t s B t c a →∞≤≤-≤≤⎛⎫ ⎪⎪+-= ⎪ ⎪⎝⎭, ..a s (1-2-10)若(3)成立,则()()1/200log log lim infsup 1u uu u t u a s ad u u u a B t s B t c a →∞≤≤-≤≤⎛⎫ ⎪⎪+-= ⎪ ⎪⎝⎭, ..a s (1-2-11)不久,高付清和王清华的研究结果被推广到Hölder 范数情形[18].命题 1.2.4如果u a 为定义于()0+∞,上的非减函数,且(1)、(2)成立,那么对任意f K ∈,且()1I f <,有[]()()10,1/l o g l i m i n f l o g i n f u u u t a uu u u t u fb f a ααβ-→∞∈-⎛⎫∆-= ⎪⎝⎭,, ..a s (1-2-12)其中()()()1/21k b f I f αα-⎛⎫= ⎪ ⎪-⎝⎭,()k α是一个正常数,精确值见Baldi 与Roynette [14].命题1.2.5 如果u a 为定义于()0+∞,上的非减函数,且 (1)、(2)与(3)成立,那么对任意f K ∈,()1I f <,有[]()()10,1/l o g l i m l o g i n f u u u t a uu u u t u fb f a ααβ-→∞∈-⎛⎫∆-= ⎪⎝⎭,, ..a s (1-2-13)其中()()()1/21k b f I f αα-⎛⎫= ⎪ ⎪-⎝⎭,()k α是正常数,精确值见Baldi 与Roynette [14].§1.3 本文的主要工作及其结构安排读研期间,在导师的指导和帮助之下,对Brown 运动的轨道的一些极限性质进行了大量的学习和探索.受到文献[15]、[16]、[22]等的启发,本文研究布朗运动的局部泛函极限定理,得到了布朗运动增量在Hölder 范数下的C-R 型局部泛函极限定理。
快速谱峭度的英文
快速谱峭度的英文Kurtosis is a statistical measure of the degree of peakedness, or the tails of a distribution relative to the normal distribution. In simple terms, it is a measure of the distribution’s outliers, or the amount of data that falls outside the typical range of values. Kurtosis can help usto understand the distribution of data, and it can assist in anomaly detection or flagging outliers.In statistics, there are several different ways to measure kurtosis, and one such measure is known as fast kurtosis.Fast kurtosis is a technique that is used to estimate kurtosis quickly and efficiently. This technique involves using a numerical algorithm to calculate the kurtosis of a given set of data. Compared to traditional methods, fast kurtosis can process large amounts of data quickly, and it isnot affected by the skewness of the data. This means that it can operate on both positively skewed and negatively skewed datasets with ease.The method of fast kurtosis uses a transform known as the fourth order moment spectrum. This transform works by generating a new set of data from the original input data, using a Fourier transformation. The new data set with the frequency domain representation is then used to calculate kurtosis quickly and efficiently. This method has the added benefit of being computationally efficient, meaning that it can be very fast, even when dealing with large data sets.Fast kurtosis has many practical applications in data science, finance, and engineering. It is commonly used in the stock market, where traders and analysts use it to flag potential anomalies in stock prices. These anomalies can signal an unexpected change in the market, which can indicate a buying or selling opportunity. Fast kurtosis is also used in econometrics, where it can be used to test for the presence of non-normality in economic data.In neuroscience, fast kurtosis is used to analyze the diffusion of water in brain tissue. Itis an important tool used to study neural connections, which can reveal information about neural processes and their changes over time. MRI scans are used to quickly analyze brain tissues, and fast kurtosis can help to identify nerve fiber bundles and other structures in the brain. This can be useful in the study of diseases such as Alzheimer's and other cognitive disorders.In conclusion, fast kurtosis is a valuable tool in statistical analysis, which enables researchers to gain insights into the kurtosis of a distribution, allowing them to identify outliers and anomalies quickly and efficiently. The speed of fast kurtosis makes it feasible to use in large datasets and in real-time scenarios. As such, it is widely used across many fields, including finance, engineering, and neuroscience. The practical applications of fast kurtosis ensure that it will remain an essential tool for the foreseeable future.。
多时间尺度气候变化
响,但自然温度的量值难以确定。
同样在一个城市,在水体附近(或公园里)的近 几十年气温趋势大约是其他观测气温趋势的一半[5]。
全球(或北半球)的平均气温趋势应该是所有地球表
面上气温趋势的面积平均值。一个比较客观的做法是
气候随时间的变化 与强迫F(t)之间在时间上不是同时
的对应关系[1],而有一个时间上的滞后
。在
大气中,L和D与大气流体的热力学性质有关,如扩散
过程等。地球上,一地气候的日气温逐小时变化,是
地球自转太阳辐射量变化的结果。描述大气热力、动
力性质的观测变量有多个,它们的变化既有天气部分
的扰动,也有气候部分的规则变化和观测误差。在一
的位相滞后太阳辐射准11a周期的位相1~2a。此外,
太阳活动和全球平均气温也都有准22a的位相循环周
期变化,气温变化的每小时速率大约是准11a变化的
一半。在全球气温变化的准11a、22a和海气耦合形成 的准60a周期中,准60a周期的气温变化幅度最大,为 0.2℃[4],变化的速率为3.8×10—7℃/h。
射不到的墙体北侧。到19世纪,温度计才进驻到了百 叶箱里。用塑料百叶箱代替木质百叶箱和用电子温度
计代替酒精温度计后,气温可降低0.4℃。志愿船舶 测量海水温度,木质吊桶与帆布吊桶取水和在船的不
同部位取水,测量到的海温也可相差0.4~0.5℃。随 着城市的发展,原来在郊区的那些气象观测仪器现在
已经在城市中心区了。气温升高也受到城市发展的影
1 引言
现代气候描述的是一段时间内气象要素和天气现 象的平均或统计状态,一段时间为月、季、年、数年 到数百年以上的尺度。气候以气温(冷、暖)和降水 (干、湿)两个基本的要素表示,通常用某一时段的 平均值和离差值表征。气候是某一时段内热平衡下的 大气多要素状态分布。
Annual and interannual (ENSO) variability of spatial scaling properties of (NDVI) in Amazonia
Annual and interannual (ENSO)variability of spatial scaling propertiesof a vegetation index (NDVI)in AmazoniaGerma ´n Poveda *,Luis F.SalazarPosgrado en Recursos Hidra ´ulicos,Escuela de Geociencias y Medio Ambiente,Universidad Nacional de Colombia,Medellı´n,ColombiaReceived 15January 2004;received in revised form 3August 2004;accepted 5August 2004AbstractThe space–time variability of the Normalized Difference Vegetation Index (NDVI)over the Amazon River basin is quantified through thebi-dimensional Fourier spectrum,and moment-scaling analysis of monthly imagery at 8km resolution,for the period July 1981–November 2002.Monthly NDVI fields exhibit power law Fourier spectra,E (k )=ck Àb ,with k denoting the wavenumber,c the prefactor,and b the scaling exponent.Fourier spectra exhibit two scaling regimes separated at approximately 29km,above which NDVI exhibit long-range spatial correlations (0b b b 2),and below which NDVI behaves like white noise in space (b g 0).Series of monthly values of c (t )and b (t )exhibit high negative correlation (À0.88,P N 0.99),which suggest their linkages in power laws,but also that E t (k )=c (t )k Àb (t ),with t the time index.Results show a significant negative simultaneous correlation (À0.82,P N 0.95)between monthly series of average precipitation over the Amazon,h P (t )i ,and scaling exponents,b (t );and high positive lagged correlation (0.63,P N 0.95),between h P (t )i and h NDVI(t +3)i .Parameters also reflect the hydrological seasonal cycle over Amazonia:during the wet season (November–March),b (t )ranges between 0.9and 1.15,while during the dry season (May–September),b (t )g 1.30.These results reflect the more (less)coherent spatial effect of the dry (wet)season over Amazonia,which translates into longer (shorter)-range spatial correlations of the NDVI field,as witnessed by higher (lower)values of b (t ).At interannual timescales,both phases of ENSO reflect on both parameters,as b (t )is higher during El Nin ˜o than during La Nin ˜a,due to the more coherent effects of El Nin ˜o-related dryness,whereas NDVI spatial variability is enhanced during La Nin ˜a,due to positive rainfall anomalies.Results from the moment-scale analysis indicate the existence of multi-scaling in the spatial variability of NDVI fields.Departures from single scaling exhibit also annual and interannual variability,which consistently reflect the effects from both phases of ENSO.Furthermore,departures from single scaling are independent of the order moment,q ,as the PDF of departures scaled by the mean collapse to a unique distribution.These results point out that ideas of spatial scaling constitute a promising framework to synthesize important hydro-ecological processes of Amazonia.D 2004Elsevier Inc.All rights reserved.Keywords:Annual and interannual variability;Spatial scaling;Amazonia1.Introduction1.1.NDVI and the hydrologic cycleSatellite information has contributed to improve our understanding of the spatial variability of hydro-climatic and ecological processes.Vegetation activity is tightlycoupled with climate,hydro-ecological fluxes,and terrain dynamics,and it controls water,energy and carbon budgets in river basins at a wide range of space–time scales.Indices of vegetation activity are constructed using satellite infor-mation of reflectance of the relevant spectral bands which enhance the contribution of vegetation.One such an index is the Normalized Difference Vegetation Index (NDVI),defined as the ratio of (NIR ÀRed)and (NIR+Red),where NIR is the surface-reflected radiation in the near-infrared band (0.73–1.1A m),and Red is the reflected radiation in the red band (0.55–0.68A m).Theoretically,NDVI takes values in the range from À1to 1,but the observed range is usually0034-4257/$-see front matter D 2004Elsevier Inc.All rights reserved.doi:10.1016/j.rse.2004.08.001*Corresponding author.School of Geosciences and Environment,Universidad Nacional de Colombia,Carrera 80x Calle 65,Bloque M2-315,Medellin AA1027,Colombia.Tel.:+5744255122;fax:+5744255003.E-mail address:gpoveda@.co (G.Poveda).Remote Sensing of Environment 93(2004)391–401smaller,with values around0for bare soil(low or no vegetation),and values of0.9or larger for dense vegetation. The work of Tucker(1979)pioneered the study of vegetation dynamics using red and near infrared spectral measurements.Sellers(1985)showed that NDVI is directly related to the photosynthetic capacity of plant canopies, which explains why NDVI is highly and directly correlated to the intercepted fraction of photosynthetically active radiation.As such,NDVI is independent of solar radiation, although variations in solar radiation can affect retrievals of NDVI.The meaning of diverse spectral vegetation indices is explained and summarized in Myneni et al.(1995).As NDVI represents the photosynthetic capacity or photosynthetic active radiation(PAR)absorption by green leaves,it is associated with fundamental hydro-ecological processes such as precipitation,which in turn is also directly linked to photosynthesis and hence plant growth.A recent work by Lotsch et al.(2003b)provides a comprehensive global analysis of NDVI and precipitation.Other variables pertaining to the hydrologic cycle have also been linked to NDVI,such as evaporation(Szilagyi et al.,1998;Lotsch et al.,2003b),and soil moisture(Nicholson&Farrar,1994; Farrar et al.,1994;Poveda et al.,2001).A strong relation-ship between evapotranspiration and NDVI have been identified in wet environments by Seevers and Ottmann (1994),and Nicholson et al.(1996),but also in water-limited environments,as reported by Tucker and Choudhury (1987),Malo and Nicholson(1990),Nicholson et al.(1994), Grist et al.(1997),Szilagyi et al.(1998),and Lotsch et al. (2003a).Changes in vegetation patterns have been studied at a global scale through NDVI estimates(Lucht et al., 2002).In turn,Nemani et al.(2003)identify those regions of the world where primary production is limited by water,by temperature or by both.1.2.Physical settingThe Amazon River basin provides an excellent example of the coupling and feedbacks in the land surface–atmos-phere system,due to its area larger than6.4million km2, constitute largest in the world,its tropical setting,and complex eco-hydro-climatological dynamics that exert a global influence.Scientific research towards understanding the hydro-climatic and ecological functioning of the Amazon is currently undergoing within the b Large-Scale Atmos-phere–Biosphere Experiment in Amazonia Q(LBA)(see Avissar and Nobre,2002;Roberts et al.,2003).Both observations and modelling results suggest strong changes in global,regional and local atmospheric circulation patterns associated with deforestation or perturbations in the land surface–atmosphere interactions over the Amazon(Salati& V ose,1984;Silva Dias et al.,1987;Zeng et al.,1996;Zhang et al.,1996;Poveda&Mesa,1997;Marengo&Nobre,2001; Werth&Avissar,2002;Nobre et al.,2004).The seasonal cycle of precipitation exhibits a wet season during Novem-ber–March and a dry season during May–September,as a result of the latitudinal migration of the Intertropical Convergence Zone(Obregon&Nobre,1990;Zeng,1999), which interacts with the seasonal cycle of moisture-laden low level winds from the Atlantic Ocean,but also with complex feedbacks of the land surface–atmosphere system,including the significant role of evapotranspiration in precipitation recycling(Salati,1985;Eltahir&Bras,1994).This work aims to quantify how the spatial statistics of NDVI reflect the seasonal hydro-climatic variability of the Amazon.1.3.Interannual variability at ENSO timescaleAt interannual timescales,tropical South America exhib-its coherent hydro-climatic anomalies during both phases of the El Nin˜o/Southern Oscillation(ENSO)(Aceituno,1988; Kiladis&Diaz,1989;Chu,1991;Marengo&Hastenrath, 1993;Ropelewski&Halpert,1996;Poveda et al.,2001; Waylen&Poveda,2002).With minor regional exceptions in timing and amplitude,the region experiences negative anomalies in rainfall,river discharges,and soil moisture during the warm phase of ENSO(El Nin˜o),and positive anomalies during the cold phase(La Nin˜a).Both large-scale forcing and land surface hydrology play a key role on the dynamics of hydro-climatic effects of ENSO over the region (Marengo&Hastenrath,1993;Poveda&Mesa,1997), which lag anomalies in the tropical Pacific sea surface temperatures by several months.The ENSO signal prop-agates to the east in northern South America,leading hydrological anomalies by1month over western Colombia (Poveda&Mesa,1997)and by6–10months in the Amazon River basin(Richey et al.,1989;Chu,1991;Eagleson, 1994).Consistently,NDVI diminishes over tropical South America during the occurrence of the warm phase of ENSO (Myneni et al.,1996;Asner et al.,2000;Poveda et al.,2001). This work aims to quantify how the spatial statistics of NDVI reflect the interannual hydro-climatic variability of the Amazon,associated with both phases of ENSO.1.4.Scaling theories of hydro-ecological processesScaling theories have provided important clues towards understanding and modelling the space–time dynamics of diverse bio-geophysical processes,such as vegetation sur-face fluxes(Katul et al.,2001),tropical convective storms (Yano et al.,2001),modeling of rainfall fields through fractal,multi-scaling,and random cascade models(Lovejoy, 1981,1982;Lovejoy&Schertzer,1991,1992;Gupta& Waymire,1990;Over&Gupta,1994;Perica&Foufoula-Georgiou,1996;Foufoula-Georgiou,1998;Deidda et al., 1999;Harris et al.,2000;Jotithyangkoon et al.,2000; Nordstrom&Gupta,2003),maximum annual river flows (Gupta&Waymire,1990;Gupta&Dawdy,1995;Goodrich et al.,1997;Ogden&Dawdy,2003),infiltration in porous media(Barenblatt,1996),low river flows(Furey&Gupta, 2000),ecological processes(Tilman&Kareiva,1997; Bascompte&Sole,1998),and vegetation dynamics(HarteG.Poveda,L.F.Salazar/Remote Sensing of Environment93(2004)391–401 392et al.,1999;Milne&Cohen,1999;Milne et al.,2002).For instance,in the study of river floods,Gupta(2004)has explained how scaling statistics in maximum annual river flows can be used to test different physical hypotheses covering complex runoff dynamics on channel networks.Diverse multi-scale statistical techniques are used to characterize and quantify the scale dependence of geo-biophysical fields,including Fourier spectra,structure and moment-scale functions.These functions are easily comput-able and allow an understanding of the spatial structure of the fields over a wide range of scales.Also,the use of multi-scale functions allows one to identify the range of scales where the scale dependence of modelled and observed variability may deviate,and the range of scales where the two agree(Harris et al.,2000).Towards those ends,we estimate the bi-dimensional Fourier spectra of monthly NDVI fields over Amazonia,and quantify the time variability of its parameters,and how they reflect the time–space variability of NDVI and precipitation fields at annual and interannual timescales.By the same token,we like to investigate whether monthly NDVI fields exhibit simple of multi-scaling properties in space,and how they reflect the annual and interannual variability of NDVI. Thirdly,we investigate the time correlation between series of monthly values of c and b with average values of NDVI and precipitation over the entire Amazon,so as to encapsulate the hydro-ecological dynamics of Amazonia. The data sets and methodologies are described in Section2, while results are presented in Section3,and the conclusions are provided in Section4.2.Data sets and methodologiesWe used digital maps of monthly NDVI from the NASA Global Inventory Modeling and Mapping Studies (GIMMS NDVI),covering the period July1981through November2002.The imagery,which consists of8km spatial resolution NDVI images,was provided by C.J. Tucker and his colleagues at NASA Goddard Space Flight Center.The GIMMS NDVI database exhibits a great deal of improvements with respect to previous NDVI data sets, including corrections for:(i)residual sensor degradation and sensor intercalibration differences;(ii)distortions caused by persistent cloud cover in tropical evergreen broadleaf forests;(iii)solar zenith angle and viewing angle effects;(iv)volcanic aerosols;(v)missing data in the Northern Hemisphere during winter using interpolation; and(vi)short-term atmospheric aerosol effects,atmos-pheric water vapor effects,and cloud cover.For details of the GIMMS NDVI data set,see Pinzo´n et al.(submitted for publication).The GIMMS NDVI data set has been rescaled in such a way that the original values in theÀ1 to1range are obtained as ndvi=(NDVIÀ1)/249À0.05, with values larger than1representing water bodies or bad data.Precipitation data for the Amazon basin were obtained from the data set produced by the Earth Observing System-Amazon Project(EOSAP)developed by Instituto Nacional de Pesquisas Espaciais(INPE),Brazil,and the University of Washington,and contains gridded monthly rainfall(0.28 latitudeÂ0.28longitude),for the period1972–1992.This data set was provided by the Global Hydrology and Climate Center of NASA.For details of this data set,see http:// /.With the purpose of implementing the spatial scaling analysis,a2048km scale region was defined inside the Amazon basin.The observed NDVI field for July1981is shown in Fig.1,aggregated at a32km scale.Character-ization of the spatial scaling properties of NDVI monthly fields was performed through estimation of the bi-dimen-sional Fourier spectrum,and moment-scale analysis.A detailed description of the methods is provided in the following section.2.1.Bi-dimensional Fourier spectrumMany geophysical phenomena exhibit power law decay-ing Fourier spectra,(Korvin,1992;Mandelbrot,1998),i.e., E kðÞf kÀb¼ckÀbð1Þwith k being the wavenumber,c is the prefactor,and b is the scaling exponent.The spectral slope,b,becomes a measure of roughness(Davis et al.,1996;Harris et al., 1996),with low spectral slopes corresponding to rougher, less correlated fields.Scaling exponents in Fourier spectra contain key insights on the dynamics underlying the physics of highly complex phenomena.For instance,the well-known behavior of dissipation of kinetic energy in turbulent flows,for which E(k)~k5/3(Kolmogorov,1941, 1962;Frisch,1995),whose scaling exponent,5/3,summa-rizes the rate at which kinetic energy is gradually trans-ferred from larger to smaller spatial scales,such that the mean kinetic energy per unit mass per unit time is conserved.Many other geophysical phenomena exhibit power law Fourier spectra,whose scaling exponents reflect different types of statistical memory and the scale of fluctuation which is inherent to their space–time correla-tions(Korvin,1992;Mesa&Poveda,1993;Turcotte, 1997;Mandelbrot,1998;Yano et al.,2001).The bi-dimensional power spectrum is computed using standard2-D Fast Fourier Transform(FFT)algorithms (Press et al.,1992).The Fourier power or energy spectrum, E(k x,k y)of a two-dimensional field,is found by multiplying the2-D FFT by its complex conjugate,where k x and k y are the wavenumber components(Harris et al.,2000).To facilitate visualization and comparison,the2-D power spectra from the NDVI fields are averaged angularly about k x=k y=0to produce the isotropic energy spectrum,E(k), with k¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffik2xþk2yq.Such isotropic energy spectrum does not mean that the field is isotropic,but rather that the angularG.Poveda,L.F.Salazar/Remote Sensing of Environment93(2004)391–401393averaging about k x =k y =0integrates the anisotropy (Harris et al.,2000).2.2.Moment-scaling analysisMoment-scaling analysis allows the quantification of the spatial intermittency (roughness)of a field,and provides a test for the type of spatial (single or multi-)scaling behavior of random fields (Over &Gupta,1994;Harris et al.,2000).Statistical self-similarity can be thought of as statistical similarity of a random field across multiple scales,then simple scaling is a type of statistical self-similarity.Consider a random field,{X (t );t a I },where I represents an index set,and an arbitrary scalar k N 0.The random field is defined to be simple scaling if the following holds,X k t ðÞ¼dk h X t ðÞð2Þwhere the equality is understood in the sense of all finite dimensional distribution functions.From the definition of statistical moments given by E [X q]=R x q f (x )d x ,q =1,2,3,...,it is concluded that for a simple scaling random field,X (t ),E X q k t ðÞ½ ¼k h q E X q t ðÞ½ ;q ¼1;2;3;Nor ;log E X q k t ðÞ½ ¼q h log k þc q t ðÞ;ð3Þwhere c q (t )=log E [X q (t )].Eq.(3)shows that simple scaling must satisfy two conditions:(i)log–log linearity;(ii)linear slope growth,i.e.,s (q )=q h ,whereas multi-scaling holds for a nonlinear slope growth.In our case,the expected value in Eq.(3)arises from the equation that defines the scaling moments of a field X j ,which are computed for a range of averaging scales,r ,with higher values of r implying examining the phenomena at finer spatial resolution.Therefore,M q r ðÞ¼hj X r x ;y ðÞj q ið4Þwhere X r represents field values at scale r ,q is the order of the moment,and h ...i denotes the expected value of NDVI over all pixels at scale r .Typically,the scale of the image is dyadically reduced from its original highest resolution (r =1/(1pixel))by successive spatial averaging of the field by a factor of 2at each step,i.e.,r =1/(2pixels)=0.5,r =1/(4pixels)=0.25,...,r =1/(256pixels)=3.9Â10À3.Scaling of the moments means that (Gupta &Waymire,1993),M q r ðÞf r Às q ðÞð5Þwhere s (q )is the moment scaling exponent function that is estimated by log–log linear regressions of the q th moment of the NDVI field,on a scan by scan basis,as |X r |vs.log r ,for each q .It is easy to check that s (1)=0since the mean of the entire field does not depend upon the scale.Thelog–logFig.1.Location of the study region depicting the NDVI field for July 1981,aggregated at a 32km scale.G.Poveda,L.F .Salazar /Remote Sensing of Environment 93(2004)391–401394linearity of log M q (r )vs.log r provides a test of the scaling hypothesis for the moment of order q .3.Results3.1.Bi-dimensional Fourier spectrumOur generalized results indicate that the Fourier spectra exhibit two regions characterized by different scaling exponents,b ,separated at the wavenumber k =0.034km À1,which corresponds to 28.6km.Fig.2shows the 2-D Fourier spectra for the September 1989NDVI field.At larger spatial scales,the NDVI fields exhibit long-range correlations characterized by 0b b b 2,whereas for larger wavenumbers (smaller spatial scales),the spectrum becomes scale independent,with b g 0,thus meaning that the spatial variability of NDVI behaves irregularly,as white noise in space.Long-range correlations in the spatial distribution of water and energy-limited vegetation have been identified for the Columbia River basin in the USA (Milne et al.,2002).Analysis of the time evolution of monthly estimated values of scaling exponents,b (t ),and prefactors,c (t ),t =1,...,257,indicates a high negative correlation coefficient (À0.88,P N 0.95),as shown in Fig.3,which means that c (t )=f [b (t )],with f [d ]representing a linear function.This result points out to the existence of a strong association between these two parameters in power laws and scaling relationships;an idea that was introduced in the context of the Hurst effect in geophysical records (Mesa &Poveda,1993),which deserves further investigation.Furthermore,our results indicate that both parameters of the Fourier spectra vary with time,and thus E t (k )=c (t )k Àb (t ),where k denotes the wavenumber,and t represents the time index.Time series of monthly values of average precipitation and NDVI over the Amazon were estimated by averaging values of each field for a fixed month,as,h P t ðÞi ¼1=nXn i ¼1p i !t;and h NDVI t ðÞi ¼1=nX n i ¼1ndvi i!tð6Þwhere n denotes the number of pixels with information foreach field:12,991for precipitation,and 65,536for NDVI.Results show a significant negative correlation (À0.82,P N 0.95)between monthly values of average precipitation,h P (t )i over the Amazon and scaling exponents,b (t ),as illustrated in Fig.4.Such negative correlation indicates that wet months exhibit rougher (less spatially correlated)NDVI fields,which are encapsulated in lower values of b (t ).On the contrary,dry periods are associated with more coherent and longer-range correlated NDVI fields,which are reflected in higher values of b (t ).Accordingly,the afore-mentioned seasonal cycle of average precipitation,and the concomitant spatial variability of NDVI are also reflectedinFig.2.Bi-dimensional Fourier spectra of the NDVI field for September 1989,with scaling exponent,b =1.11,and prefactor,c =0.034.E (k )has arbitrary units,as NDVI is dimensionless.Values of NDVI are rescaled is such a manner that original data are recovered as ndvi=(NDVI À1)/249–0.05.The range of spatial scales covers from 512to 16km.The dotted line separates the two scaling regions of the spectrum at k =0.035km À1,which corresponds to a spatial scale of 28.6km.Fig.3.Time evolution of prefactors,c (t ),and scaling exponents,b (t ),for estimated bi-dimensional Fourier spectra of NDVI monthly fields,during the study period.Simultaneous correlation coefficient is À0.88(P N0.99).Fig. 4.Time evolution of monthly mean precipitation,h P (t )i ,over Amazonia and scaling exponents,b (t ).Simultaneous correlation is À0.82(P N 0.95).G.Poveda,L.F .Salazar /Remote Sensing of Environment 93(2004)391–401395a high positive correlation coefficient (0.72,P N 0.95)between the monthly series of average precipitation,h P (t )i and that of 6-month lagged scaling exponents,hb (t +6)i (not shown here).Despite that no significant correlation (À0.062)appears between the series of h P (t )i and h NDVI(t )i ,there is a significant positive correlation at 3-month lag (0.63,P N 0.95),see Fig.5,when precipitation leads NDVI.Such time lag suggests an integrated timescale at which rainfall affects NDVI dynamics at basin scale.The physical origin of this observation lies in the complex interactions of the land surface–atmosphere system,which include the afore-mentioned important effect of precipitation recycling in Amazonia (Salati,1985;Eltahir &Bras,1994).This observation deserves further investigation.3.1.1.Annual and interannual timescalesThe average long-term annual cycle of b (t )and c (t )were estimated from the 257estimated values (July 1981–November 2002).There is a strong negatively correlated seasonal cycle of prefactors,c (t ),and scaling exponents,b (t ),as evidenced in Fig.6.During the wet season (November–March),the estimated values of b (t )lie between 0.9and 1.15,while during the dry season (May–September),higher values are on the order of b (t )=1.30.These results are explained by the more coherent spatial effects of the dry season over the Amazon basin,which produce long-range spatial correlations in the NDVI field,reflected in higher estimates of b (t ).The annual cycle of prefactors,c (t ),exhibit higher values (~0.09–0.10)during the wet season,and lower values (~0.02–0.30)during the dry season.An understanding of the physical processes that govern such a strong association between scaling exponents and prefactors at seasonal scales is a topic of further research.At interannual timescales,ENSO strongly affects vege-tation activity and NDVI variability in Amazonia (Gutman,1991;Kogan &Sullivan,1993;Myneni et al.,1996;Asner et al.,2000;Poveda et al.,2001;Pinzo ´n,2002).Fig.7shows the annual cycle of scaling exponents,b (t ),during two contrasting ENSO years,i.e.,the 1991–1992El Nin ˜o,and the 1988–1989La Nin ˜a,as well as the average during normal years.The annual cycle is defined from June (year 0)through May (year +1),to better capture the aforementioned delayed effects of both ENSO phases.Overall,results indicate that the phase of the annual cycle of b (t )remains unchanged during both phases of ENSO,but there is a clear-cut effect on its amplitude.This is evidenced by higher values of b (t )during El Nin ˜o as compared with those during La Nin ˜a and normal years,throughout the annual cycle.Interestingly enough,the highest values of the scaling exponent are attained during August for both ENSO phases,and the lower values appears in February–March during El Nin ˜o,and in November–February during La Nin ˜a.This observation can be explained by the spatially coherent dryness caused over the Amazon by the warm phase of ENSO.It is well known that,in general,the Amazon basin experiences strong droughts during El Nin ˜o,and positive rainfall anomalies during La Nin ˜a (Richey et al.,1989;Fig.5.Time evolution of average values of monthly precipitation,h P (t )i ,and NDVI h NDVI(t )i ,over Amazonia.The caption of Fig.2explains the range of NDVI values.The low simultaneous correlation coefficient (À0.06)increases to À0.63(P N 0.95),when precipitation lead values of NDVI by 3months.Fig.6.Long-term annual cycle of estimated prefactors,c (t ),and scaling exponents,b (t ),from the estimated 2-D Fourierspectra.Fig.7.Annual cycle of scaling exponents,b (t ),during the 1991–1992El Nin ˜o event,the 1988–1989La Nin ˜a event,and during normal years.G.Poveda,L.F .Salazar /Remote Sensing of Environment 93(2004)391–401396Marengo,1992;Marengo &Hastenrath,1993;Obregon &Nobre,1990;Poveda &Mesa,1997;Poveda et al.,2001),whose effects are stronger in northern and central Amazonia(Marengo et al.,1998).The most remarkable differences occur in the November–March wet season during both phases of ENSO.During this epoch,both ENSO phases attain their maximum amplitude,and the associated tele-connections are more strongly developed,which in con-junction with land surface–atmosphere feedbacks cause stronger hydro-ecological anomalies,which affect the NDVI response over the Amazon.Our results confirm the coherent large spatial scale effects of El Nin ˜o-related drought over the Amazon basin,as a result of the 1991–1992event.It is concluded that scaling exponents,b (t ),exhibit significant variability at ENSO timescales,which are consistent with and reflect the identified hydrological anomalies.Similar to the temporal behavior of h NDVI(t )i ,results for the scaling exponents confirm that NDVI fields are more spatially correlated during El Nin ˜o than during La Nin ˜a.The interannual variability associated with both phases of ENSO is consistently exemplified by the evolution of b (t )and c (t )during the 1997–1998El Nin ˜o event,and during the 1998–2000La Nin ˜a event,shown in Fig.3.3.2.Moment-scaling analysisMoment-scale analysis were performed after checking for the condition that b (t )b 2(Harris et al.,2000).Fig.8shows the results for July 1991,with the scaling of marginal moments with q =0.5,1.0,...,4(top),and the estimated s ðq ˆÞcurve (bottom).Results indicate that monthly NDVI fields exhibit multi-scaling behavior in space,as indicated by the nonlinear behavior of the s ðq ˆÞfunction.Deviations from simple scaling were quantified as D q ¼s ðq ˆÞobserved Às q ðÞtheoretical ,e.g.,the difference between the sample values of the function s ðq ˆÞ,with respect to the linear growth for simple scaling (see Fig.9).Two features are worth mentioning:(i)there is a clear-cut annualandFig.8.Scaling of marginal moments with q =0.5,1.0,...,4(top),and estimated s ðq ˆÞcurve (bottom),for NDVI in July 1991.The straight continuous line and 95%confidence intervals (dashed)in the bottom pannel denote the theoretical behavior for simplescaling.Fig.9.Time evolution of departures from simple scaling,of the estimated s ðq ˆÞcurve,for q =1.5,...,4.0,during the study period.G.Poveda,L.F .Salazar /Remote Sensing of Environment 93(2004)391–401397。
频闪视觉训练在体育运动中的应用现状及发展趋势研究
第40卷第1期2021年2月福建体育科技Fujian Sports Science and TechnologyVol.40No.1February2021频闪视觉训练在体育运动中的应用现状及发展趋势研究张致玮,贾谊(中北大学体育学院,山西太原030051)摘要:频闪视觉训练是一种新型的训练形式,该训练模式由个体在间歇性视觉条件下进行训练,目的是在正常视觉条件下提高后续能力。
介绍了频闪视觉训练的相关原理,列举和说明了频闪视觉训练在体育运动中的应用现状和使用效果。
大量的研究表明频闪视觉训练对不同运动项目、不同领域、不同人群的好处,这对进一步推广频闪视觉训练具有重要意义。
最后,讨论了频闪视觉训练现有研究的不足和未来的发展趋势。
关键词:频闪视觉训练;运动;应用现状;发展趋势文章编号:1004-8790(2021)01-0043-04中图分类号:G808.12文献标识码:A Research on the Application Status and Development Trend of Stroboscopic Vision Training in SportsZHANG Zhi-wei,JIA Yi(School of Physical and Education,North University,Taiyuan030051,China)Abstract:Stroboscopic vision training is a new form of training,which is trained by individuals under intermittent vision condition to improve subsequent performance under normal vision condition.This paper introduces the relevant principles of stroboscopic vision training,enumerates and illustrates the application status and effect of stroboscopic vision training in sports.A large number of studies have shown the benefits of stroboscopic visual training for different sports,different fields and different populations,which is of great significance to further popularize stroboscopic visual training.Finally,the deficiencies of current studies and the future development trend of stroboscopic vision training are discussed.Keywords:stroboscopic vision training;movement;application status;development trend人类所获取的感官信息80%以上来自视觉。
MJO对我国天气气候影响的新事实
MJO对我国天气气候影响的新事实任宏利;沈雨旸【摘要】在次季节时间尺度上,热带大气季节内振荡(MJO)是全球气候变率的首要模态。
MJO不仅对热带天气气候产生直接影响,还能够通过传播和激发大气遥相关等方式对热带外地区产生重要影响,成为目前次季节—季节气候预测最重要的可预报性来源。
MJO对于我国天气气候影响的探索由来已久,在很多方面有了显著进展,但仍需深入研究。
首先对MJO影响我国天气气候的过往研究进行了回顾,并进一步利用新的观测资料诊断分析,发现了MJO对我国气候影响的一些新事实。
初步结论包括:MJO对我国冬季降水的影响主要局限在江南—华南区域,而夏季扩展到南方和青藏高原地区;对冬季气温的影响较大,其范围覆盖了东北、华北以及西部广大区域,而夏季解释方差有所减小,其区域位于除了黄河流域以外的广大地区;在去掉高频噪音后,纯粹MJO信号对我国主要区域气温和降水低频变化的解释方差可接近30%;MJO对我国冬夏季温度降水的影响存在明显滞后效应,应在使用MJO信号进行我国气象要素预报时加以考虑。
%Madden-Julian Oscillation (MJO) or more generally called tropical Intra-Seasonal Oscillation (ISO), dominates climate variability worldwide on the subseasonal timescale. It is well-known that MJO can impact weather and climate not only in tropics, but also in extratropics through the propagation and excitation of atmospheric teleconnections, and is the most important predictability source in the subseasonal-to-seasonal prediction. A great many studies have focused on impacts of MJO on weather and climate in China in the past decades, and made a signiifcant progress in different aspects. However, further deep understanding is stillneeded. This study firstly reviews previous studies of impacts of MJO on China weather and climate, and then shows the new results regarding such impacts of MJO, based on diagnoses of new observations, then gives preliminary conclusionsas followings:. the impacts of MJO on rainfall are mainly limited in the regions from south of Yangtze River to the South of China in winter, but are extended to the more southern and Tibetan Plateau areas in summer. It shows a signiifcant inlfuence of MJO on winter temperature over the Northeast of China, the North of China, and most of the western China, while a relatively weakened impact on summer temperature over the large areas in China except for the Yellow River Basin. After removing high-frequency noise, pure MJO signal can explain about 30% variance of low-frequency surface air temperature and rainfall in China. Moreover, it is found that there is a delayed impact of MJO on temperature and rainfall in both winter and summer in China and it is need to be consideredin the future predictions and futher studies .【期刊名称】《气象科技进展》【年(卷),期】2016(006)003【总页数】9页(P97-105)【关键词】MJO;中国气候;降水;气温;影响【作者】任宏利;沈雨旸【作者单位】国家气候中心,中国气象局气候研究开放实验室,中国气象局-南京大学气候预测研究联合实验室,北京 100081;中国气象局公共气象服务中心,北京 100081【正文语种】中文时间尺度介于10~90 d的大气季节内振荡(Intra-seasonal oscillation,ISO)是气候系统中最重要的大气环流现象之一。
高斯朴素贝叶斯训练集精确度的英语
高斯朴素贝叶斯训练集精确度的英语Gaussian Naive Bayes (GNB) is a popular machine learning algorithm used for classification tasks. It is particularly well-suited for text classification, spam filtering, and recommendation systems. However, like any other machine learning algorithm, GNB's performance heavily relies on the quality of the training data. In this essay, we will delve into the factors that affect the training set accuracy of Gaussian Naive Bayes and explore potential solutions to improve its performance.One of the key factors that influence the training set accuracy of GNB is the quality and quantity of the training data. In order for the algorithm to make accurate predictions, it needs to be trained on a diverse and representative dataset. If the training set is too small or biased, the model may not generalize well to new, unseen data. This can result in low training set accuracy and poor performance in real-world applications. Therefore, it is crucial to ensure that the training data is comprehensive and well-balanced across different classes.Another factor that can impact the training set accuracy of GNB is the presence of irrelevant or noisy features in the dataset. When the input features contain irrelevant information or noise, it can hinder the algorithm's ability to identify meaningful patterns and make accurate predictions. To address this issue, feature selection and feature engineering techniques can be employed to filter out irrelevant features and enhance the discriminative power of the model. Byselecting the most informative features and transforming them appropriately, we can improve the training set accuracy of GNB.Furthermore, the assumption of feature independence in Gaussian Naive Bayes can also affect its training set accuracy. Although the 'naive' assumption of feature independence simplifies the model and makes it computationally efficient, it may not hold true in real-world datasets where features are often correlated. When features are not independent, it can lead to biased probability estimates and suboptimal performance. To mitigate this issue, techniques such as feature extraction and dimensionality reduction can be employed to decorrelate the input features and improve the training set accuracy of GNB.In addition to the aforementioned factors, the choice of hyperparameters and model tuning can also impact the training set accuracy of GNB. Hyperparameters such as the smoothing parameter (alpha) and the covariance type in the Gaussian distribution can significantly influence the model's performance. Therefore, it is important to carefully tune these hyperparameters through cross-validation andgrid search to optimize the training set accuracy of GNB. By selecting the appropriate hyperparameters, we can ensure that the model is well-calibrated and achieves high accuracy on the training set.Despite the challenges and limitations associated with GNB, there are several strategies that can be employed to improve its training set accuracy. By curating a high-quality training dataset, performing feature selection and engineering, addressing feature independence assumptions, and tuning model hyperparameters, we can enhance the performance of GNB and achieve higher training set accuracy. Furthermore, it is important to continuously evaluate and validate the model on unseen data to ensure that it generalizes well and performs robustly in real-world scenarios. By addressing these factors and adopting best practices in model training and evaluation, we can maximize the training set accuracy of Gaussian Naive Bayes and unleash its full potential in various applications.。
选择与坚持:跨期选择与延迟满足之比较
第2期
任天虹等 : 选择与坚持:跨期选择与延迟满足之比较
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会通过预实验选择一对较为恰当的 SS 与 LL, 以 保证 SS 与 LL 之间的差异既要大到足以使被试愿 意选择后者 , 又要小到使 SS 对儿童有足够的诱惑 , 从而避免等待时间的天花板效应与地板效应 (Mischel & Underwood, 1974)。 动物实验在跨期选择与延迟满足的研究中都 有 所 涉 及 , 这 些动 物研 究 的 实验 范式 直 观 易 懂 , 对儿童被试的研究也颇有启示意义。随着研究内 容的扩展与研究范式的改进 , 跨期选择与延迟满 足在研究对象上有融合的趋势。 3.2 研究内容 跨期选择关注被试的时间折扣 (time discounting), 而延迟满足则更关注被试在等待时间上的 个 体 差 异 、 自 我 控 制 策 略 及 其 有 效 性 (Ainslie, 1975; Mischel et al., 1989)。如果说跨期选择的研 究者将其研究重点放在了计算、分析、推理、权 衡等较为高级的认知过程上 , 那么延迟满足的研 究者则将其重点放在了情绪、意志力、动机强度 等更为基础的本能反应上。 时间折扣是跨期选择研究的基本假设 , 也是 其研究的重要内容 , 它是指在跨期选择中 , 个体 首先会对延迟结果的价值根据其延迟的时间进行 一定的折扣, 然后再对两个结果进行比较(Frederick et al., 2002; Scholten & Read, 2010)。经济学家力 图找到某一通用的公式来描述折扣程度与结果及 延 迟 时 间 之间 的 关 系 , 表 现 为 数 理 模 型 的 优化 ; 心理学家则更关注外界因素对个体时间折扣程度 的影响 , 表现为认知神经机制的揭示。 延迟满足的研究者并不关注折扣程度 , 他们 更加关注等待时间上的个体差异 , 并在实验中细 致地检验被试自我控制策略的选择与使用状况 , 这些研究者热衷于以追踪研究揭示儿童在实验中 的表现与其个性及行为特征之间的关系 (Mischel et al., 1989)。 3.3 研究范式 跨期选择的研究关注被试的选择过程 , 它要 求被试做出一系列选择 ; 延迟满足则更关注被试 的坚持过程 , 要求被试坚持完成选择后的等待过 程。尽管在跨期选择的部分任务中也涉及到等待 过程 , 但是它与延迟满足有本质的区别。被试在 延迟满足任务中能够自主选择中止等待 , 而在跨 期选择任务中则只能消极等待延时结束 (Evans & Beran, 2007)。
Peters (2010) Episodic Future Thinking Reduces Reward Delay Discounting
NeuronArticleEpisodic Future Thinking ReducesReward Delay Discounting through an Enhancement of Prefrontal-Mediotemporal InteractionsJan Peters1,*and Christian Bu¨chel11NeuroimageNord,Department of Systems Neuroscience,University Medical Center Hamburg-Eppendorf,Hamburg20246,Germany*Correspondence:j.peters@uke.uni-hamburg.deDOI10.1016/j.neuron.2010.03.026SUMMARYHumans discount the value of future rewards over time.Here we show using functional magnetic reso-nance imaging(fMRI)and neural coupling analyses that episodic future thinking reduces the rate of delay discounting through a modulation of neural decision-making and episodic future thinking networks.In addition to a standard control condition,real subject-specific episodic event cues were presented during a delay discounting task.Spontaneous episodic imagery during cue processing predicted how much subjects changed their preferences toward more future-minded choice behavior.Neural valuation signals in the anterior cingulate cortex and functional coupling of this region with hippo-campus and amygdala predicted the degree to which future thinking modulated individual preference functions.A second experiment replicated the behavioral effects and ruled out alternative explana-tions such as date-based processing and temporal focus.The present data reveal a mechanism through which neural decision-making and prospection networks can interact to generate future-minded choice behavior.INTRODUCTIONThe consequences of choices are often delayed in time,and in many cases it pays off to wait.While agents normally prefer larger over smaller rewards,this situation changes when rewards are associated with costs,such as delays,uncertainties,or effort requirements.Agents integrate such costs into a value function in an individual manner.In the hyperbolic model of delay dis-counting(also referred to as intertemporal choice),for example, a subject-specific discount parameter accurately describes how individuals discount delayed rewards in value(Green and Myer-son,2004;Mazur,1987).Although the degree of delay discount-ing varies considerably between individuals,humans in general have a particularly pronounced ability to delay gratification, and many of our choices only pay off after months or even years. It has been speculated that the capacity for episodic future thought(also referred to as mental time travel or prospective thinking)(Bar,2009;Schacter et al.,2007;Szpunar et al.,2007) may underlie the human ability to make choices with high long-term benefits(Boyer,2008),yielding higher evolutionaryfitness of our species.At the neural level,a number of models have been proposed for intertemporal decision-making in humans.In the so-called b-d model(McClure et al.,2004,2007),a limbic system(b)is thought to place special weight on immediate rewards,whereas a more cognitive,prefrontal-cortex-based system(d)is more involved in patient choices.In an alternative model,the values of both immediate and delayed rewards are thought to be repre-sented in a unitary system encompassing medial prefrontal cortex(mPFC),posterior cingulate cortex(PCC),and ventral striatum(VS)(Kable and Glimcher,2007;Kable and Glimcher, 2010;Peters and Bu¨chel,2009).Finally,in the self-control model, values are assumed to be represented in structures such as the ventromedial prefrontal cortex(vmPFC)but are subject to top-down modulation by prefrontal control regions such as the lateral PFC(Figner et al.,2010;Hare et al.,2009).Both the b-d model and the self-control model predict that reduced impulsivity in in-tertemporal choice,induced for example by episodic future thought,would involve prefrontal cortex regions implicated in cognitive control,such as the lateral PFC or the anterior cingulate cortex(ACC).Lesion studies,on the other hand,also implicated medial temporal lobe regions in decision-making and delay discounting. In rodents,damage to the basolateral amygdala(BLA)increases delay discounting(Winstanley et al.,2004),effort discounting (Floresco and Ghods-Sharifi,2007;Ghods-Sharifiet al.,2009), and probability discounting(Ghods-Sharifiet al.,2009).Interac-tions between the ACC and the BLA in particular have been proposed to regulate behavior in order to allow organisms to overcome a variety of different decision costs,including delays (Floresco and Ghods-Sharifi,2007).In line with thesefindings, impairments in decision-making are also observed in humans with damage to the ACC or amygdala(Bechara et al.,1994, 1999;Manes et al.,2002;Naccache et al.,2005).Along similar lines,hippocampal damage affects decision-making.Disadvantageous choice behavior has recently been documented in patients suffering from amnesia due to hippo-campal lesions(Gupta et al.,2009),and rats with hippocampal damage show increased delay discounting(Cheung and Cardinal,2005;Mariano et al.,2009;Rawlins et al.,1985).These observations are of particular interest given that hippocampal138Neuron66,138–148,April15,2010ª2010Elsevier Inc.damage impairs the ability to imagine novel experiences (Hassa-bis et al.,2007).Based on this and a range of other studies,it has recently been proposed that hippocampus and parahippocam-pal cortex play a crucial role in the formation of vivid event repre-sentations,regardless of whether they lie in the past,present,or future (Schacter and Addis,2009).The hippocampus may thus contribute to decision-making through its role in self-projection into the future (Bar,2009;Schacter et al.,2007),allowing an organism to evaluate future payoffs through mental simulation (Johnson and Redish,2007;Johnson et al.,2007).Future thinking may thus affect intertemporal choice through hippo-campal involvement.Here we used model-based fMRI,analyses of functional coupling,and extensive behavioral procedures to investigate how episodic future thinking affects delay discounting.In Exper-iment 1,subjects performed a classical delay discounting task(Kable and Glimcher,2007;Peters and Bu¨chel,2009)that involved a series of choices between smaller immediate and larger delayed rewards,while brain activity was measured using fMRI.Critically,we introduced a novel episodic condition that involved the presentation of episodic cue words (tags )obtained during an extensive prescan interview,referring to real,subject-specific future events planned for the respective day of reward delivery.This design allowed us to assess individual discount rates separately for the two experimental conditions,allowing us to investigate neural mechanisms mediating changes in delay discounting associated with episodic thinking.In a second behavioral study,we replicated the behavioral effects of Exper-iment 1and addressed a number of alternative explanations for the observed effects of episodic tags on discount rates.RESULTSExperiment 1:Prescan InterviewOn day 1,healthy young volunteers (n =30,mean age =25,15male)completed a computer-based delay discounting proce-dure to estimate their individual discount rate (Peters and Bu ¨-chel,2009).This discount rate was used solely for the purpose of constructing subject-specific trials for the fMRI session (see Experimental Procedures ).Furthermore,participants compiled a list of events that they had planned in the next 7months (e.g.,vacations,weddings,parties,courses,and so forth)andrated them on scales from 1to 6with respect to personal rele-vance,arousal,and valence.For each participant,seven subject-specific events were selected such that the spacing between events increased with increasing delay to the episode,and that events were roughly matched based on personal rele-vance,arousal,and valence.Multiple regression analysis of these ratings across the different delays showed no linear effects (relevance:p =0.867,arousal:p =0.120,valence:p =0.977,see Figure S1available online).For each subject,a separate set of seven delays was computed that was later used as delays in the control condition.Median and range for the delays used in each condition are listed in Table S1(available online).For each event,a label was selected that would serve as a verbal tag for the fMRI session.Experiment 1:fMRI Behavioral ResultsOn day 2,volunteers performed two sessions of a delay dis-counting procedure while fMRI was measured using a 3T Siemens Scanner with a 32-channel head-coil.In each session,subjects made a total of 118choices between 20V available immediately and larger but delayed amounts.Subjects were told that one of their choices would be randomly selected and paid out following scanning,with the respective delay.Critically,in half the trials,an additional subject-specific episodic tag (see above,e.g.,‘‘vacation paris’’or ‘‘birthday john’’)was displayed based on the prescan interview (see Figure 1)indicating which event they had planned on the particular day (episodic condi-tion),whereas in the remaining trials,no episodic tag was pre-sented (control condition).Amount and waiting time were thus displayed in both conditions,but only the episodic condition involved the presentation of an additional subject-specific event tag.Importantly,nonoverlapping sets of delays were used in the two conditions.Following scanning,subjects rated for each episodic tag how often it evoked episodic associations during scanning (frequency of associations:1,never;to 6,always)and how vivid these associations were (vividness of associa-tions:1,not vivid at all;to 6,highly vivid;see Figure S1).Addition-ally,written reports were obtained (see Supplemental Informa-tion ).Multiple regression revealed no significant linear effects of delay on postscan ratings (frequency:p =0.224,vividness:p =0.770).We averaged the postscan ratings acrosseventsFigure 1.Behavioral TaskDuring fMRI,subjects made repeated choices between a fixed immediate reward of 20V and larger but delayed amounts.In the control condi-tion,amounts were paired with a waiting time only,whereas in the episodic condition,amounts were paired with a waiting time and a subject-specific verbal episodic tag indicating to the subjects which event they had planned at the respective day of reward delivery.Events were real and collected in a separate testing session prior to the day of scanning.NeuronEpisodic Modulation of Delay DiscountingNeuron 66,138–148,April 15,2010ª2010Elsevier Inc.139and the frequency/vividness dimensions,yielding an‘‘imagery score’’for each subject.Individual participants’choice data from the fMRI session were then analyzed byfitting hyperbolic discount functions to subject-specific indifference points to obtain discount rates (k-parameters),separately for the episodic and control condi-tions(see Experimental Procedures).Subjective preferences were well-characterized by hyperbolic functions(median R2 episodic condition=0.81,control condition=0.85).Discount functions of four exemplary subjects are shown in Figure2A. For both conditions,considerable variability in the discount rate was observed(median[range]of discount rates:control condition=0.014[0.003–0.19],episodic condition=0.013 [0.002–0.18]).To account for the skewed distribution of discount rates,all further analyses were conducted on the log-trans-formed k-parameters.Across subjects,log-transformed discount rates were significantly lower in the episodic condition compared with the control condition(t(29)=2.27,p=0.016),indi-cating that participants’choice behavior was less impulsive in the episodic condition.The difference in log-discount rates between conditions is henceforth referred to as the episodic tag effect.Fitting hyperbolic functions to the median indifference points across subjects also showed reduced discounting in the episodic condition(discount rate control condition=0.0099, episodic condition=0.0077).The size of the tag effect was not related to the discount rate in the control condition(p=0.56). We next hypothesized that the tag effect would be positively correlated with postscan ratings of episodic thought(imagery scores,see above).Robust regression revealed an increase in the size of the tag effect with increasing imagery scores (t=2.08,p=0.023,see Figure2B),suggesting that the effect of the tags on preferences was stronger the more vividly subjects imagined the episodes.Examples of written postscan reports are provided in the Supplemental Results for participants from the entire range of imagination ratings.We also correlated the tag effect with standard neuropsychological measures,the Sensation Seeking Scale(SSS)V(Beauducel et al.,2003;Zuck-erman,1996)and the Behavioral Inhibition Scale/Behavioral Approach Scale(BIS/BAS)(Carver and White,1994).The tag effect was positively correlated with the experience-seeking subscale of the SSS(p=0.026)and inversely correlated with the reward-responsiveness subscale of the BIS/BAS scales (p<0.005).Repeated-measures ANOVA of reaction times(RTs)as a func-tion of option value(lower,similar,or higher relative to the refer-ence option;see Experimental Procedures and Figure2C)did not show a main effect of condition(p=0.712)or a condition 3value interaction(p=0.220),but revealed a main effect of value(F(1.8,53.9)=16.740,p<0.001).Post hoc comparisons revealed faster RTs for higher-valued options relative to similarly (p=0.002)or lower valued options(p<0.001)but no difference between lower and similarly valued options(p=0.081).FMRI DataFMRI data were modeled using the general linear model(GLM) as implemented in SPM5.Subjective value of each decision option was calculated by multiplying the objective amount of each delayed reward with the discount fraction estimated behaviorally based on the choices during scanning,and included as a parametric regressor in the GLM.Note that discount rates were estimated separately for the control and episodic conditions(see above and Figure2),and we thus used condition-specific k-parameters for calculation of the subjective value regressor.Additional parametric regressors for inverse delay-to-reward and absolute reward magnitude, orthogonalized with respect to subjective value,were included in theGLM.Figure2.Behavioral Data from Experiment1Shown are experimentally derived discount func-tions from the fMRI session for four exemplaryparticipants(A),correlation with imagery scores(B),and reaction times(RTs)(C).(A)Hyperbolicfunctions werefit to the indifference points sepa-rately for the control(dashed lines)and episodic(solid lines,filled circles)conditions,and thebest-fitting k-parameters(discount rates)and R2values are shown for each subject.The log-trans-formed difference between discount rates wastaken as a measure of the effect of the episodictags on choice preferences.(B)Robust regressionrevealed an association between log-differences indiscount rates and imagery scores obtained frompostscan ratings(see text).(C)RTs were signifi-cantly modulated by option value(main effectvalue p<0.001)with faster responses in trialswith a value of the delayed reward higher thanthe20V reference amount.Note that althoughseven delays were used for each condition,somedata points are missing,e.g.,onlyfive delay indif-ference points for the episodic condition areplotted for sub20.This indicates that,for the twolongest delays,this subject never chose the de-layed reward.***p<0.005.Error bars=SEM.Neuron Episodic Modulation of Delay Discounting140Neuron66,138–148,April15,2010ª2010Elsevier Inc.Episodic Tags Activate the Future Thinking NetworkWe first analyzed differences in the condition regressors without parametric pared to those of the control condi-tion,BOLD responses to the presentation of the delayed reward in the episodic condition yielded highly significant activations (corrected for whole-brain volume)in an extensive network of brain regions previously implicated in episodic future thinking (Addis et al.,2007;Schacter et al.,2007;Szpunar et al.,2007)(see Figure 3and Table S2),including retrosplenial cortex (RSC)/PCC (peak MNI coordinates:À6,À54,14,peak z value =6.26),left lateral parietal cortex (LPC,À44,À66,32,z value =5.35),and vmPFC (À8,34,À12,z value =5.50).Distributed Neural Coding of Subjective ValueWe then replicated previous findings (Kable and Glimcher,2007;Kable and Glimcher,2010;Peters and Bu¨chel,2009)using a conjunction analysis (Nichols et al.,2005)searching for regions showing a positive correlation between the height of the BOLD response and subjective value in the control and episodic condi-tions in a parametric analysis (Figure 4A and Table S3).Note that this is a conservative analysis that requires that a given voxel exceed the statistical threshold in both contrasts separately.This analysis revealed clusters in the lateral orbitofrontal cortex (OFC,À36,50,À10,z value =4.50)and central OFC (À18,12,À14,z value =4.05),bilateral VS (right:10,8,0,z value =4.22;left:À10,8,À6,z value =3.51),mPFC (6,26,16,z value =3.72),and PCC (À2,À28,24,z value =4.09),representing subjective (discounted)value in both conditions.We next analyzed the neural tag effect,i.e.,regions in which the subjective value correlation was greater for the episodic condi-tion as compared with the control condition (Figure 4B and Table S4).This analysis revealed clusters in the left LPC (À66,À42,32,z value =4.96,),ACC (À2,16,36,z value =4.76),left dorsolateral prefrontal cortex (DLPFC,À38,36,36,z value =4.81),and right amygdala (24,2,À24,z value =3.75).Finally,we performed a triple-conjunction analysis,testing for regions that were correlated with subjective value in both conditions,but in which the value correlation increased in the episodic condition.Only left LPC showed this pattern (À66,À42,30,z value =3.55,see Figure 4C and Table S5),the same region that we previously identified as delay-specific in valuation (Petersand Bu¨chel,2009).There were no regions in which the subjective value correlation was greater in the control condition when compared with the episodic condition at p <0.001uncorrected.ACC Valuation Signals and Functional Connectivity Predict Interindividual Differences in Discount Function ShiftsWe next correlated differences in the neural tag effect with inter-individual differences in the size of the behavioral tag effect.To this end,we performed a simple regression analysis in SPM5on the single-subject contrast images of the neural tag effect (i.e.,subjective value correlation episodic >control)using the behavioral tag effect [log(k control )–log(k episodic )]as an explana-tory variable.This analysis revealed clusters in the bilateral ACC (right:18,34,18,z value =3.95,p =0.021corrected,left:À20,34,20,z value =3.52,Figure 5,see Table S6for a complete list).Coronal sections (Figure 5C)clearly show that both ACC clusters are located in gray matter of the cingulate sulcus.Because ACC-limbic interactions have previously been impli-cated in the control of choice behavior (Floresco and Ghods-Sharifi,2007;Roiser et al.,2009),we next analyzed functional coupling with the right ACC from the above regression contrast (coordinates 18,34,18,see Figure 6A)using a psychophysiolog-ical interaction analysis (PPI)(Friston et al.,1997).Note that this analysis was conducted on a separate first-level GLM in which control and episodic trials were modeled as 10s miniblocks (see Experimental Procedures for details).We first identified regions in which coupling with the ACC changed in the episodic condition compared with the control condition (see Table S7)and then performed a simple regression analysis on these coupling parameters using the behavioral tag effect as an explanatory variable.The tag effect was associated with increased coupling between ACC and hippocampus (À32,À18,À16,z value =3.18,p =0.031corrected,Figure 6B)and ACC and left amygdala (À26,À4,À26,z value =2.95,p =0.051corrected,Figure 6B,see Table S8for a complete list of activa-tions).The same regression analysis in a second PPI with the seed voxel placed in the contralateral ACC region from the same regression contrast (À20,34,22,see above)yielded qual-itatively similar,though subthreshold,results in these same structures (hippocampus:À28,À32,À6,z value =1.96,amyg-dala:À28,À6,À16,z value =1.97).Experiment 2We conducted an additional behavioral experiment to address a number of alternative explanations for the observed effects of tags on choice behavior.First,it could be argued thatepisodicFigure 3.Categorical Effect of Episodic Tags on Brain ActivityGreater activity in lateral parietal cortex (left)and posterior cingulate/retrosplenial and ventro-medial prefrontal cortex (right)was observed in the episodic condition compared with the control condition.p <0.05,FWE-corrected for whole-brain volume.NeuronEpisodic Modulation of Delay DiscountingNeuron 66,138–148,April 15,2010ª2010Elsevier Inc.141tags increase subjective certainty that a reward would be forth-coming.In Experiment 2,we therefore collected postscan ratings of reward confidence.Second,it could be argued that events,always being associated with a particular date,may have shifted temporal focus from delay-based to more date-based processing.This would represent a potential confound,because date-associated rewards are discounted less than delay-associated rewards (Read et al.,2005).We therefore now collected postscan ratings of temporal focus (date-based versus delay-based).Finally,Experiment 1left open the question of whether the tag effect depends on the temporal specificity of the episodic cues.We therefore introduced an additional exper-imental condition that involved the presentation of subject-specific temporally unspecific future event cues.These tags (henceforth referred to as unspecific tags)were obtained by asking subjects to imagine events that could realistically happen to them in the next couple of months,but that were not directly tied to a particular point in time (see Experimental Procedures ).Episodic Imagery,Not Temporal Specificity,Reward Confidence,or Temporal Focus,Predicts the Size of the Tag EffectIn total,data from 16participants (9female)are included.Anal-ysis of pretest ratings confirmed that temporally unspecific and specific tags were matched in terms of personal relevance,arousal,valence,and preexisting associations (all p >0.15).Choice preferences were again well described by hyperbolic functions (median R 2control =0.84,unspecific =0.81,specific =0.80).We replicated the parametric tag effect (i.e.,increasing effect of tags on discount rates with increasing posttest imagery scores)in this independent sample for both temporally specific (p =0.047,Figure 7A)and temporally unspecific (p =0.022,Figure 7A)tags,showing that the effect depends on future thinking,rather than being specifically tied to the temporal spec-ificity of the event cues.Following testing,subjects rated how certain they were that a particular reward would actually be forth-coming.Overall,confidence in the payment procedure washighFigure 4.Neural Representation of Subjective Value (Parametric Analysis)(A)Regions in which the correlation with subjective value (parametric analysis)was significant in both the control and the episodic conditions (conjunction analysis)included central and lateral orbitofrontal cortex (OFC),bilateral ventral striatum (VS),medial prefrontal cortex (mPFC),and posterior cingulate cortex(PCC),replicating previous studies (Kable and Glimcher,2007;Peters and Bu¨chel,2009).(B)Regions in which the subjective value correlation was greater for the episodic compared with the control condition included lateral parietal cortex (LPC),ante-rior cingulate cortex (ACC),dorsolateral prefrontal cortex (DLPFC),and the right amygdala (Amy).(C)A conjunction analysis revealed that only LPC activity was positively correlated with subjective value in both conditions,but showed a greater regression slope in the episodic condition.No regions showed a better correlation with subjective value in the control condition.Error bars =SEM.All peaks are significant at p <0.001,uncorrected;(A)and (B)are thresholded at p <0.001uncorrected and (C)is thresholded at p <0.005,uncorrected for display purposes.NeuronEpisodic Modulation of Delay Discounting142Neuron 66,138–148,April 15,2010ª2010Elsevier Inc.(Figure 7B),and neither unspecific nor specific tags altered these subjective certainty estimates (one-way ANOVA:F (2,45)=0.113,p =0.894).Subjects also rated their temporal focus as either delay-based or date-based (see Experimental Procedures ),i.e.,whether they based their decisions on the delay-to-reward that was actually displayed,or whether they attempted to convert delays into the corresponding dates and then made their choices based on these dates.There was no overall significant effect of condition on temporal focus (one-way ANOVA:F (2,45)=1.485,p =0.237,Figure 7C),but a direct comparison between the control and the temporally specific condition showed a significant difference (t (15)=3.18,p =0.006).We there-fore correlated the differences in temporal focus ratings between conditions (control:unspecific and control:specific)with the respective tag effects (Figure 7D).There were no correlations (unspecific:p =0.71,specific:p =0.94),suggesting that the observed differences in discounting cannot be attributed to differences in temporal focus.High-Imagery,but Not Low-Imagery,Subjects Adjust Their Discount Function in an Episodic ContextFor a final analysis,we pooled the samples of Experiments 1and 2(n =46subjects in total),using only the temporally specific tag data from Experiment 2.We performed a median split into low-and high-imagery participants according to posttest imagery scores (low-imagery subjects:n =23[15/8Exp1/Exp2],imagery range =1.5–3.4,high-imagery subjects:n =23[15/8Exp1/Exp2],imagery range =3.5–5).The tag effect was significantly greater than 0in the high-imagery group (t (22)=2.6,p =0.0085,see Figure 7D),where subjects reduced their discount rate by onaverage 16%in the presence of episodic tags.In the low-imagery group,on the other hand,the tag effect was not different from zero (t (22)=0.573,p =0.286),yielding a significant group difference (t (44)=2.40,p =0.011).DISCUSSIONWe investigated the interactions between episodic future thought and intertemporal decision-making using behavioral testing and fMRI.Experiment 1shows that reward delay dis-counting is modulated by episodic future event cues,and the extent of this modulation is predicted by the degree of sponta-neous episodic imagery during decision-making,an effect that we replicated in Experiment 2(episodic tag effect).The neuroi-maging data (Experiment 1)highlight two mechanisms that support this effect:(1)valuation signals in the lateral ACC and (2)neural coupling between ACC and hippocampus/amygdala,both predicting the size of the tag effect.The size of the tag effect was directly related to posttest imagery scores,strongly suggesting that future thinking signifi-cantly contributed to this effect.Pooling subjects across both experiments revealed that high-imagery subjects reduced their discount rate by on average 16%in the episodic condition,whereas low-imagery subjects did not.Experiment 2addressed a number of alternative accounts for this effect.First,reward confidence was comparable for all conditions,arguing against the possibility that the tags may have somehow altered subjec-tive certainty that a reward would be forthcoming.Second,differences in temporal focus between conditions(date-basedFigure 5.Correlation between the Neural and Behavioral Tag Effect(A)Glass brain and (B and C)anatomical projection of the correlation between the neural tag effect (subjective value correlation episodic >control)and the behav-ioral tag effect (log difference between discount rates)in the bilateral ACC (p =0.021,FWE-corrected across an anatomical mask of bilateral ACC).(C)Coronal sections of the same contrast at a liberal threshold of p <0.01show that both left and right ACC clusters encompass gray matter of the cingulate gyrus.(D)Scatter-plot depicting the linear relationship between the neural and the behavioral tag effect in the right ACC.(A)and (B)are thresholded at p <0.001with 10contiguous voxels,whereas (C)is thresholded at p <0.01with 10contiguousvoxels.Figure 6.Results of the Psychophysiolog-ical Interaction Analysis(A)The seed for the psychophysiological interac-tion (PPI)analysis was placed in the right ACC (18,34,18).(B)The tag effect was associated with increased ACC-hippocampal coupling (p =0.031,corrected across bilateral hippocampus)and ACC-amyg-dala coupling (p =0.051,corrected across bilateral amygdala).Maps are thresholded at p <0.005,uncorrected for display purposes and projected onto the mean structural scan of all participants;HC,hippocampus;Amy,Amygdala;rACC,right anterior cingulate cortex.NeuronEpisodic Modulation of Delay DiscountingNeuron 66,138–148,April 15,2010ª2010Elsevier Inc.143。
湍流燃烧模型
Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 195 2. Balance equations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 196
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D. Veynante, L. Vervisch / Progress in Energy and Combustion Science 28 (2002) 193±266
6. Tools for turbulent combustion modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 212 6.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 212 6.2. Scalar dissipation rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 214 6.3. Geometrical description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 214 6.3.1. G-®eld equation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 214 6.3.2. Flame surface density description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 216 6.3.3. Flame wrinkling description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 218 6.4. Statistical approaches: probability density function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 218 6.4.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 218 6.4.2. Presumed probability density functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 219 6.4.3. Pdf balance equation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 219 6.4.4. Joint velocity/concentrations pdf . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 221 6.4.5. Conditional moment closure (CMC) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 221 6.5. Similarities and links between the tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 221
刘萍萍翻译
定义色的色域边界的测试目标Phil Green彩色影像集团LCP的,英国伦敦2000年12月1.简介在生产中的一个色域是颜色可以在其上复制的范围。
这个范围将取决于许多因素,其中最重要的是介质本身的物理性能和它们一起使用的着色剂。
其他因素包括介质存在的条件,半色调或采纳抖动的方法,和在渲染过程中的任何特性或限制,如固体密度或墨水限制。
在一些行业如报纸的生产,这些因素很多是有一定程度的标准化,并有可能定义一个色域将适用(有一些变化)跨越大部分产品印刷的过程。
虽然色域通常代表两个方面(如xy色度或CIELAB的a* / b *值),这可能会引起误解的原因有两个。
首先,它忽略了亮度范围的可复制(也称为动态范围),这是色域的一个重要方面;第二,通过忽略亮度尺寸可能会出现一个给定的色域里面的颜色,而事实上并非如此。
对于硬拷贝的媒体,如着色剂的组合并不按照简单的加法原则,色域在色彩空间如CIELAB空间中是一种不规则的固体。
在CIELAB中比较不同介质的色域,我们注意到一些非常大的差异,也就是说,摄影逆转材料,而中冷置新闻纸用于重现。
可能对色域最明显有用的信息是援助在原始和繁殖媒体之间的映射颜色的过程。
色域映射算法使用双方媒体的色域边界,来确定需要多少压缩,使比较大的色域适合较小的色域。
这通常需要找到与色域边界一行的交集,通过要映射的点从收敛域边界点(通常在位于轴色差)找。
在最近的一项色域映射模型研究中,来源于高品质的复制品经验发现与经验数据拟合的模型可以通过由包括线性插值从模型中的色域边界形状的'尖'(在给定色相角度最大色度)来改善。
1.1色域边界计算色域边界的许多方法被描述在[1,2,3,4,5,6]。
这些方法的主要特点由Morovic[6进行了总结]。
不同的媒体和图像的色域也已在最近文件[7,8]中进行了比较。
在着色剂空间,色域边界可以被视为一个立方体因为在顶点它有固体着色剂和他们的第二组合的面孔。
A survey on modeling and simulation of vehicular networks_ Communications, mobility, and tools
A survey on modeling and simulation of vehicular networks:Communications,mobility,andtoolsFrancisco J.Ros ⇑,Juan A.Martinez,Pedro M.RuizDepartment of Information and Communications Engineering,University of Murcia,Murcia,Spaina r t i c l e i n f o Article history:Available online 15February 2014Keywords:Vehicular networks Mobility modelCommunication model Simulationa b s t r a c tSimulation is a key tool for the design and evaluation of Intelligent Transport Systems (ITS)that take advantage of communication-capable vehicles in order to provide valuable safety,traffic management,and infotainment services.It is widely recognized that simulation results are only significant when real-istic models are considered within the simulation toolchain.However,quite often research works on the subject are based on simplistic models unable to capture the unique characteristics of vehicular commu-nication networks.If the implications of the assumptions made by the chosen models are not well under-stood,incorrect interpretations of simulation results will follow.In this paper,we survey the most significant simulation models for wireless signal propagation,dedicated short-range communication technologies,and vehicular mobility.The support that different simulation tools offer for such models is discussed,as well as the steps that must be undertaken to fine-tune the model parameters in order to gather realistic results.Moreover,we provide handy hints and references to help determine the most appropriate tools and models.We hope this article to help prospective collaborative ITS researchers and promote best simulation practices in order to obtain accurate results.Ó2014Elsevier B.V.All rights reserved.1.Introduction and motivationIn the last years,the development of collaborative Intelligent Transport Systems (ITS)has been a focus of deep muni-cation-capable vehicles enable a plethora of valuable services tar-geted at improving road safety,alleviating traffic congestion,and enhancing the overall driving experience [1].To support these ser-vices,different standardization bodies have defined the network-ing architecture of ITS stations [2,3],including vehicles’on-board units (OBU)and infrastructure’s road-side units (RSU).Multiple network interface cards of different communication technologies coexist within a same OBU or RSU to support different use cases.Thus,cellular or broadband wireless interfaces provide the vehicle with connectivity to the infrastructure network (V2I),while dedicated short-range communications (DSRC)in the 5.9GHz frequency band allow for vehicle-to-vehicle (V2V)and vehicle-to-roadside (V2R)data transfers.In these cases,vehicles form a vehicular ad hoc network (VANET)in which collaborative services can be deployed.The design and evaluation of ITS services and communication protocols is cumbersome,given the scale of vehicular networks and their unique characteristics.Some small-scale testbeds have been deployed [4,5]as a proof of concept,but results from small experiments cannot be extrapolated to real networks.In very few cases,field operational tests (FOT)have been implemented to eval-uate an ITS platform under real traffic conditions [6].However,gi-ven the high amount of required resources to deploy a FOT,it is only an option for a limited number of researchers and practitio-ners on the field.As an alternative,simulation models feature a good trade-off between the realism of results and the flexibility of target networks under study.Not surprisingly,most research on VANET and collaborative ITS rely on simulation as the main tool for design and evaluation.Different fields stitch together in the development of collabora-tive ITS,including wireless communications and civil traffic engi-neering.In order to gather significant simulation results,a good understanding of the different models involved is required.It is widely recognized that simplistic wireless communication models lead to unreasonable results that do not match reality [7].In addi-tion,vehicular mobility patterns greatly differ from other network-ing scenarios and they need specific models to capture the characteristics of vehicles’movements.Different mobility patterns have a distinct impact onto simulation results [8,9].Therefore,an ITS researcher must be aware of the different models that can be/10.1016/com.2014.01.0100140-3664/Ó2014Elsevier B.V.All rights reserved.⇑Corresponding author.Tel.:+34868884644;fax:+34868884151.E-mail address:fjros@um.es (F.J.Ros).employed for each aspect of the simulation environment and how model parameters should be tuned to obtain realistic results.While,traditionally,mobility and network simulators have been developed by different communities for different end users, both tools merge in collaborative ITS.It is of paramount impor-tance that simulations account for realistic vehicles’movements as generated by mobility simulators.Then,network simulators must provide realistic simulation of communication models as the vehicles move according to a given traffic pattern.In some cases,an ITS application influences the mobility of vehicles.For in-stance,a traffic management service might indicate some vehicles to follow an alternative route to avoid a congested road.To support such scenarios,integrated mobility and network simulations are necessary and can be provided by interfacing existing tools or developing new ones.In this paper,we survey the most significant approaches for wireless modeling and mobility modeling in vehicular networks. Specifically,we describe the models that have been often em-ployed to characterize wireless signal propagation(path loss and fading)in the absence or presence of obstacles,including the sup-port provided in available simulation mon configura-tions of models parameters for both highway and urban environments are provided when applicable.In addition,we cover the simulation models which are available for the simulation of IEEE802.11p DSRC and related standards.Differences among them are outlined.Regarding vehicular mobility,we briefly review some of the many models that have been proposed for decades and pro-vide references forfine-tune calibration of model parameters when high mobility accuracy is required.Furthermore,we summarize the main features of common mobility simulation tools and de-scribe the process to obtain a realistic vehicular scenario.Finally, we discuss the available options for performing integrated mobil-ity and network simulations.We hope this work to help prospec-tive collaborative ITS researchers and promote best simulation practices in order to obtain accurate results.The reminder of this paper is organized as follows.Section2is focused on wireless signals modeling and simulation tools in the context of vehicular networks.Simulation models for DSRC tech-nologies are reviewed in Section3.In Section4,we survey some significant vehicular mobility models and different traffic simula-tion packages.The steps to set up a realistic vehicular scenario are also discussed.Section5deals with coupled network and mobility simulations to account for ITS services that influence the behavior of trafficflows.Finally,Section6concludes this article.2.Vehicular wireless communicationUnderstanding the implications of our chosen communication models is key to design appropriate simulation experiments and get insight from their results.In this section we review the most relevant models for vehicular communication systems,as well as the different tools that support them.We highlight the main take-aways that a prospective ITS researcher must keep in mind when conducting a simulation-based study.In addition,valuable information and handy references to help design experiments with different degrees of realism are provided.2.1.Modeling of wireless linksWhen modeling a wireless ad hoc network,one of thefirst questions we have to answer is when we can state that a node1u is able to communicate with another node v.In such case,we say that a link exists from u to v.2The simplest approach to model a wireless link is derived from a Uniform Disk Graph(UDG).All nodes are assumed to feature a com-munication range of radius r.In this way,a bidirectional link be-tween u and v exists if and only if j u;v j6r,where jÁj denotes the Euclidean distance.Note that a UDG represents an ideal net-work in the sense that perfect communication occurs up to r dis-tance units from the source.This model does not have into account reception errors which might be provoked by radio inter-ferences.It has been often employed in the literature since it pro-vides a rough estimation of network connectivity in a simple way. However,it is well known that real wireless links do not follow this ideal model at all[11].In order to capture the characteristics of realistic wireless links, signal propagation must be accurately defined(Section2.2).This determines how signal power dissipates as a function of the dis-tance.In the absence of interferences,the receiver will be able to decode the wireless signal,and therefore reconstruct the original message,whenever the signal to noise ratio(SNR)satisfies the fol-lowing condition:SNR¼SNP b;where S is the received signal power,N is the noise power,and b is a threshold dependent on the sensitivity of the wireless decoder. Noise represents the undesired random disturbance of a useful information signal.Since wireless medium is shared by the nodes in an ad hoc net-work,transmissions from a node interfere with concurrent com-munications between different nodes.This may cause great disturbance in the resulting signal,so that receivers would not be able to decode the message.In such case,we say that a collision has occurred.Thus,in the most general case,correct reception of a message by a node must satisfy that the signal to interference-noise ratio(SINR)holds the following requirement:SINR¼SIþNP b;where I is the cumulative power of interfering signals.Next we de-scribe some of the commonly employed propagation models for wireless signals,so that the SINR for a given receiver can be computed.2.2.Modeling of wireless signal propagationAs we have seen in the previous subsection,the signal strength at the receiver is lower than when it leaves the transmitter.Several factors contribute to this phenomenon,such as the natural power dissipation as the signal expands,the presence of obstacles which reflect,diffract and scatter the original signal,and the existence of multiple paths which may lead to signal cancellation at the recei-ver.The mean signal strength at the receiver as a function of the distance from the transmitter can be estimated by large-scale prop-agation models,while rapidfluctuations of the signal at the wave-length scale is better represented by small-scale fading models.The following subsections briefly review the most representative mod-els that are relevant to vehicular wireless communications.They can be classified according to different criteria[12].In Fig.1,we distinguish among(i)deterministic vs stochastic models,(ii) large-scale path loss vs small-scale fading models,and(iii)whether obstacles(surrounding buildings,the vehicles themselves)are1Throughout this paper,‘node’can be exchanged with‘vehicle’or‘RSU’.2We assume that nodes employ omni-directional antennas.This is the most common scenario,although works on vehicular ad hoc networks with directional antennas have also been undertaken[10].2 F.J.Ros et al./Computer Communications43(2014)1–15explicitly accounted for or not.At the expense of higher computa-tion cost,increased realism is achieved by considering more characteristics of the wireless channel.In order to achieve this, some models build upon simpler ones to provide a more realistic framework for vehicular communications.For a deeper coverage of large-scale and small-scale fading models in general,please refer to[13].rge-scale path lossGiven the transmission power P t,large-scale models predict the received signal power P r as a function of the distance d between transmitter and receiver.The attenuation of the signal strength at the receiver with respect to the transmitter is called the path loss (PL).Regardless the propagation model we employ to obtain P r,the path loss can be computed(in dB)as in the following expression:PLðdÞ¼10log10P t P rThe Friis’free space propagation model[14]assumes the ideal condition that there is just one clear line-of-sight(LOS)path be-tween sender and receiver.If we consider isotropic antennas and nodes located in a plane,this model represents the communication range as a circle around the transmitter.Friis proposed the follow-ing expression to compute P r:P rðdÞ¼P t G t G r k2ð4pÞ2d2L;ð1Þwhere G t and G r are the antenna gains of the transmitter and recei-ver respectively,L P1is the system loss(because of electronic cir-cuitry),k is the signal wavelength,and P r is measured in the same units as P t(usually W or mW).As you can see,signal strength de-cays as a quadratic power law of the distance.In typical communications within a vehicular network,ideal conditions to apply the Friis model are rarely achieved.For in-stance,signal reflection is not being considered.The two-ray ground reflection model[13]provides more accurate predictions by explicitly accounting for both the direct path between sender and receiver and the ground-reflected path.For large distances, the following expression estimates the received signal strength: P rðdÞ¼P t G t G r h2th2rd4L;ð2Þwhere h t and h r are the heights of the transmit and receive anten-nas,respectively,and P r is measured in the same units as P t(usually W or mW).Note that according to Eq.(2),when transmitter and receiver are far away(d)ffiffiffiffiffiffiffiffiffih t h rp)the power decays with the distance raised to the fourth power(much faster fall than in free space). In addition,the path loss becomes independent of the signal fre-quency.The former conclusions do not hold for a short distance d,in which case a different expression must be used to compute P r.A common model employed in such cases is the Friis’free space propagation.For instance,such approach can be found in both the ns-2and ns-3network simulators.Thus,a cross-over distance d c is calculated:when d<d c,Eq.(1)is employed;when d>d c,Eq.(2)is used;at the cross-over distance d c,Friis and two-ray ground mod-els provide the same result.Therefore,d c can be computed as follows:d c¼4p h t h rkNote that for the5.9GHz DSRC band for vehicular communica-tions,and assuming that antennas are mounted on cars’roofs at1.5 meters high,d c%556m.This means that if you are employing the two-ray ground model in an ns-2or ns-3simulation of a IEEE WAVE/802.11p vehicular ad hoc network,communications be-tween vehicles far away less than556m will experience a qua-dratic power decay typical of free space communications.This could lead to non-accurate simulationresults. Fig.1.Taxonomy of wireless signal propagation models for V2V/V2R communications.In any case,the former models are not able to capture the dif-ferent subtleties of wireless communications in general,and vehic-ular environments in particular.Therefore,most models employed in practice follow an empirical approach in which analytical expressions for the path loss arefitted according to a set of mea-surements performed in the target scenario.Such expressions approximate the signal strength at an arbitrary distance d by tak-ing as input the signal strength at a reference close distance d0. P rðd0Þcan be either empirically determined or computed according to the free space model(Eq.(1)).For DSRC vehicular communica-tions,commonly employed values for d0are10m[15]and1m [16].The log-distance path loss model[13]includes a path loss expo-nent c that indicates the rate at which the path loss increases with the distance:P rðdÞ¼P rðd0ÞÀ10c log10dd0;ð3Þwhere P r is measured in dB.Given a set of measurements,the path loss exponent c can befitted to let Eq.(3)approximate the real data set.In practice,dual-slope piecewise-linear models like the one in Eq.(4)provide a betterfit.Such model has been employed to rep-resent large-scale path loss in highways[15]by adjusting its parameters according to a set of experiments carried out at highway 101in the Bay Area[17].Afterwards,this dual-slope log-distance model was implemented within ns-2[16]and later on integrated within the official simulator codebase(starting from ns-2.34).P rðdÞ¼P rðd0ÞÀ10c1log10d dd06d6d cP rðd0ÞÀ10c1log10d cÀ10c2log10dcd>d c8><>:ð4ÞSo far,all reviewed models ignore the fact that two receivers at the same distance d from the transmitter could sense very different signal strengths depending on the environment the signal encoun-ters on its path.Different measurements have shown that the sig-nal strength(in dB)is random and log-normally distributed about the mean distance-dependent value.Therefore,log-normal shad-owing models better capture this fact:P rðdÞ¼P rðd0ÞÀ10c log10dd0þX r;ð5Þwhere X r is a zero-mean normally distributed random variable with standard deviation r.By means of sets of experiments,parameters c and r can be obtained via linear regression,adjusting the model to produce realistic random values for a given scenario.Also in this case,more accurate results have been found by using dual-slope piecewise-linear models such as the one in Eq.(6).This model has been employed for urban scenarios[18],where the authors con-ducted two sets of experiments in Pittsburgh.The obtained config-uration has been employed afterwards[19]to perform vehicular simulations in ns-2.3P rðdÞ¼P rðd0ÞÀ10c1log10dþX r1d06d6d cP rðd0ÞÀ10c1log10d c dÀd>d c10c2log10dcþX r28>>>><>>>>:ð6ÞIn order to provide the reader with a handy reference,Table1 summarizes the configuration of the main path loss models that have been employed for characterizing vehicular networks,both in highway and urban scenarios.Fig.2shows the impact of free space,two-ray ground and single-slope log-normal shadowing(ur-ban environment,third row of Table1)on vehicular communica-tions at the5.9GHz DSRC band.The results have been obtained in an interference-free unobstructed scenario with IEEE802.11p communications at3Mbps.The transmission power is set to 20dB,and antennas are mounted at 1.5m height.Destination vehicles are from10m to2000m away from the source,which is-sues500broadcast frames.For each one,we record the power level each receiver senses the frame,including whether it can be suc-cessfully decoded.Note that free space and two-ray ground models provide the same power loss up to the cross-over distance d c(Fig.2(a))in com-mon implementations of these simulation models.In addition, both approaches feature a non-realistic deterministic radio range, Table1Configuration of path loss models for vehicular networks.Legend:P reference to model proposal.S reference to model usage in simulation.Scenario Model Parameters P SHighway dual-slope c1¼1:9c2¼3:8[17]log-distance d c¼200m[16]d c¼80m[15]Urban log-normal c¼2:75r¼5:5[18]Urban log-normal c¼2:32r¼7:1[18]Urban dual-slope c1¼2:1c2¼3:8[18]log-normal r1¼2:6r2¼4:4d c¼100mUrban dual-slope c1¼2c2¼4[18][19]log-normal r1¼5:6r2¼8:4d c¼100m(a)Power loss4006008001000120014001600Distance (m)Free spaceTwo−ray groundLog−normal(b)Reception PDFFig.2.Path loss models for vehicular DSRC.3Code available a t ht tp://masimum.in f.um.es/fjrm/develo pm en t/lognormalnakagami6.4 F.J.Ros et al./Computer Communications43(2014)1–15while the log-normal model provides a non-deterministic radio range.This leads to very distinct frame reception probabilities,as can be observed in Fig.2(b).Suchfigure shows the probability density function(PDF)of successfully decoding a frame with respect to the distance between sender and receiver.In other words,this‘‘Reception PDF’’characterizes the likelihood that the resulting SINR at the receiver is enough to recover the original frame given the sensitivity of the decoder.While deterministic models offer a binary probability distribution in the absence of interference(frames are always decoded if the distance is below the effective communication range,and not decoded otherwise), stochastic approaches introduce randomness typical of wireless communications.The main take-aways from this subsection can be summarized as:Conditions to convey significant simulation results from free space and two-ray ground simulation models are rarely(if ever) achieved in practice for vehicular networks.Log-normal path loss models capture the randomness that log-distance models lack.Usually,multi-slope models provide a betterfit than single-slope ones.Whatever the path loss model employed,it must befine-tuned to the particular vehicular scenario(highway,urban)under evaluation.2.2.2.Small-scale fadingPath loss models discussed so far do not capture the effect of the multiple waves of a signal that arrive at the receiver through differ-ent paths.Since the traveled distance is different for each wave,as well as the environment it traverses,each version of the original signal reaches the receiver with different amplitude at a different time instant.In addition,if the transmitter/receiver is in motion as it is the case in vehicular networks,the Doppler effect also causes frequency dispersion with respect to the original signal.These aspects make the receiver face a heavily distorted version of the original signal.There are abrupt changes in amplitude and phase that can be modeled by means of small-scale fading models. Such kind of fading occurs at the scale of a wavelength,and it might be the dominant component in a severe multi-path environ-ment like the one encountered in vehicular networks.In order to account for small-scale fading,we usually rely on statistical distributions that model the envelope of the signal over time[13].The Ricean distribution models the amplitude of a mul-ti-path envelope when there exists a stronger wave with LOS be-tween transmitter and receiver.As the distance between sender and destination increases in the 5.9GHz band,the probability density function of the signal amplitude is better captured by the Rayleigh distribution.In case there is no line-of-sight(NLOS), higher-than-Rayleigh fading is observed(the Weibull distribution can be a goodfit in such case[20]).In general,the Nakagami distribution[21]is able to capture different severities of fading depending on the chosen parameters. In fact,Ricean fading can be approximated by Nakagami,Rayleigh fading can be seen as a special case of the Nakagami model,and higher-than-Rayleigh fading can be modeled with Nakagami.This distribution approximates the amplitude of the wireless signal according to the following probability density function fðx;l;xÞ.fðx;l;xÞ¼2l l x2lÀ1x l CðlÞeÀl x2x;ð7Þwhere l is a shape parameter,x¼E½x2 is an estimate of the aver-age power in the fading envelope,and C is the Gamma function. Therefore,x can be computed by employing any of the path loss models that we discussed above.If l¼1,Rayleigh fading is obtained(higher-than-Rayleigh fading for l<1,and less severe fading for l>1).Given that signal amplitude is Nakagami-distributed with parameters l;xðÞ,signal power obeys a Gamma distribution with parametersðl;x lÞ.A set of experiments conducted at highway101in the Bay Area[17]were employed tofit this model.Estimate x is computed by means of the dual-slope log-distance model discussed in the previ-ous subsection.For a betterfit of the data,distances between sen-der and receiver are grouped in a set of bins and differentfits for parameter l are estimated on a per-bin basis.Table2shows the parameters employed in two different highway setups.The Nakagami fading model has also been used for urban sce-narios.In the Pittsburgh experiments[18],x is calculated by means of log-normal path loss models(both single-slope and dual-slope).The value of parameters l on a per-bin basis on two different data sets are provided in Table2.This model is available for simulation as a separate patch for the ns-2network simulator.3 Fig.3compares the power loss and reception probability of high-way(first row of Table2)and urban(fourth row of Table2)models in DSRC(same setup as in the otherfigures of this section).Note the higher dispersion that fading models provoke with respect to large-scale path loss(Fig.3(a)and(c)),as well as the higher chal-lenge that urban environments impose on vehicular communica-tions(Fig.3(b)and(d)).Fig.4shows the cumulative density function(CDF)of the reception probability in IEEE802.11p when different path loss and fading models are employed in simulation. Suchfigure summarizes the results we have seen so far.Specifi-cally,deterministic path loss models are shown to give afixed communication range,which is lower for two-ray ground than free space given that the former models a higher power decay beyond the cross-over distance.On its hand,the log-normal path loss mod-el provides a probabilistic communication range with higher reception likelihood at short distances than at long distances. When small-scale fading is also taken into account,the effective communication range decreases(the reduction is higher in urban scenarios than in highways).The main take-aways from this subsection are:Small-scale fading is the dominant component in dynamic multi-path scenarios like those comprised of communicating vehicles.Therefore,it must be taken into account to convey realistic simulation results.The Nakagami model is general enough to capture different lev-els of fading.Table2Configuration of the Nakagami fading model for vehicular networks.Legend:P reference to model proposal.S reference to model usage in simulation.Scenario x l P SHighway Dual-slope l1¼1:5forð0;80 m[17][19] Log-distance l2¼0:75otherwiseHighway Dual-slope l1¼3forð0;50 m[15][15] Log-distance l2¼1:5forð50;150 ml3¼1otherwiseUrban Log-normal l1¼4:07forð0;5:5 m[18]l2¼2:44forð5:5;13:9 ml3¼3:08forð13:9;35:5 ml4¼1:52forð33:5;90:5 ml5¼0:74forð90:5;230:7 ml6¼0:84forð230:7;588 mUrban Log-normal l1¼3:01forð0;4:7 m[18][19]l2¼1:18forð4:7;11:7 ml3¼1:94forð11:7;28:9 ml4¼1:86forð28:9;71:6 ml5¼0:44forð71:6;177:3 ml6¼0:32forð177:3;439 mF.J.Ros et al./Computer Communications43(2014)1–155A better fit is obtained if different distance bins between sender and receiver are considered.Whatever the fading model employed,it must be fine-tuned to the particular vehicular scenario (highway,urban)under evaluation.2.2.3.Propagation through obstaclesThe former models do not consider obstacles in an explicit way.Given that many of them consist of stochastic processes fine-tuned according to real experiments,the effect of obstacles that reflect,diffract and scatter the original signal is incorporated in an indirect way.However,they do not accurately cover the shadowing of the signal by a given obstacle between two vehicles,since the environ-ment map is not incorporated into the model.Therefore,different works have focused on explicitly account-ing for the impact of obstacles into vehicular communications.Obstacles reduce channel congestion at the cost of a greater (more realistic)number of NLOS situations.This also fosters the appear-ance of hidden terminals,challenging the performance of medium access control schemes.Given the existence of numerous obstacles in real vehicular communication scenarios,they should be consid-ered in simulations since they have a great impact onto the accu-racy of simulation results.Ray tracing techniques have been employed to accurately ac-count for the effect of obstacles in wireless communications [22].However,traditional ray tracing is not appropriate for the simula-tion of vehicular networks due to the high processing requirements it imposes.The approach is not able to scale to large networks.Hence,different works have proposed simplified ray tracing tech-niques that employ pre-processing steps to reduce the simulation time without heavily impacting the accuracy of the results.Along these lines,a general methodology for generating urban channel models for a given 3D map was proposed recently [23].Nevertheless,simpler solutions are often employed in practice.For instance,the ns-2simulator incorporates a so-called shadow-ing visibility model which actually consists of two log-normal models that can be independently configured.One of them is used when there is LOS between the communicating entities,and the other is employed for NLOS cases.In order to determine what mod-el shall be used for a given transmission,the user must provide a bitmap file that represents the obstacles in the simulation scenario.The former approach is hard to generalize because it does not account for the number of traversed obstacles,their size,and their shape.In general,it is better to rely on a path loss model and com-pute the extra attenuation which is due to the obstacles which are in the path of the signal.Such scheme is adopted by the inexpen-sive empirical model [24]for urban simulation,in which the extra loss L o is related to the number of times n the border of a obstacle is(a)Power loss -Urban(b)Power loss -Highway vs Urban05001000150020000.511.522.533.5Distance (m)Urban path loss Urban path loss + fading(c)Reception PDF -Urban 020040060080010001200140000.511.522.533.5Distance (m)Highway Urban(d)Reception PDF -Highway vs UrbanFig.3.Fading models for vehicular DSRC.6 F.J.Ros et al./Computer Communications 43(2014)1–15。
可再生能源 英语作文
可再生能源英语作文Title: The Future of Renewable Energy。
Renewable energy has emerged as a crucial component in the global quest for sustainability and environmental preservation. With increasing concerns over climate change and the finite nature of fossil fuels, the importance of transitioning to renewable sources of energy has become more apparent than ever before. In this essay, we will delve into the significance of renewable energy, its benefits, challenges, and the path forward towards a more sustainable future.First and foremost, renewable energy refers to energy derived from naturally replenished resources that are virtually inexhaustible on a human timescale. These sources include solar, wind, hydroelectric, geothermal, and biomass energy. Unlike fossil fuels, which emit harmful greenhouse gases upon combustion, renewable energy sources offer a cleaner and more sustainable alternative. 。
有机碳文献目录
要查的参考文献:2010-9-31.suborbital timescale variability of North Atlantic Deep water during the past 2000ooo years李力, 孙有斌, 鹿化煜, 等. 末次间冰期黄土高原粉尘事件及其与北大西洋寒冷事件的对比. 科学通报, 1998, 43(1): 90—932.冯兆东, 陈发虎, 张虎才, 等. 末次冰期-间冰期蒙古高原与黄土高原对全球变化的重要贡献. 中国沙漠, 2000, 20(2): 171—1773.[易朝路,焦克勤,刘克新,等3 冰碛物V?"测年与天山乌鲁木齐河源末次冰期系列[>]冰川冻土,4.李力, 孙有斌, 鹿化煜, 等. 末次间冰期黄土高原粉尘事件及其与北大西洋寒冷事件的对比. 科学通报, 1998, 43(1): 90—935.安芷生, Porter S C, Chappell J, 等. 最近130 ka 洛川黄土堆积序列与格陵兰冰芯记录对比. 科学通报, 1994, 39(24): 2254—22566.吴锡浩, 蒋复初, 肖华国, 等. 中原邙山黄土及最近200 ka 构造运动与气候变化. 中国科学D 辑: 地球科学, 1999, 29(1): 75-817.覃嘉铭, 袁道先, 程海, 等. 贵州都匀七星洞石笋剖面晚更新世高分辨率的气候地层学. 第四纪研究, 2004, 24(3): 318—3248.罗运利, 孙湘君. 南海北部周边地区倒数第二次冰期-末次间冰期植被演化. 海洋地质与第四纪地质, 2003, 23(1): 19—269.方小敏, 戴雪荣, 李吉均. 末次间冰期季风气候不稳定性——以末次间冰期古土壤发生为例. 中国科学, D 辑, 1996,26(2) 154~160 71 Fang J Q. Lake evolution10.安芷生, Porter S C, Chappell J, 等. 最近130 ka 洛川黄土堆积序列与格陵兰冰芯记录.科学通报, 1994, 39(24):2254~225611.Chen F H, Bloemendal J, Wang J M, et al. High-resolution multi-proxy climate records fromChinese loess: evidence for rapid climatic changes over the last 75 kyr B.P.. Palaeogeography Palaeoclimatology Palaeoecology, 1997, 130: 323~33512.Ding Z L, Rutter N W, Liu T S, et al. Correlation of Dansgaard-Oeschger cycles betweenGreenland ice and Chinese loess. Palaeoclimates, 1997, 4: 1~1113.丁仲礼, 任剑章, 刘东生, 等. 晚更新世季风-沙漠系统千年尺度的不规则变化及其机制问题. 中国科学, D 辑, 1996,26(5): 386~39114.郭正堂, 刘东生, 吴乃琴, 等. 最后两个冰期黄土记录中的Heinrich 型气候节拍. 第四纪研究, 1996, (1): 21~2815.姚檀栋. 末次冰期青藏高原的气候突变古里雅冰芯与GRIP 冰芯对比研究. 中国科学,D 辑, 1999, 29(2) 175~18416.熊尚发, 丁仲礼, 刘东生, 等. 末次冰期中国黄土古气候记录与高纬冰芯及热带海洋记录对比. 海洋地质与第四纪地质, 1998, 18(1) 71~7617.沈吉,刘兴起,MatsumotoR等.晚冰期以来青海湖沉积物多指标高分辨率的古气候演化.中国科学(D辑), 2004,34(6):582~58918.王国安,韩家懋,周力平. 中国北方C3植物碳同位素组成与年均温度关系[ J ]. 中国地质, 2002, 29 ( 1) : 55257 [Wang Guoan, Han J iamao, Zhou Lip ing. Relationship s between δ13C values of C3 p lantsand the annual average temperature in northern China[ J ].Geology in China, 2002, 29 (1) : 55257 ]19.王国安,韩家愁,刘东生. 中国北方黄土区C23草本植物碳同位素组成研究[ J ]. 中国科学:D辑, 2003, 33 (6) : 5502556 [Wang Guo2an, Han J iamao, Liu Dongsheng. The carbonisotope composition of C3 herbaceous p lants in loess area of northern China. Science in China: SeriesD, 2003, 46 (10) : 106921076 ]20.宋友桂,朱诚. 1998.天目山深溪流域晚更新世晚期以来环境演变[J].山地研究, 16 (4) : 257~ 262.21.张美良,袁道先,林玉石,覃嘉铭,章程,程海. 2003.桂林响水洞600 ka BP以来石笋高分辨率的气候记录[J].地球学报, 24(5):439 ~ 444.22.刘东生第四纪环境[*]5 北京:科学出版社,23.姚檀栋,施雅风,等古里雅冰心中末次间冰期以来气候变化记录研究[6]5 中国科学(D辑)施雅风5 中国冰川199724.有机碳同位素文献:1.刘嘉麒,倪云燕,储国强. 2001.第四纪的主要气候事件[J].第四纪研究, 21 (3): 239 ~ 248. 碳酸盐文献:2.吴敬禄, 王苏民, 潘红玺, 等. 青藏高原东部RM 孔140 ka 以来湖泊碳酸盐同位素记录的古气候特征. 中国科学, D辑, 1997, 27(3): 255~2593.顾兆炎.黄土-古土壤序列碳酸盐同位素组成与古气候变化[J].科学通报, 1991(10):767-770.4.赵景波.黄土地层中的CaCO3与环境.沉积学报, 1993,11(1):136~1425.谭明,秦小光,刘东升等.洞穴碳酸钙沉积的古气候记录研究[J]. 地球科学进展,1996,11(4):388~395Hereinch事件:快捷方式 (3) 到 显示桌面.lnk新疆伊犁地区末次冰期气候的不稳定性叶玮董光荣袁玉江马英杰深度年代曲线图:。
南亚高压的南北偏移与我国夏季降水的关系
南亚高压的南北偏移与我国夏季降水的关系魏维;张人禾;温敏【摘要】该文定义了一个能较好反映南亚高压南北偏移的指数,并发现该指数与我国夏季降水,尤其是华北和长江流域的降水,无论在年际变化上还是长期趋势上都具有十分显著的相关关系.南亚高压位置偏北时,在我国东部至日本上空存在一个显著的异常反气旋,其中心自上而下向南倾斜,在高层给华北地区带来辐散,在低层使得气流在长江流域辐散,在华北地区辐合,造成华北地区降水偏多,长江流域降水偏少.同时,南亚高压偏北对应着高层西风急流以及中层西太平洋副热带高压偏北,使得我国整个雨带偏北.此外,通过与海温的相关分析发现,南亚高压的长期偏南趋势可能受到印度洋增暖的直接影响.南北偏移指数可作为预测我国夏季区域降水的重要指标,在气候预测业务中有一定的应用价值.%South Asian High (SAH) is the most intense and stable upper level anticyclone in boreal summer. As a member of the East Asian Summer Monsoon system, SAH plays an important role in the regional climate anomaly over China.rnThe meridional variation of SAH is analyzed by using the monthly mean data derived from the European Center for Medium-Range Weather Forecasts (ECMWF) 40-year reanalysis (ERA-40) from 1958 to 2002. An index of SAH (SAHI) is defined to measure its meridional variation in summer and to analyze its relationship with the summer precipitation over China. The results show a significant correlation between the meridional position of SAH and the summer rainfall over China for both the interannual timescale variability and the long-term linear tendency. The correlation coefficients between SAHI and the summer rainfall in North China and in the Yangtze River Valleys are 0. 577and ?. 604, respectively, both of which exceeds 0. 01 level. When SAH locates further northward (southward), North China and South China are wetter (drier) than normal, while the Yangtze River Valleys is drier (wetter) than normal. And the southward linear trend of SAH corresponds with the decreasing trend of rainfall in North China and the increasing trend of rainfall in the Yangtze River Valleys.rnLinear regression analysis of the circulation reveals that when SAH locates further northward, an a-nomalous anticyclone controls eastern China with its center tilted southward from 200 hPa to 850 hPa. In the upper atmosphere, the anomalous anticyclone forms a divergence zone over North China. In the lower atmosphere, it results in flows diverging over the Yangtze River valley, and converging over North China. Besides, the northward movement of SAH would cause the upper-level westerly jet and the Western Pacific Subtropical High move northward, with the rainbelt locating in North China.rnThe meridional anomalous variation of SAH is closely related to the sea surface temperature anomalies (SSTA) of the Tropical Indian Ocean (TIO) , the Central and Eastern Equatorial Pacific, and the northern Pacific. And the TIO SSTA might modulate its meridional position directly. Positive TIO SSTA might lead to a southward expansion of SAH.rnDue to strong correlation with the summer rainfall over China and being modulated by the TIO SSTA, the meridional variation of SAHI could be considered as an important indicator used to predict the regional climate anomaly.【期刊名称】《应用气象学报》【年(卷),期】2012(023)006【总页数】10页(P650-659)【关键词】南亚高压;南北偏移;我国夏季降水;印度洋增暖【作者】魏维;张人禾;温敏【作者单位】中国气象科学研究院灾害天气国家重点实验室,北京100081;中国气象科学研究院灾害天气国家重点实验室,北京100081;中国气象科学研究院灾害天气国家重点实验室,北京100081【正文语种】中文该文定义了一个能较好反映南亚高压南北偏移的指数,并发现该指数与我国夏季降水,尤其是华北和长江流域的降水,无论在年际变化上还是长期趋势上都具有十分显著的相关关系。
脑机接口概述
美国Smith-Kettlewell视觉科学研究所 Sutter等人设计的脑反应接口以对视觉刺激
反应中所产生的视觉诱发电位作为BCI信号 输入,通过诱发电位选择计算机显示屏上 某一特定部分,进而可以实现选择的功能 。
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我国清华大学 高上凯等人深入分析了稳态视觉诱发电位 (SSVEP)的特征和提取方法,设计了具有
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BCI分类
基于视觉诱发电位的BCI 基于P300信号的BCI 基于皮层慢电位的BCI 基于感知运动节律的BCI
7受到一个固定频率的视觉刺 激的时候,人的大脑视觉皮层会产生一个 连续的与刺激频率有关( 刺激频率的基频或 倍频处) 的响应。这个响应被称为稳态视觉 诱发电位( Steady-State Visual Evoked Potentials,SSVEP),它可以可靠的应用于脑 -机接口系统( BCIs) 。
脑-机接口概述
研究背景
肌萎缩性脊髓 侧索 硬化症
脑中风 脑或脊髓损伤 脑瘫 其他疾病
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脑-机接口的定义
脑机接口(英语:brain-computer interface,简称 BCI;有时也称作direct neural interface或者brainmachine interface),是在人或动物脑与外部设备 间创建的直接连接通路。
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脑电信息的解析
信号的实时在线处理 个体参数优化的问题 脑-机交互适应学习的问题 异步的BCI系统工作模式
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实用化的系统设计
系统工作稳定可靠 用户在使用中方便舒适 系统可便携且价格便宜
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脑-机接口产品
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我们BCI所作的工作
脑疲劳 基于ALPHA波的BCI 基于运动想象的BCI 基于视觉稳态刺激的BCI
东亚大槽变异及其与东亚冬季风的关系
附件2作者姓名:王林论文题目:东亚大槽变异及其与东亚冬季风的关系作者简介:王林,男,1981年1月出生,2003年9月进入中国科学院大气物理研究所硕博连读,师从陈文研究员,于2008年7月获博士学位。
中文摘要东亚冬季风是北半球冬季最活跃的气候系统成员之一,其年际、年代际变化正受到人们越来越多的关注。
本论文利用ERA40再分析资料、中国160站气温降水资料、NOAA/CPC 全球陆地降水资料以及英国哈德莱中心的历史SST、陆地气温资料,从东亚冬季风的重要成员-东亚大槽的变化出发,对东亚冬季风强度和路径的年际变化进行了分析,并在年际和年代际时间尺度上分析了上游乌拉尔山地区的环流异常以及下游北太平洋地区的海洋、大气状况对东亚冬季风变化的影响。
此外,我们还利用德国马普气象研究所的大气环流模式MAECHAM5的敏感试验,对影响东亚冬季风路径年际变化的因子进行了考察。
论文主要研究内容和结果如下:(1)东亚大槽槽线倾斜的变化与东亚冬季风路径的关系。
东亚大槽年际变化的第二模态描述了大槽槽线倾斜的变化,它可以反映东亚冬季风两支气流间的相对强弱。
当东亚大槽槽线偏竖时,冬季风的南支气流偏强而东支气流偏弱,更多的冷空气会沿冬季风的南支气流进入赤道,因此,东亚北部大范围的地区温度显著增加;同时,由于南下的冷空气增强,赤道的对流活跃区被向南推移,中南半岛的降水有所减少。
当东亚大槽槽线偏斜的时候,结果基本相反。
由于冬季风路径变化所引起的温度异常可能会超过冬季风强弱所引起的温度异常,因此考虑冬季风路径的变化对冬季气候预测有非常重要的意义。
此外,这种变化对后期气候预测也有一定的指示意义。
当冬季风南支气流偏强时,随后的春季南海-西太平洋区域温度偏低,暖湿空气北跳延迟,这会导致华南地区降水减少,前汛期偏弱。
资料分析表明,北太平洋的海温异常是引起东亚大槽轴向变化的重要因子,正(负)的北太平洋海温会对应偏竖(斜)的东亚大槽。
这种影响可能是通过东亚-太平洋地区温度梯度的改变,进而影响大气中的斜压波来实现的。
指南—ATA
ORIGINAL STUDIES,REVIEWS,AND SCHOLARLY DIALOGTHYROID CANCER AND NODULESRevised American Thyroid Association ManagementGuidelines for Patients with Thyroid Nodulesand Differentiated Thyroid CancerThe American Thyroid Association (ATA)Guidelines Taskforceon Thyroid Nodules and Differentiated Thyroid CancerDavid S.Cooper,M.D.1(Chair)*,Gerard M.Doherty,M.D.,2Bryan R.Haugen,M.D.,3Richard T.Kloos,M.D.,4Stephanie L.Lee,M.D.,Ph.D.,5Susan J.Mandel,M.D.,M.P.H.,6Ernest L.Mazzaferri,M.D.,7Bryan McIver,M.D.,Ph.D.,8Furio Pacini,M.D.,9Martin Schlumberger,M.D.,10Steven I.Sherman,M.D.,11David L.Steward,M.D.,12and R.Michael Tuttle,M.D.13Background:Thyroid nodules are a common clinical problem,and differentiated thyroid cancer is becoming increasingly prevalent.Since the publication of the American Thyroid Association’s guidelines for the man-agement of these disorders was published in 2006,a large amount of new information has become available,prompting a revision of the guidelines.Methods:Relevant articles through December 2008were reviewed by the task force and categorized by topic and level of evidence according to a modified schema used by the United States Preventative Services Task Force.Results:The revised guidelines for the management of thyroid nodules include recommendations regarding initial evaluation,clinical and ultrasound criteria for fine-needle aspiration biopsy,interpretation of fine-needle aspiration biopsy results,and management of benign thyroid nodules.Recommendations regarding the initial management of thyroid cancer include those relating to optimal surgical management,radioiodine remnant ablation,and suppression therapy using levothyroxine.Recommendations related to long-term management of differentiated thyroid cancer include those related to surveillance for recurrent disease using ultrasound and serum thyroglobulin as well as those related to management of recurrent and metastatic disease.Conclusions:We created evidence-based recommendations in response to our appointment as an independent task force by the American Thyroid Association to assist in the clinical management of patients with thyroid nodules and differentiated thyroid cancer.They represent,in our opinion,contemporary optimal care for pa-tients with these disorders.Thyroid nodules are a common clinical problem.Epi-demiologic studies have shown the prevalence of palpa-ble thyroid nodules to be approximately 5%in women and 1%in men living in iodine-sufficient parts of the world (1,2).In contrast,high-resolution ultrasound (US)can detect thyroid nodules in 19–67%of randomly selected individuals with higher frequencies in women and the elderly (3).The clinical importance of thyroid nodules rests with the need to exclude thyroid cancer which occurs in 5–15%depending on age,sex,radiation exposure history,family history,and other factors*Authors are listed in alphabetical order and were appointed by ATA to independently formulate the content of this manuscript.None of the scientific or medical content of the manuscript was dictated by the ATA.1The Johns Hopkins University School of Medicine,Baltimore,Maryland.2University of Michigan Medical Center,Ann Arbor,Michigan.3University of Colorado Health Sciences Center,Denver,Colorado.4The Ohio State University,Columbus,Ohio.5Boston University Medical Center,Boston,Massachusetts.6University of Pennsylvania School of Medicine,Philadelphia,Pennsylvania.7University of Florida College of Medicine,Gainesville,Florida.8The Mayo Clinic,Rochester,Minnesota.9The University of Siena,Siena,Italy.10Institute Gustave Roussy,Paris,France.11University of Texas M.D.Anderson Cancer Center,Houston,Texas.12University of Cincinnati Medical Center,Cincinnati,Ohio.13Memorial Sloan-Kettering Cancer Center,New York,New York.THYROIDVolume 19,Number 11,2009ªMary Ann Liebert,Inc.DOI:10.1089=thy.2009.01101167(4,5).Differentiated thyroid cancer(DTC),which includes papillary and follicular cancer,comprises the vast majority (90%)of all thyroid cancers(6).In the United States,approx-imately37,200new cases of thyroid cancer will be diagnosed in2009(7).The yearly incidence has increased from3.6per 100,000in1973to8.7per100,000in2002,a2.4-fold increase (p<0.001for trend)and this trend appears to be continuing (8).Almost the entire change has been attributed to an in-crease in the incidence of papillary thyroid cancer(PTC), which increased2.9-fold between1988and2002.Moreover, 49%of the rising incidence consisted of cancers measuring 1cm or smaller and87%consisted of cancers measuring2cm or smaller(8).This tumor shift may be due to the increasing use of neck ultrasonography and early diagnosis and treat-ment(9),trends that are changing the initial treatment and follow-up for many patients with thyroid cancer.In1996,the American Thyroid Association(ATA)pub-lished treatment guidelines for patients with thyroid nodules and DTC(10).Over the last decade,there have been many advances in the diagnosis and therapy of both thyroid nodules and DTC.Controversy exists in many areas,including the most cost-effective approach in the diagnostic evaluation of a thyroid nodule,the extent of surgery for small thyroid cancers, the use of radioactive iodine to ablate remnant tissue following thyroidectomy,the appropriate use of thyroxine suppression therapy,and the role of human recombinant thyrotropin (rhTSH).In recognition of the changes that have taken place in the overall management of these clinically important prob-lems,the ATA appointed a task force to re-examine the current strategies that are used to diagnose and treat thyroid nodules and DTC,and to develop clinical guidelines using principles of evidence-based medicine.Members of the taskforce included experts in thyroid nodule and thyroid cancer management with representation from thefields of endocrinology,surgery, and nuclear medicine.The medical opinions expressed here are those of the authors;none were dictated by the ATA.The final document was approved by the ATA Board of Directors and endorsed(in alphabetical order)by the American Asso-ciation of Clinical Endocrinologists(AACE),American College of Endocrinology,British Association of Head and Neck Oncologists(BAHNO),The Endocrine Society,European As-sociation for Cranio-Maxillo-Facial Surgery(EACMFS),Eur-opean Association of Nuclear Medicine(EANM),European Society of Endocrine Surgeons(ESES),European Society for Paediatric Endocrinology(ESPE),International Association of Endocrine Surgeons(IAES),and Latin American Thyroid So-ciety(LATS).Other groups have previously developed guidelines,in-cluding the American Association of Clinical Endocrinologists and the American Association of Endocrine Surgeons(11),the British Thyroid Association and The Royal College of Physi-cians(12),and the National Comprehensive Cancer Network (13)that have provided somewhat conflicting recommenda-tions due to the lack of high quality evidence from random-ized controlled trials.The European Thyroid Association has published consensus guidelines for the management of DTC (14).The European Association of Nuclear Medicine has also recently published consensus guidelines for radioiodine(RAI) therapy of DTC(15).The ATA guidelines taskforce used a strategy similar to that employed by the National Institutes of Health for its Consen-sus Development Conferences(http:===aboutcdp.htm),and developed a series of clinically relevant questions pertaining to thyroid nodule and thyroid cancer di-agnosis and treatment.These questions were as follows:—Questions regarding thyroid nodulesWhat is the appropriate evaluation of clinically or inci-dentally discovered thyroid nodule(s)?*What laboratory tests and imaging modalities are in-dicated?*What is the role offine-needle aspiration(FNA)?What is the best method of long-term follow up of pa-tients with thyroid nodules?What is the role of medical therapy of patients with benign thyroid nodules?How should thyroid nodules in children and pregnant women be managed?—Questions regarding the initial management of DTCWhat is the role of preoperative staging with diagnostic imaging and laboratory tests?What is the appropriate operation for indeterminate thyroid nodules and DTC?What is the role of postoperative staging systems and which should be used?What is the role of postoperative RAI remnant ablation? What is the role of thyrotropin(TSH)suppression therapy?Is there a role for adjunctive external beam irradiation or chemotherapy?—Questions regarding the long term management of DTC What are the appropriate features of long-term man-agement?What is the role of serum thyroglobulin(Tg)assays? What is the role of US and other imaging techniques during follow-up?What is the role of TSH suppression in long-term follow-up?What is the most appropriate management of patients with metastatic disease?How should Tg-positive,scan-negative patients be managed?What is the role of external radiation therapy?What is the role of chemotherapy?—What are directions for future research?The initial ATA guidelines were published in2006(16). Because of the rapid growth of the literature on this topic, plans for revising the guidelines within24–36months of publication were made at the inception of the project.Re-levant articles on thyroid cancer were identified using the same search criteria employed for the original guidelines(16). Individual task force members submitted suggestions for clarification of prior recommendations,as well as new infor-mation derived from studies published since2004.Relevant literature continued to be reviewed through December2008. To begin the revision process,a half-day meeting was held on June2,2007.The Task Force was broadened to include European experts and a head and neck surgeon.Three sub-sequent half-day meetings were held on October5,2007;July 13,2008;and October5,2008,to review these suggestions and for additional comments to be considered.The meeting in July 2008also included a meeting with six additional surgeons in1168COOPER ET AL.REVISED ATA THYROID CANCER GUIDELINES1169anization of Management Guideline Recommendations,Tables,and Figuresfor Patients with Thyroid Nodules and Differentiated Thyroid CancerPage Location key a Sections and subsections Item b1171[A1]THYROID NODULE GUIDELINES T11171[A2]Evaluation of Newly Discovered Thyroid Nodules F11171[A3]Laboratory tests1171[A4]Serum TSH R1–R2 1171[A5]Serum thyroglobulin(Tg)R31171[A6]Serum calcitonin R41173[A7]Role offine-needle aspiration(FNA)1173[A8]Ultrasound(US)with FNA R5,T3 1174[A9]Cytopathological interpretation of FNA samples1174[A10]Nondiagnostic cytology R61174[A11]Cytology suggesting papillary thyroid cancer(PTC)R71174[A12]Indeterminate cytology R8–R10 1175[A13]Benign cytology R111175[A14]Multinodular goiter(MNG)=multiple thyroid nodules R12–R13 1175[A15]Long-Term Follow-Up of Thyroid Nodules R14–R15 1176[A16]Medical therapy for benign thyroid nodules R16–R17 1176[A17]Thyroid nodules in children R181176[A18]Thyroid nodules in pregnant women R19–R20 1176[B1]DIFFERENTIATED THYROID CANCER(DTC):INITIAL MANAGEMENT GUIDELINES1176[B2]Goals of Initial Therapy of DTC1177[B3]Preoperative staging of DTC1177[B4]Neck imaging R21–R22 1177[B5]Serum Tg R231177[B6]Thyroid surgery1178[B7]Surgery for nondiagnostic biopsy R24–R25 1178[B8]Surgery for biopsy diagnostic of malignancy R261179[B9]Lymph node dissection R27–R28,F2 1180[B10]Completion thyroidectomy R29–R30 1180[B11]Postoperative staging systems1180[B12]Role of postoperative staging1180[B13]AJCC=UICC TNM staging R31,T4 1181[B14]Role of postoperative remnant ablation R32,T5 1183[B15]Preparation for radioiodine(RAI)remnant ablation R33,F3 1183[B16]rhTSH preparation R341183[B17]RAI scanning before RAI ablation R351185[B18]Radiation doses for RAI ablation R36–R37 1185[B19]Low-iodine diet for RAI ablation R381185[B20]Post RAI ablation whole-body RAI scan R391185[B21]Post Initial Therapy of DTC1185[B22]Role of TSH suppression therapy1185[B23]Degree of initial TSH suppression required R401186[B24]Adjunctive measures1186[B25]External beam irradiation R411186[B26]Chemotherapy R421186[C1]DTC:LONG-TERM MANAGEMENT1186[C2]Appropriate Features of Long-Term Management1186[C3]Appropriate method of follow-up after surgery F41186[C4]Criteria for absence of persistent tumor1186[C5]Role of serum Tg assays R43–R45 1189[C6]Whole body RAI scans,US,and other imagingIf viewing these guidelines on the Web,or in a File,copy the Location Key to the Find or Search Function to navigate rapidly to the desired section.b R,recommendation;T,table;F,figure.(continued)Table1.(Continued)Page Location key a Sections and subsections Item b 1189[C7]Diagnostic whole-body RAI scans R46–R47 1189[C8]Cervical ultrasound R48a–c 1189[C9]FDG-PET Scanning R48d1189[C10]Role of thyroxine suppression of TSH R491190[C11]Management of Metastatic Disease1190[C12]Surgery for locoregional metastases R501190[C13]Surgery for aerodigestive invasion R511191[C14]RAI for local or distant metastatic disease1191[C15]Methods for administering RAI R52–R54 1191[C16]The use of lithium in RAI therapy R551191[C17]Metastasis to various organs1192[C18]Pulmonary metastasis R56–R58 1192[C19]Non–RAI-avid pulmonary disease R591193[C20]Bone metastases R60–R64 1193[C21]Brain metastases R65–R67 1194[C22]Management of Complications of RAI Therapy R68–R70 1194[C23]Secondary malignancies and leukemia from RAI R711194[C24]Other risks to bone marrow from RAI R721194[C25]Effects of RAI on gonads and in nursing women R73–R74 1195[C26]Management of Tg Positive,RAI Scan–Negative Patients R75–R77,F5 1197[C27]Patients with a negative post-treatment whole-body scan R78–R79 1197[C28]External beam radiation for metastatic disease R801197[D1]DIRECTIONS FOR FUTURE RESEARCH1197[D2]Novel Therapies and Clinical Trials1197[D3]Inhibitors of oncogenic signaling pathways1197[D4]Modulators of growth or apoptosis1197[D5]Angiogenesis inhibitors1197[D6]Immunomodulators1197[D7]Gene therapy1198[D8]Better Understanding of the Long-Term Risks of RAI1198[D9]Clinical Significance of Persistent Low-Level Tg1198[D10]The Problem of Tg Antibodies1198[D11]Small Cervical Lymph Node Metastases1198[D12]Improved Risk StratificationTable2.Strength of Panelists’Recommendations Based on Available EvidenceRating DefinitionA Strongly recommends.The recommendation is based on good evidence that the service or intervention can improveimportant health outcomes.Evidence includes consistent results from well-designed,well-conducted studies in representative populations that directly assess effects on health outcomes.B Recommends.The recommendation is based on fair evidence that the service or intervention can improveimportant health outcomes.The evidence is sufficient to determine effects on health outcomes,but the strength of the evidence is limited by the number,quality,or consistency of the individual studies;generalizability toroutine practice;or indirect nature of the evidence on health outcomes.C Recommends.The recommendation is based on expert opinion.D Recommends against.The recommendation is based on expert opinion.E Recommends against.The recommendation is based on fair evidence that the service or intervention does notimprove important health outcomes or that harms outweigh benefits.F Strongly recommends against.The recommendation is based on good evidence that the service or interventiondoes not improve important health outcomes or that harms outweigh benefits.I Recommends neither for nor against.The panel concludes that the evidence is insufficient to recommend foror against providing the service or intervention because evidence is lacking that the service or interventionimproves important health outcomes,the evidence is of poor quality,or the evidence is conflicting.As a result,the balance of benefits and harms cannot be determined.Adapted from the U.S.Preventive Services Task Force,Agency for Healthcare Research and Quality(17).an effort to produce guidelines related to central neck dis-section that would be as authoritative as possible.The orga-nization of management guideline recommendations is shown in Table1.It was agreed to continue to categorize the published data and strength of recommendations using a modified schema proposed by the U.S.Preventive Services Task Force(17)(Table2).[A1]THYROID NODULE GUIDELINESA thyroid nodule is a discrete lesion within the thyroid gland that is radiologically distinct from the surrounding thyroid parenchyma.Some palpable lesions may not corre-spond to distinct radiologic abnormalities(18).Such abnor-malities do not meet the strict definition for thyroid nodules. Nonpalpable nodules detected on US or other anatomic im-aging studies are termed incidentally discovered nodules or ‘‘incidentalomas.’’Nonpalpable nodules have the same risk of malignancy as palpable nodules with the same size(19). Generally,only nodules>1cm should be evaluated,since they have a greater potential to be clinically significant can-cers.Occasionally,there may be nodules<1cm that require evaluation because of suspicious USfindings,associated lymphadenopathy,a history of head and neck irradiation,or a history of thyroid cancer in one or morefirst-degree relatives. However,some nodules<1cm lack these warning signs yet eventually cause morbidity and mortality.These are rare and, given unfavorable cost=benefit considerations,attempts to diagnose and treat all small thyroid cancers in an effort to prevent these rare outcomes would likely cause more harm than good.Approximately1–2%of people undergoing2-deoxy-2[18F]fluoro-d-glucose positron emission tomography (18FDG-PET)imaging for other reasons have thyroid nodules discovered incidentally.Since the risk of malignancy in these 18FDG-positive nodules is about33%and the cancers may be more aggressive(20),such lesions require prompt evaluation (21–23).When seen,diffuse18FDG uptake is likely related to underlying autoimmune thyroiditis.[A2]What is the appropriate evaluation of clinicallyor incidentally discovered thyroid nodule(s)?(See Fig.1for algorithm)With the discovery of a thyroid nodule,a complete history and physical examination focusing on the thyroid gland and adjacent cervical lymph nodes should be performed.Pertinent historical factors predicting malignancy include a history of childhood head and neck irradiation,total body irradiation for bone marrow transplantation(24),family history of thy-roid carcinoma,or thyroid cancer syndrome(e.g.,Cowden’s syndrome,familial polyposis,Carney complex,multiple en-docrine neoplasia[MEN]2,Werner syndrome)in afirst-degree relative,exposure to ionizing radiation from fallout in childhood or adolescence(25),and rapid growth and hoarseness.Pertinent physicalfindings suggesting possible malignancy include vocal cord paralysis,lateral cervical lymphadenopathy,andfixation of the nodule to surrounding tissues.[A3]What laboratory tests and imaging modalities are indicated?[A4]Serum TSH with US and with or without scan.With the discovery of a thyroid nodule>1cm in any diameter or diffuse or focal thyroidal uptake on18FDG-PET scan,a se-rum TSH level should be obtained.If the serum TSH is subnormal,a radionuclide thyroid scan should be obtained to document whether the nodule is hyperfunctioning(i.e., tracer uptake is greater than the surrounding normal thy-roid),isofunctioning or‘‘warm’’(i.e.,tracer uptake is equal to the surrounding thyroid),or nonfunctioning(i.e.,has uptake less than the surrounding thyroid tissue).Since hyperfunc-tioning nodules rarely harbor malignancy,if one is found that corresponds to the nodule in question,no cytologic evaluation is necessary.If overt or subclinical hyperthy-roidism is present,additional evaluation is required.Higher serum TSH,even within the upper part of the reference range,is associated with increased risk of malignancy in a thyroid nodule(26).&RECOMMENDATION1Measure serum TSH in the initial evaluation of a patient with a thyroid nodule.If the serum TSH is subnormal,a radionuclide thyroid scan should be performed using either technetium99m Tc pertechnetate or123I.Recommendation rating:ADiagnostic thyroid US should be performed in all patients with a suspected thyroid nodule,nodular goiter,or radiographic abnormality;e.g.,a nodule found incidentally on computed tomography(CT)or magnetic resonance im-aging(MRI)or thyroidal uptake on18FDG-PET scan. Thyroid US can answer the following questions:Is there truly a nodule that corresponds to the palpable abnormal-ity?How large is the nodule?Does the nodule have benign or suspicious features?Is suspicious cervical lymphade-nopathy present?Is the nodule greater than50%cystic?Is the nodule located posteriorly in the thyroid gland?These last two features might decrease the accuracy of FNA bi-opsy performed with palpation(27,28).Also,there may be other thyroid nodules present that require biopsy based on their size and appearance(18,29,30).As already noted, FNA is recommended especially when the serum TSH is elevated because,compared with normal thyroid glands, the rate of malignancy in nodules in thyroid glands involved with Hashimoto’s thyroiditis is as least as high or possibly higher(31,32).&RECOMMENDATION2Thyroid sonography should be performed in all patients with known or suspected thyroid nodules.Recommenda-tion rating:A[A5]Serum Tg measurement.Serum Tg levels can be ele-vated in most thyroid diseases and are an insensitive and nonspecific test for thyroid cancer(33).&RECOMMENDATION3Routine measurement of serum Tg for initial evaluation of thyroid nodules is not recommended.Recommendation rating:F[A6]Serum calcitonin measurement.The utility of serum calcitonin has been evaluated in a series of prospective, nonrandomized studies(34–37).The data suggest that theREVISED ATA THYROID CANCER GUIDELINES1171use of routine serum calcitonin for screening may detect C-cell hyperplasia and medullary thyroid cancer at an earlier stage and overall survival may be improved.How-ever,most studies rely on pentagastrin stimulation test-ing to increase specificity.This drug is no longer available in the United States,and there remain unresolved issues of sensitivity,specificity,assay performance and cost-effectiveness.A recent cost-effectiveness analysis suggested that calcitonin screening would be cost effective in the United States (38).However,the prevalence estimates of medullary thyroid cancer in this analysis included patients with C-cell hyperplasia and micromedullary carcinoma,123I or 99Tc Scan a Normal or High TSHHistory, Physical, TSHLow TSHDiagnostic USRESULTS of FNAElevated TSHEvaluate and RxforHyperthyroidismNot Functioning HyperfunctioningNodule on US Do FNA (See R5a–c)No Nodule on USNormal TSHEvaluate andRx for Hypo-thyroidismFNA not IndicatedNondiagnosticMalignant PTCSuspicious for PTCBenignIndeterminateRepeat US- Guided FNANon-diagnosticClose Follow-Up or Surgery (SeeText)Pre-op USSurgeryFollicular NeoplasmHürthle Cell NeoplasmFollowConsider 123I Scanif TSH Low NormalNotHyperfunctioningHyperfunctioning WORKUP OF THYROID NODULEDETECTED BY PALPATION OR IMAGINGFIG.1.Algorithm for the evaluation of patients with one or more thyroid nodules.aIf the scan does not show uniform distribution of tracer activity,ultrasound may be considered to assess for the presence of a cystic component.1172COOPER ET AL.which have an uncertain clinical significance.If the un-stimulated serum calcitonin determination has been ob-tained and the level is greater than100pg=mL,medullary cancer is likely present(39).&RECOMMENDATION4The panel cannot recommend either for or against the routine measurement of serum calcitonin.Recommenda-tion rating:I[A7]What is the role of FNA biopsy?FNA is the most accurate and cost-effective method for evaluating thyroid nodules.Retrospective studies have reported lower rates of both nondiagnostic and false-negative cytology specimens from FNA procedures performed via US guidance compared to palpation(40,41).Therefore,for nodules with a higher likelihood of either a nondiagnostic cytology(>25–50%cystic component)(28)or sampling error(difficult to palpate or posteriorly located nodules),US-guided FNA is preferred(see Table3).If the diagnostic US confirms the presence of a pre-dominantly solid nodule corresponding to what is palpated, the FNA may be performed via palpation or US guidance. Traditionally FNA biopsy results are divided into four cate-gories:nondiagnostic,malignant(risk of malignancy at sur-gery>95%),indeterminate or suspicious for neoplasm,and benign.The recent National Cancer Institute Thyroid Fine-Needle Aspiration State of the Science Conference proposed a more expanded classification for FNA cytology that adds two additional categories:suspicious for malignancy(risk of ma-lignancy50–75%)and follicular lesion of undetermined sig-nificance(risk of malignancy5–10%).The conference further recommended that‘‘neoplasm,either follicular or Hu¨rthle cell neoplasm’’be substituted for‘‘indeterminate’’(risk of malig-nancy15–25%)(42).[A8]US for FNA decision making(see Table3).Various sonographic characteristics of a thyroid nodule have been associated with a higher likelihood of malignancy(43–48). These include nodule hypoechogenicity compared to the normal thyroid parenchyma,increased intranodular vascu-larity,irregular infiltrative margins,the presence of micro-calcifications,an absent halo,and a shape taller than the width measured in the transverse dimension.With the exception of suspicious cervical lymphadenopathy,which is a specific but insensitivefinding,no single sonographic feature or combi-nations of features is adequately sensitive or specific to identify all malignant nodules.However,certain features and combination of features have high predictive value for ma-lignancy.Furthermore,the most common sonographic ap-pearances of papillary and follicular thyroid cancer differ.A PTC is generally solid or predominantly solid and hy-poechoic,often with infiltrative irregular margins and in-creased nodular vascularity.Microcalcifications,if present, are highly specific for PTC,but may be difficult to distinguish from colloid.Conversely,follicular cancer is more often iso-to hyperechoic and has a thick and irregular halo,but does not have microcalcifications(49).Follicular cancers that are<2cm in diameter have not been shown to be associated with met-astatic disease(50).Certain sonographic appearances may also be highly pre-dictive of a benign nodule.A pure cystic nodule,although rare (<2%of all nodules),is highly unlikely to be malignant(47).In addition,a spongiform appearance,defined as an aggregation of multiple microcystic components in more than50%of the nodule volume,is99.7%specific for identification of a benignTable3.Sonographic and Clinical Features of Thyroid Nodules and Recommendations for FNA Nodule sonographic or clinical features Recommended nodule threshold size for FNAHigh-risk history aNodule WITH suspicious sonographic features b>5mm Recommendation A Nodule WITHOUT suspicious sonographic features b>5mm Recommendation I Abnormal cervical lymph nodes All c Recommendation A Microcalcifications present in nodule 1cm Recommendation B Solid noduleAND hypoechoic>1cm Recommendation B AND iso-or hyperechoic 1–1.5cm Recommendation C Mixed cystic–solid noduleWITH any suspicious ultrasound features b 1.5–2.0cm Recommendation B WITHOUT suspicious ultrasound features 2.0cm Recommendation C Spongiform nodule 2.0cm d Recommendation C Purely cystic nodule FNA not indicated e Recommendation E a High-risk history:History of thyroid cancer in one or morefirst degree relatives;history of external beam radiation as a child;exposure to ionizing radiation in childhood or adolescence;prior hemithyroidectomy with discovery of thyroid cancer,18FDG avidity on PET scanning;MEN2=FMTC-associated RET protooncogene mutation,calcitonin>100pg=mL.MEN,multiple endocrine neoplasia;FMTC,familial medullary thyroid cancer.b Suspicious features:microcalcifications;hypoechoic;increased nodular vascularity;infiltrative margins;taller than wide on transverse view.c FNA cytology may be obtained from the abnormal lymph node in lieu of the thyroid nodule.d Sonographic monitoring without biopsy may be an acceptable alternative(see text)(48).e Unless indicated as therapeutic modality(see text).REVISED ATA THYROID CANCER GUIDELINES1173。
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a r X i v :a s t r o -p h /9906200v 1 11 J u n 1999Short-timescale Variability in the Broadband Emission of the Blazars Mkn421and Mkn501M.J.Carson 1,B.McKernan 1,T.Yaqoob 2,D.J.Fegan 11Department of Physics,University College Dublin,Dublin 4,Ireland2Laboratory for High Energy Astrophysics,Code 660.2Goddard Space Flight Center,Greenbelt,MD 20771AbstractWe analyse ASCA x-ray data and Whipple γ-ray data from the blazars Mkn421and Mkn501for short-timescale variability.We find no evidence for statistically significant (>3σ)variability in these data,in either source,on timescales of less than ∼10minutes.1IntroductionThe recently detected TeV radiation from the Blazars Mkn421and Mkn501may be Comptonized syn-chrotron radiation (Comastri et al.,1997)or Comptonized ambient radiation (Sikora et al.,1997)or some mix-ture of the two.Alternatively,the γ-radiation may result from a proton-initiated cascade (Beall &Bednarek,1998).Models of the γ-radiation preict large qualitative differences in some source parameters,so a search for short-timescales of variability in these AGN may be important in constraining models of emission and ultimately in distinguishing between them.Short timescale variability may also constrain quantum gravity effects predicted by some string theories (Amelino-Camelia et al.,1998).Variability studies (using the χ2-test)have been carried out on data from Mkn501and Mkn421.The shortest observed timescales of variability in TeV γ-rays correspond to doubling times in rapid flares of ∼15minutes for Mkn421(McEnery,1997)and ∼2hours for Mkn501(Quinn et al.,1999)which may indicate a variability cut-off or a lack of instrumental or method sensitivity to real variability at timescales shorter than these.The shortest timescales so far observed in these sources at x-ray energies are ∼1day for Mkn501(Kataoka et al.,1999)and 0.5-1.0days for Mkn421(Takahashi,Madejski &Kubo,1999).The analysis reported here involves a broadband variability study of Mkn501and Mkn421using the Excess Pair Fraction (EPF)technique (Yaqoob et al.,1997).We analyse non-simultaneous Whipple TeV γ-ray data and ASCA x-ray data from these blazars.2EPF applied to x-ray data ASCA (Tanaka et al.1994)observed Mkn 421and Mkn 501,obtaining moderate energy resolution spectra in the 0.5–10keV band,with a time-resolution of bet-ter than 2s for two of the four instruments onboard (4s for the other two).Mkn 421was observed on eigh-teen occassions between 1993,May 10and 1997,June 3,with a total exposure time of ∼240×103s.The 1996data do not overlap with the TeV data reported in this paper.Mkn 501was observed on four occassions between 1996,March 21and 1996,April 2,with a to-tal exposure time of ∼49×103s.Additional ASCA observations of both sources exist but only those madeof the dates mentioned above were in the public archive at the time of writing.An excellent account of the Mkn 421ACSA observations and results can be found in Taka-hashi,Madejski,and Kubo (1999)and the ASCA results for Mkn 501can be found in Kataoka et al.(1999).We re-analysed the ASCA data ourselves,which were re-Figure 1:EPF for ASCA x-ray data on Mkn421.duced in the same manner as described in Yaqoob et al.(1998).We made EPF for each source following the methods described in Yaqoob et al.(1997),averagedThe results for both sources are shown in Figs.show theoretical EPF for a source with 0%,5%,and10%random amplitude variability respectively.The curves were generated as described in detail in Yaqoob et al.1997.It can be seen that in Mkn 421we can rule out variability at the 5%level with a confidencelevel greater than 3σfrom ∼500s down to timescalesof ∼11s.(Note:The kink at 32s is due to only two of the four ASCA instruments being used below this timescale because the two Solid State Spectrome-ters have less timing resolution than the two Gas Imag-ing Proportional Counters).For Mkn 501,for whichmuch less data are available,we can rule out variabil-ity at the 10%level with a confidence level greater than 3σfrom ∼400s down to timescales of ∼32s.Figure 2:Open circles show the Excess Pair Fraction (EPF)computed from all the ASCA Mkn 501observations (see text).3EPF applied to γ-ray data TeV data are recorded by the Whipple ˆIACT uses the well-established Extensive Air (EAS)technique to indirectly observe very high ergy γ-rays from sources such as AGN jets and nova remnants (Cawley et al.,1990).Most showers are due to the collision of high energy mic rays (hadrons or leptons),therefore the ray data,as observed by the IACT,have a very signal to noise ratio.Parameter cuts are used to inate most of the noise whilst retaining at least of the signal.The TeV data are typically ∼28minute intervals,at different elevations and various weather conditions.Only those data sets the most stable raw count rates were used.The data were taken from the period April 1996-May during which time the most significant flaring event ever seen at this energy was observed.The Mkn501data were taken from the period April 1997-May 1997,dur-Figure 3:Open circles show EPF for Whipple Mkn421data.The solid line is the theoretical EPF expected from a constant source and dashed lines are the theoretical EPF for 5%and 10%random amplitude variability.ing which time this AGN was at its most active since its discovery at this energy in 1995.The presence of strong flaring within these data is important for statistical purposes as the count rate from the AGN increases from typically ≤1γmin −1to ∼10γmin −1during a strong flaring state.Fig.(3)shows EPF for Mkn421.Approximately 1760minutes of data are used in this analysis at TeV en-ergies.None of the data points are greater than3σthat of a theoretical constant source with the samecount rate.Because of the28minute data binningtimescales less than14minutes can be consideredthis technique.Theγ-ray rate(∼3γmin−1)us probing timescales much lower than∼200Fig.(4)shows EPF for Mkn501with1900minutes of data.Again none of the dataare greater than3σfrom that of a constant sourcethe same mean count rate indicating a lack ofvariability in this source below∼10mins.Here theis∼9min−1and so the lower timescale belowthe statistics become too poor is∼100seconds.we can rule out variability in both sources belowminutes at a confidence level>3σat the10%variabil-Figure4:EPF for Whipple Mkn501data.ity level.4Discussion and ConclusionsOur results thus far show that there appears to be no statistically significant variability in either of the two AGN,below timescales of∼600s.From these results we conclude that either the statistics of the present data are insufficient to establish significant variability or the data does not vary on these timescales.If we assume that the source is not varying on timescales shorter than10minutes then we can estimate the minimum size of the emission region:the Doppler beaming factor is given by D=1/(1+z)Γb(1−βcosθ)where z is the redshift of the source,Γb is the Lorentz boost,β=v′/c(where v′is the velocity of the emitting region)and θis the angle between the jet axis and the observer.Causal arguments require that the size of the emission region R em is constrained to satisfyDR em≤cδtYaqoob,T.,et al.1997,ApJ490,L25 Yaqoob,T.,et al.1998,ApJ505,L87。