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改进型中心引力优化CFO算法研究

改进型中心引力优化CFO 算法研究孟　超1,孙知信1,2(1.南京航空航天大学计算机科学与技术学院,江苏南京　210016;2.南京邮电大学宽带无线通信与传感网技术教育部重点实验室,江苏南京　210003) 摘　要:　中心引力优化算法(Central Force Optimization )是一种新型的基于天体力学的多维搜索优化算法.这是一种确定性的优化算法,该算法利用一组“质子”在引力作用下的运动,搜索决策空间最优值.但该算法仍然有局部收敛的特点.本文对该算法中质子运动方程做了分析研究,利用天体力学中的摄动理论对算法进行了改进,给出了改进后的新的CFO 算法的迭代公式,并且对新的公式进行了分析.最后实验结果表明针对CFO 算法的摄动改进可以使得搜索质子跳过CFO 空间中的局部解,使得算法收敛精度和速度都有了不同程度的提高.关键词:　质子;中心引力优化;确定性算法;摄动理论中图分类号:　TP393 文献标识码:　A 文章编号:　0372-2112(2014)01-0089-07电子学报URL :http ://www .ejournal .org .cn DOI :10.3969/j .issn .0372-2112.2014.01.014Research on Improved Central Force Optimization AlgorithmMENG Chao 1,SUN Zhi -xin 1,2(1.C olle ge of C omputer Scie nce and Tec hnol ogy ,Nanjing Univer sity of Ae ronautics and Astr onautics ,Nanjing ,Jiangsu 210016,C hina ;2.Key Laboratory of Broadband Wir eles s Communic ation and Se ns or Net work T echnol ogy ,Nanjing Unive rs ity of Posts and T elec ommunications ,Ministr y of Education ,Nanjing ,Jiangs u 210003,C hina )Abstract :　Central Force Opti mization (CFO )is a new deterministic multi -dimensional search metaheu ristic based on the metaphor of g ravitational kinematics .CFO is a deterministic algorithm that explores a decision space by “flying ”a group of probes who se trajectories are governed by Newton ′s laws .But it may be local trapping .This paper makes a thorough research on the probes move governed by the equations of gravitational motion throug h the Celestial Mechanics ,establishing the relatio nship between CFO algo rithm and Celestial Mechanics ,using the perturbation theory of Celestial Mechanics to improve CFO algorithms and deducing the new iterative equation .Finally ,simulation results show that CFO based on pertu rbatio n theory avoid s local trap .The enhanced algo -rithm has great advantage of convergence property and ro bu stness compared to stochastic algorithms .Key words :　probe ;central fo r ce optimization ;deterministic algorithm ;perturbation theory1　引言中心引力优化算法＊(Central Forc e Optimization )是一种确定性的启发式算法,该算法可以实行多维全局的优化搜索[1].算法来源于万有引力动力学.目前几乎所有的启发式的优化算法都是基于某种自然界的隐喻,比如近几年刚出现的GSO 算法[2],该算法是一种全新的最优化算法,很好地运用了动物种群搜寻资源行为的特性———一个或若干个带领群体的首领(Produc er ),跟随首领并分享搜寻成果的大多数群体成员(scr ounger ),以及部分脱离群体的“掉队者”(ranger ),此算法对最优化问题中的各变量进行描述,并对寻优方式进行模拟.其他还有一些常见的优化算法比如:粒子群优化算法[3,4]和蚁群优化算法[5～7].以上这些算法都会有一些随机的特征.随机优化算法的每一次的运行都会得到不同的结果.CFO 算法是一种确定性的优化算法,因为万有引力规则和物理的现象都是确定性的.确定性的算法有着许多的优点,比如该算法可以采用确定性的数据.算法每一次执行,同样的起始数据可以得到同样的结果,所以该算法执行期间可以使用反馈的方收稿日期:2012-11-22;修回日期:2013-04-21;责任编辑:李勇锋基金项目:国家自然科学基金(No .60973140,No .61170276);江苏省高校自然科学研究重大项目(No .12KJ A520003);江苏省自然科学基金(No .BK2009425)＊此算法最近几年才出现,还没有相关的中文论文,笔者根据算法来源于天体的万有引力,所以暂译为中心引力算法.第1期2014年1月电子学报ACTA ELECTRONICA SINICA Vol .42　No .1Jan .　2014法实时调整参数.CF O 算法通过一组质子在决策空间搜索最优值.质子类似于粒子群算法的粒子和蚁群算法的蚂蚁,但搜索方式有相当大的不同.CF O 算法是由R A For mato 在2007年最先提出的.目前的研究处于初级阶段.文献[1]最先提出了基本CF O 算法,指出在三维空间中在大质量天体的轨道周围聚集小的天体,由此作者提出了CF O 算法,并将该算法应用于一些电磁学优化装载问题,取得了一些成果.文献[8]将算法应用到训练人工神经网络对数据分类,并且与P SO 训练进行比较,文中采用了三种模型,采用了Iris 数据作为训练和测试集,最后结论CFO 算法是一种新的启发式算法,性能优越.文献[9]将该算法应用于管道的泄漏和摩擦因子校准的问题上,结果说明该算法有效.文献[10]将算法与Nelder -Mead 算法相结合提出了混合型算法,取得好的效果.该算法在其他研究领域也有了一定的应用[11,12].文献[13]提出了改进的方案,利用算法对初始参数敏感的特点,在初始利用质子不同的分布,多次的运行,得到最好的结果.但是改进后,并没有避免局部收敛性.目前还没有避免局部收敛的改进算法.与其他的算法不同,CF O 在搜索过程中,当质子“陷入”局部最优时,由于引力作用,可将其从局部最优中“拉出”,继续搜索.在搜索中,这个过程会不断反复,收敛速度和精度受到影响.所以对算法进行改进,避免质子陷入局部最优“陷阱”,显得很重要.本文利用天体力学的摄动理论对CFO 算法局部收敛性能进行了改进,给出了改进后的新的迭代模型,并对新的模型进行了分析,指出了质子在摄动力存在下的运动规律,并说明正是由于摄动力的存在,产生了摄动增量使得质子跳过局部最优“陷阱”,继续在全局范围搜索.实验结果说明了质子跳过局部最优“陷阱”,可以有效的提高算法收敛的速度和精度.2　基本C FO 优化算法 CF O 算法[1]定位目标函数f (x 1,x 2,…,x N d )全局最大值在空间区域Ψ:x mi n i ≤x i ≤x max i ,1≤i ≤Νd ,x i 是N d 维空间的变量,i 是坐标数,Νd 是决策空间的维数.算法有两个运动方程式(1)和(2).a p j -1=G ∑N pk =1,k ≠pU (M k j -1-M p j -1)·(M k j -1-M p j -1)α ×(R k j -1-R pj -1)R k j -1-R p j -1β(1)R p j =R p j -1+a pj -1,j ≥1(2)a p j -1是p 在第j -1步的加速度,R p j=∑N dk =1x p,jk e k 是质子在第j 步的位置向量.x p ,jk 是它在第k 维的坐标,e k是沿x k 轴的单位向量.N p 和N t 代表总质子总数和总共的迭代步数.M p j -1=f (x p ,j -11,x p ,j -12,…,x p ,j -1Nd)是p 在迭代步j -1时的目标函数值.U 是一个单步函数,U (z )=1,z ≥00,other wise,α和β分别是1和3,为了计算方便β取值1.“质量”是一个根据质子位置变化的自定义的值.具体算法参见文献[1].3　CFO 算法的摄动改进 CFO 算法的核心是对于加速度a 的求解.对于加速度的进一步的分析,详见参考文献[1].假设CFO 空间是由(e 1,e 2,…,e N d )这组两两正交的单位向量所张成一个N d 维空间.(x 1,x 2,…,x N d )为质子P i 在CF O 搜索空间中的坐标,在CF O 算法中,质子组中任意一个质子P i 受到了目标函数值大的其余质子的引力的合力作用,此合力称为中心体的引力.另外,在假设CFO 决定空间中还存在另一附加质子q 质量为m q ,q 对于CFO 空间中质子组存在万有引力,这样与摄动理论保持一致.设质子除了受到中心体的引力外还受到附加质子q 的引力F ,称为质子受到了摄动力F 的作用.由于有摄动力的存在[14,15],使得任一质子在CF O 空间中存在向着摄动力方向的加速度,则相应的运动方程都有如下形式x =x 0+δx ,其中x 0是质子在无摄动力存在下坐标,δx 是摄动增量,此摄动称为坐标位置摄动.于是质子在第j 步的位置向量为:R p j=∑N dk =1(x p,jk +δx )e k ,其中参数定义如式(1).由于有摄动力产生了摄动增量δx ,当质子遇到局部最优的“陷阱”时,此增量可以将使得质子绕过局部最优,继续在全局范围搜索.下面将推导得出摄动力存在下的质子加速度公式.在CFO 的Νd 维空间中质子q 对于p i 的距离和质子组中任两个质子的距离是式(3)和(4).r qi =(∑N dk =1(x q k -x i k )2)12(3)r i j =(∑N d k =1(x i k -x j k )2)12(4)其中x i k 表示质子i 的第k 维坐标.x q k 表示质子q 的第k维坐标.质子q 受到质子组的引力式(5).90 电子学报2014年在文献[1]都是采用了probe ,即探测器,笔者根据天体力学的一些理论暂译为质子,即有质量的粒子.F q k=G x i k -x q kr 30im q m i 1(5)其中F qk 表示质子q 的第k 维的引力分量.式(5)是质子组中任意一个质子i 对附加质子q 的万有引力的第k 维分量,则根据牛顿第二运动定律整个质子组中所有质子对附加质子q 的引力和,所产生的附加质子q 的加速度为式(6):m q ¨x qk =G ∑N pi =1m q m i (x q k -x ik )r 30i(6)其中¨x qk表示质子q 第k 维的坐标分量对于时间t 的二阶导数,也就是加速度第k 维的分量.式(11)右边表示的质子组中所有质子对附加质子引力的合力,所以用了求和的符号.式(11)左边是牛顿第二运动定律.同理对于质子组中任意一个P i 受到其余质子的引力和q 质子的共同作用,其运动方程式(7).m i ¨x i k=Gm q m i (x q k -x ik )r 3q i +G∑N pj =1m i m j (x i k-x j k )r 3i j,(j ≠i )(7)现在作坐标平移,将坐标原点平移到q 点,令在新的坐标系中坐标分量用X 表示,则质子组中第i 个质子k 维在新的坐标系中存在如下关系:x i k =x q k +X ik(8)其中X i k 是第i 个质子在新的坐标系下的k 维坐标.用式(6)两边消去m q ,式(7)两边消去m i ,代入式(8)得到: ¨X ik =¨x ik -¨x q k =-G (m q+m i )x ik r 3q i+G ∑N pj =1,j ≠im j (x i k -x j k r 3ij -x j kr 3qj )(9)其中r qi 和r q j 是质子i 和j 到坐标原点的距离.m 的定义与CFO 算法的定义保持一致.即M ASS CFO =U (M k j -1-M p j -1)·(M k j -1-M p j -1)α,α=1,则m i =0,m j =M ASS CFO .根据牛顿第二运动定律,式(6)描述了附加质子的运动规律,式(7)描述了质子组中任意质子的运动规律.当坐标发生平移以后,新旧坐标符合式(8),将其两边求二阶导数,并将式(6)和(7)两边消去质量,再分别代入式(8),并令m q 是单位质量的质子为1,得到式(9).为了与式(1)的表示方法统一,将式(9)的i 和j 换成k 和p 得质子摄动力存在下,在CFO 算法空间迭代式(10)a p j -1=-GR p j -1R p j -1β+G∑N pk =1,k ≠pU (M kj -1-M pj -1)·(M kj -1-M pj -1)α·(R k j -1-R pj -1R k j -1-R pj -1β-R k j -1R kj -1β)(10)其中β=3.利用式(10)可以将基本CFO 迭代算法修改,修改后的算法如下:1.初始时,质子还未开始运动是静止状态,设置a p j -1=0,质子初始位置向量是随机的.2.计算初始时的质子对应位置的目标函数值.3.For j =0to N t·For p =1t o N p如果有质子超出上界或是下界,则进行质子位置的调整[1];·For p =1t o N p式(1)计算加速度,计算质子的位置R p j =R p j -1+a pj -1用式(10)重新计算加速度,计算质子新位置R p ,/j =R p j -1+a p j -1,即在摄动力存在下的新位置;计算这两个位置的目标函数值M p j =f (R p j )和M /p j =f (R p /j ),若M /p j >M p j ,则R p j =R p ,/j .CFO 算法始终是求搜索空间中的最大值,这一点与其他的随机优化算法有区别.4　质子的摄动运动方程分析附加质子q 质子提供的摄动力,让质子组中的质子运动产生了摄动增量δx .正是由于摄动力产生了摄动增量δx ,此增量可以将使得质子绕过局部最优的陷阱中,继续让质子在全局范围搜索.本节进一步从理论上证明δx 的存在.由于直接讨论质子的坐标摄动比较困难,所以本文从质子坐标相关的量,质子运动轨道根数的角度,来讨论轨道根数的摄动.最后推导得到一个与质子运动方程x =x 0+δx 相似的一个基于轨道根数的运动方程,证得δx 的存在.介绍轨道根数的定义前,先介绍一个定理.任一质子运动方程符合下列的偏微分方程[16,17],N d 是CFO 空间维数. St+H (X 1,X 2,…,X N d ;S X 1, S X 2,…, SX N d,t )=0(11)其中X r (r =1,2,…,N d )是质子在N d 维空间中的坐标,H 是汉米尔顿函数,式(16)的特征方程是:﹒X r = H Y r ,﹒Y r =- HX r(r =1,2,…,N d )(12)其中﹒X 为X 对时间t 的一阶导数.定理　如果函数S =S (X 1,X 2,…,X N d ,t ,α1,α2,…,αN d )+α,是偏微分方程式(11)的任意一个全积分,(α1,α2,…,αN d )为任意相互独立的积分常数,则有关系式:91第　1　期孟　超:改进型中心引力优化CFO 算法研究βr = S αr ,Y r = S X r (r =1,2,…,N d ),所确定的函数:X r =X r (t ,α1,α2,…,αN d ;β1,β2,…,βN d )Y r =Y r (t ,α1,α2,…,αN d ;β1,β2,…,βN d )为特征方程式(16)的解,其中βr 也是相互独立的积分常数.定理说明CFO 空间迭代过程中,对于任意一个质子位置可以通过其2＊N d 个积分常数来确定.当有了摄动力以后,对于坐标位置的摄动,可以通过积分常数的摄动来研究.在天体力学中积分常数,称为轨道根数,本文中CF O 算法中的质子运动和天体力学中质子运动相似,所以仍然采用天体力学的称谓,即轨道根数的摄动.轨道根数的摄动运动方程和坐标摄动运动方程x =x 0+δx 相类似,相应的摄动增量为δc .本节将研究CFO 算法中任一质子的2＊N d 个轨道根数所产生的摄动.以下用c pi 表示任意一个质子第i 个轨道根数,c qi 表示附加质子的第i 个轨道根数,考虑质子组中任一质子其质量为m ,附加质子质量m q ,则在CFO 算法中任一质子的受摄运动方程有以下形式:﹒c pi =m q F i (c p ,c q ,t )﹒c qi =mG i (c p ,c q ,t )(13)函数F i ,G i 中的c q 和c p 表示所有的轨道根数.对质子摄动方程的研究,就是要解式(13),2＊N d 阶常微分方程的问题.本文将主要研究质子组中任一质子轨道根数,即c pi .将式(13),按照m 幂级数展开为:c pi =c (0,0)pi +mc (1,0)p i +m q c (0,1)pi +m 2c (2,0)pi +mm q c (1,1)pi +m 2q c (0,2)pi +m 3c (3,0)pi……c qi =c (0,0)qi+mc (1,0)q i+m q c (0,1)q i+m 2c (2,0)qi+mm q c (1,1)qi +m 2q c (0,2)qi +m 3c (3,0)qi (14)其中c j ,k pi 为展开式中的系数,只要求出这些系数,代入式(14),就可以得到摄动运动方程的解,证得轨道根数摄动增量δc 存在性,进一步也可以证得坐标摄动增量δx 存在.将函数F i ,按照二重马克劳林级数展开为:F i =(F i )0+m ( F i m )0+m q ( F im q )0+12[m 2( 2F im 2)0+2mm q ( 2F i m m q )0+m 2q ( 2F i m 2q)0]+……(15)其中(X )0表示在X 中求过导数后再令m =m q =0.但是m 和m q 隐含在c pi ,c qi 中.它们已经展开为m 和m q 的幂级数的形式,即式(14).因此有F im =∑2＊N ds =1( F i c p s c p s m + F i c qs c qsm ) F iq=∑2＊N ds =1( F i ps c p s q + F i q s c q s q )对m 和m q 的高次的微商也可以进行下去.由上式可得:( F i m )0=∑2＊N ds =1[( F i c ps )0c (1,0)ps +( F i c qs )0c (1,0)qs ]( F i m q )0=∑2＊N ds =1[( F i c ps )0c (0,1)p s +( F i c qs )0c (0,1)qs ]代入式(14)可得: F i (c p ,c q ,t )=(F i )0　+m ∑2＊N ds =1[( F i c ps )0c (1,0)ps +( F ic q s )0c (1,0)q s]　+m q ∑2＊N ds =1( F i c ps c ps m q + F i c q s c qsm q )+ (16)以式(14)和式(16)代入式(13)两端可得:﹒c (0,0)pi +m ﹒c (1,0)pi+m q ﹒c (0,1)p i+m 2﹒c (2,0)pi+mm q ﹒c (1,1)pi +m 2q ﹒c (0,2)pi +m 3﹒c (3,0)pi…=m q (F i )0+mm q ∑2＊N ds =1[( F i c p s )0c (1,0)p s +( F i c qs )0c (1,0)qs ]+m 2q∑2＊N ds =1( F i ps c ps q + F i qs c q sq )+ (17)比较两端m 和m q 的同次幂系数可得:﹒c (0,0)p i=0,(18)﹒c (1,0)pi=0﹒c (0,1)pi =(F i )0=F i (c (0,0)p,c (0,0)q ,t )(19)﹒c (2,0)pi=0﹒c (1,1)pi =∑2＊N ds =1[( F i c ps )0c (1,0)ps +( F ic qs)0c (1,0)qs ]﹒c (0,2)pi=∑2＊N ds =1[( F i c ps )0c (0,1)ps +( F i c qs )0c (0,1)qs ](20)高次项可以一直推导下去,可得一般项的表达式.由式(18)得到c (0,0)pi应为常数相对于m q 等于0的情况,即附加质子质量为0为无摄运动.简记为:c (0)pi ,一共有2＊N d 个.在式(19)中c pi 的展开式中为m q 的一次幂系数,是轨道根数的一阶摄动.其中除了等于0者以外,还有i .﹒c (0,1)pi =(F i )0=F i (c (0)p ,c (0)q ,t )(21)其中(F i )是关于轨道根数的已知函数[16,17],上式表明,在(F i )中,质子的轨道根数都用初值,应为常数,(F i )可以表示为时间的显函数,可以直接积分求出c (0,1)pi.因此一阶摄动,结果为92 电子学报2014年c pi =c (0)pi +m q c (0,1)pi(22)含有二次幂因子的项,叫做二阶摄动,即在式(20)中的项.除掉等于0的因子外,还有:﹒c (1,1)pi=∑2＊N ds =1( F ic q s )0c (1,0)q s﹒c (1,2)pi =∑2＊N ds =1( F i c p s )0c (0,1)p s (23)其中c (0,1)ps 和c (1,0)q s已经求得,其余各量为已知,积分得二阶摄动结果为:c pi =c (0)pi +m q c (0,1)pi +mm q c (1,1)pi +m 2q c (0,2)p i(24)在一阶摄动下,令δc 1=m q c (0,1)p i ,二阶摄动,令δc 2=m q c (0,1)pi +mm q c (1,1)pi +m 2q c (0,2)pi得到摄动方程c pi =c (0)p i +δc 1(25)和c pi =c (0)p i +δc 2(26)其中c (0)pi 为未施加摄动力的轨道根数,也是算法改进前的轨道根数.式(25)和(26)说明改进后的摄动力存在下的轨道根数等于无摄运动轨道根数与摄动增量的和.正是由于摄动增量的存在使得质子的运动在原来基础上产生了偏移,跳过了局部最优的“陷阱”.式(25)和(26)与摄动运动方程x =x 0+δx (其中x 0是质子在无摄动力存在下坐标,δx 是摄动增量)存在一致性,由定理中的公式及其轨道根数摄动运动方程,可以通过δc 相应的推导出坐标摄动δx .相关的推导过程和坐标摄动的概念,可以参见文献[17,18].5　实验结果及其性能分析下面通过几个benchmark 函数优化问题测试本文算法的性能,并与文献[2,3,4,19]标准粒子群算法(SP -SO ),种群优化算法(GSO ),和标准CFO 算法(SCF O )的测试结果进行比较.摄动CF O 简写为DCFO .最后比较的结果说明了DCF O 算法在收敛精度和收敛速度方面要优于其他的随机优化算法.本节通过matlab 对几种benchmark 函数进行仿真实验.这几种函数分别为:f 1=-∑N di =1(x 2i -10c os (2πx i )+10),-5.12≤x i ≤5.12f ＊max=0f 2=-∑N d -1i =1[100(x i +1-x 2i )2+(x i -1)2],-30≤x i ≤30f ＊max =0f 3=∑N di =1[x i sin θ(x i )],-500≤x i ≤500f ＊max (10)=4189.8　f ＊max (30)=12569.5f 4=20exp (-0)]-20-e ,-32≤x i ≤32f ＊ma x =0其中f 3函数在10维和30维的最优值为4189.8和12569.5.仿真实验的结果如表1所示.表中部分数据来源于文献[2,3,4,19],参数设置同参考文献.Optimal f 表示理论上的函数全局最大值CFO 算法是搜索目标函数的全局最大值,这点与其他优化算法有区别,所以在测试函数前都添加了负号.Dim 表示维数.由于表1中其他的算法都是随机优化算法,要求统计意义的性能评估,所以表1中是算法运行100次的平均值.而CF O 算法是确定性的算法,同样的初始参数,运行多次都有同样的结果.表1　几个benchmark 函数的算法比较DI SPSO GSO SCFO DCFO f 110-0.5632-1.2457-0.343-1.34e -330-20.7863-1.0179-3.52e -6-2.34e -8f 210-5.5960-0.3254-0.1245-0.113e -230-37.3852-49.8359-2.172e -2-4.212e -4f 3102635.6134181.2314190.2554189.7412306703.61212569.4912569.4912569.501f 410-2.633e -4-3.255e -3-0.5e -5-0.12e -830-1.340e -3-2.655e -5-1.5e -7-3.43e -10 由表1中还可以看出在相同的维数条件下,标准粒子群算法SPSO 容易陷入局部最优陷阱,种群优化算法GSO 算法寻优能力很强比标准粒子群算法有了明显的改善,但是结果不如SCFO 更接近理论值.DCFO 算法和标准SCFO 算法相比寻优结果有了一些改进,这说明摄动增量的存在使得质子跳过局部最优值,保持了质子的多样性,让更多的质子在全局空间进行搜索.下面以f 3为例说明优化结果.函数全局最优值是12,569,在x i =420.9687,i =1,…,30,函数的最优值分布在决定空间的主对角线上,初始化时质子应该尽量的远离对角线,以便于质子可以在决定空间中尽可能的搜索.图1显示了各种算法在维数为30,且CF O 的质子数为240,PSO 的粒子数为30时的收敛轨迹.从图1中可以看出DCFO 算法在迭代5次内fitness 会明显提93第　1　期孟　超:改进型中心引力优化CFO 算法研究高,大约在迭代6次时接近最优值,而在迭代50次以后fitness 增加缓慢,所以在图1中只绘出50次以内的最优值变化情况.而其他的算法,标准PSO 算法会陷入局部最小错过全局最优值,种群优化算法GSO 收敛速度缓慢,标准CFO 算法收敛速度有所改善,但是比起DCF O 收敛要慢,这主要是因为标准CFO 算法在迭代过程中,质子会不断的陷入局部最优的“陷阱”中,接着通过质子间万有引力的作用将部分质子“拉出”,这从一定程度上影响了收敛的速度,而摄动理论的引入,可以帮助改善这种问题.由于摄动增量的存在使得质子直接跳过局部最优,提高了收敛的速度.衡量质子是否收敛到最优解的参数是质子与最优质子间的平均距离.D av g =1L ·(Νp -1)∑N pp =1∑N di =1[x p,j i -x p ＊,j i ]2(27)其中x p ,ji表示质子p 在第j 次迭代中第i 维的坐标值,p ＊表示j 次迭代中最优质子.另外L =∑N di =1(x max i -x min i )2(28)这是搜索空间主对角线的长度,x min i ≤x i ≤x maxi ,i =1,…,N d ,x mi n i 和x max i 是第i 维坐标的最大值与最小值.在CFO 算法中平均距离D av g 随迭代次数变化的曲线如图2所示,平均距离有波动性,总是不断的上下振动.平均距离不断的上下波动性说明了CFO 算法迭代中质子陷入局部最优的“陷阱”,然后在质子间万有引力的作用下又不断的从局部最优中被“拉出”,继续在全局范围搜索最优解.对其他几个函数的实验同样可以得到类似的结论.图3是DCFO 算法平均距离曲线,由于摄动增量存在使得质子可以绕过其中一些局部最优值,所以曲线的波动有了相当的改善.避免质子陷入局部最优的“陷阱”,有效的提高了搜索的速度和精度.在平均距离公式中有个L 的因子,这相当于搜索空间主对角线的长度,在迭代的后期各个质子距离越来越近,主对角线的长度越来越小,分母的减小直接导致了最后平均距离值的增加.所以在迭代后期平均距离曲线的升高.目前还没有理论来解释D a vg 曲线的这一现象,然而D a vg 的曲线和天体中的ΔV 曲线很相似,参见文献[20],该文献详细比较了D avg 和ΔV 曲线的相似性.CFO 算法通过天体数学分析理论推导得出,所以两者间会有相似.这也说明了,天体力学中的理论可以应用在算法中.6　结论本文针对CFO 算法局部收敛的问题,根据天体力学摄动理论对CFO 迭代算法进行了改进,利用摄动增量的作用使得质子跳过了一些局部最优解,从一定程度上提高了算法收敛的速度和精度,最后的仿真实验表明基于摄动的CFO 算法与标准CFO 算法相比,收敛结果更精确,收敛速度更快.说明利用摄动理论的改进是行之有效的方法.参考文献[1]R A Formato .Central force optimization :A new metaheuristicwith applications in applied electromagnetics [J ].Progress in Electromagnetics Research PIER ,2007,77:425-449.[2]S He ,et al .Group search opti mizer :An opti mization algorithminspired by animal searching behavior [J ].IEEE Transactionson Evolutionary Computation ,2009,13(5):973-990.[3]吴晓军,杨战中,赵明.均匀搜索粒子群算法[J ].电子学报,2011,39(6):1261-1266.WU Xiao -jun ,YANG Zhan -zhong ,ZHAO Ming .A uniform searching particle swarm optimization algorithm [J ].Acta Elec -tronica Sinica ,2011,39(6):1261-1266.(in Chinese )[4]申元霞,王国胤,曾传华.相关性粒子群优化模型[J ].软件学报,2011,22(4):695-708.SHEN Yuan -Xia ,WA NG Guo -Yin ,ZENG Chuan -Hua .Correl -ative particle swarm optimization model [J ].Journal of Soft -ware ,2011,22(4):695-708.(in Chinese )[5]苏兆品,蒋建国,梁昌勇,张国富.蚁群算法的几乎处处强收敛性分析[J ].电子学报,2009,37(8):1646-1650.SU Zhao -pin ,JIANG Jian -guo ,L IANG Chang -yong ,Z HAN G Guo -fu .An almost everywhere strong co nvergence proof for a class of ant colony algorithms [J ].Acta Electronica Sinica ,2009,37(8):1646-1650.(in Chinese )94 电子学报2014年[6]R F Tavares,et al.An ant colo ny optimization approach to apermutational flow shop schedu ling problem with outsourcing al-lowed[J].Computers&Operations Research,2011,38(9): 1286-1293.[7]雷秀娟,等.基于连接强度的PPI网络蚁群优化聚类算法[J].电子学报,2012,40(4):695-702.L EI Xiu-juan,et al.Joint strength based ant colony optimization clu stering algorithm fo r PPI networks[J].Acta Electronica Sini-ca,2012,40(4):695-702.(in Chinese)[8]Green II,et al.T raining neural networks using central force op-timization and particle swarm optimization:insights and com-parisons[J].Expert Systems with Applications,2012,39(1): 555-563.[9]Ali Hag highi,Helena,et al.Detection of leakage freshwater andfriction factor calibration in drinking networks using central force optimizatio n[J].Water Resource Manage,2012,26:2347-2363.[10]K R Mahmoud.Central force optimization:Nelder-Mead hy-brid algorithm fo r rectangu lar microstrip antenna design[J].Electromagnetics,2011,31(8):8866-8872.[11]M J ttice vector quantization applied to speech and au-dio coding[J].ZTE Commu nications,2012,10(2):25-33.[12]S Ragot,B Kovesi,A L Guyader.Noise feedback coding re-visited:refu rbished legacy codecs and new coding models[J].ZT E Communications,2012,10(2):34-44.[13]R A Formato.Central force optimization with variable initialprobes and adaptive decision space[J].Applied Mathematicsand Computation,2011,217:8866-8872.[14]V G Szebehely.Theory of orbits-The restricted problem ofthree bodies[J].Soviet Astro nomy,1969,13(2):364-366.[15]E L Stiefel,G Scheifele.Linear and regular Celestial Mechan-ics[M].Berli n:Springer-Verlang,1971.[16]Siegel C J,Moser J K.Lectures on Celestial MechanicsSpringer[M].Berlin:Springer-Verlang,1972.[17]李林森.引力常数随时间变化对双星轨道演变的长期效应[J].天文学报,2011,52(3):242-250.LI Lin-sen.Secular effect of evolution of the orbits of binariesinduced by the variation of gravitational constant with time[J].Acta Astronomica Sinica,2011,52(3):242-250.(inChinese)[18]汤靖师,等.近地卫星运动的坐标系附加摄动在拟平均根数法中的处理[J].天文学报,2010,51(1):75-84.TANG Jing-shi,et al.T he application o f Quasi-mean-element-method to LEO under additional perturbation due to change ofcoordinate system[J].Acta Astronomica Sinica,2012,51(1): 75-84.(in Chinese)[19]刘洪波,王秀坤,谭国真.粒子群优化算法的收敛性分析及其混沌改进算法[J].控制与决策,2006,21(6):636-640.LIU Hong-bo,WANG Xiu-kun,T AN Guo-zhen.Convergenceanalysis of particle swarm opti mization and its improved algo-rithm based on chaos[J].Control and Decision,2006,21(6): 636-640.(in Chinese)[20]Valsecchi,G B,et al.Reso nant returns to close approaches:analytical theo ry[J].Astronomy&Astrophysics,2003,408(3):1179-1196.作者简介孟　超　男,1977年生于江苏镇江,南京航空航天大学计算机科学与技术学院博士研究生.研究方向为智能计算、云计算.孙知信(通信作者)　男,1964生于安徽宣城,教授、博士生导师、江苏省云计算专家委员会委员,主要研究领域为网络安全.E-mail:sunz x@nj 95第　1　期孟　超:改进型中心引力优化CFO算法研究。

Sweden：LPJ-GUESS

Sweden: LPJ-GUESSThe name of the group: Department of Physical Geography and Ecosystem AnalysisName, title and affiliation of Principal investigator: Assoc. Prof. Almut Arneth; Assoc. Prof. Ben Smith Name and affiliation of contact point, incl. detailed address: Almut Arneth & Ben Smith, INES,Sölvegatan12,22362Lund,*****************************.se;*******************.seURL: http://www.nateko.lu.se/INES/Svenska/main.aspPartner institutions: PIK, SMHI/EC-EARTHWhich components:land sfc yesatmospheric chemistry yesProject Description:LPJ-GUESS (Smith et al., 2001; Hickler et al., 2004) is a generalized, process-based model of vegetation dynamics and biogeochemistry designed for regional to global applications. It combines features of the widely used Lund-Potsdam-Jena Dynamic Global Vegetation Model (LPJ-DGVM; Sitch et al., 2003) with those of the General Ecosys-tem Simulator (GUESS; Smith et al., 2001) in a single, flexible modeling framework. The models have identical representations of ecophysiological and biogeochemical processes, including the hydrological cycle updates d e-scribed in Gerten et al. (2004). They differ in the level of detail with which vegetation dynamics and canopy stru c-ture are simulated: simplified, computationally efficient representations are used in the LPJ-DGVM, while in GUESS a "gap-model" approach is used which is particularly suitable for continental to regional simulations. Tepre-sentations of stochastic establishment, individual tree mortality and disturbance events ensure representation of suc-cessional vegetation dynamics which is important for vegetation response to extreme events.LPJ-GUESS models terrestrial carbon and water cycle from days to millennia (Sitch et al. 2003; Koca et al. 2006; Morales et al. 2005, 2007) and has been shown to reproduce the CO2 fertilisation effects seen in FACE sites (Hickler et al. in press). It has been widely applied to assess impacts on carbon cycle and veg etation based on scenarios from climate models (Gritti et al. 2006; Koca et al. 2006; Morales et al. 2007; Olesen et al. 2007). In addition it has several unique features that are currently not available in any of the Earth System Models:(1) A process-based description for the main biogenic volatile organic compounds (BVOC) emitted by vegetation. BVOC are crucial for air chemistry and climate models, since they contribute to formation and destruction of trop o-spheric O3 (depending on presence and absence of NOx), constrain the atmospheric lifetime of methane, and are key precursors to secondary organic aerosol formation. LPJ-GUESS is the only land surface model with a mechanistic BVOC representation that links their production to photosynthesis. It also uniquely accounts for the recently disco v-ered direct CO2-BVOC inhibition which has been shown to fundamentally alter future and past emissions compared to empirical BVOC algorithms that neglect this effect (Arneth et al., 2007a,b). (2) The possibility to simulate past and present vegetation description on a tree species (as well as PFT) level (Miller et al., in press, Hickler et al., 2004). This is crucial for simulations of BVOC and other reactive trace gases and allows for a much better represe ntation of vegetation heterogeneity in regional and continental atmospheric chemistry-climate studies (Arneth et al., 2007b), an important aspect since spatial heterogeneity must be accounted for with atmospherically reactive chemical species. (3) LPJ-GUESS accounts for deforestation by early human agriculture throughout the Holocene and the effects on global carbon cycle and atmospheric CO2 concentration (Olofsson & Hickler 2007). We currently investigate the impor-tance of Holocene human deforestation on BVOC and fire trace gas and aerosol emissions, and how these may affect Holocene CH4 levels, and simulations of pre-industrial O3. (4) LPJ-GUESS accounts for deforestation by early human agriculture throughout the Holocene and the effects on global carbon cycle and atmospheric CO2 concentra-tion (Olofsson & Hickler 2007). We currently investigate the importance of Holocene human deforestation on BVOC and fire trace gas and aerosol emissions, and how these may affect Holocene CH4 levels, and simulations of pre-industrial O3. (5) A novel global process-based fire description, SPITFIRE (Thonicke et al., 2007) has been incorporated; it is currently used to study effects of climate change and of human vs. natural ignition on carbon cycle and trace gas emissions in savanna ecosystems. (6) Prognostic schemes for agricultural and forest land use that p a-rameterise farmer and forestmanagement decisions under changing climate and productivity. The agricultural scheme has been implemented and applied at the global scale (Bondeau et al. 2007), the forest management scheme in a prototype form for Sweden (Koca et al. 2006). (7) Incorporation of a permafrost module, wetland processes and methane emissions, as well as vegetation nitrogen cycle is in progress.The vegetation dynamics module of LPJ-GUESS has been coupled to the land surface scheme of the Rossby Centre regional climate model RCA3 (Jones et al. 2004a,b) and is being applied to investigate biophysical feedbacks of land surface changes on climate at the regional scale in Europe. The above listed process descriptions are also applicable and available to global ESMs.(References available on request)。

线粒体融合和裂变失衡英文

线粒体融合和裂变失衡英文Mitochondrial Fusion and Fission Imbalance.Mitochondria are dynamic organelles that undergo continuous fusion and fission events. These processes are essential for maintaining mitochondrial morphology, function, and quality control. Fusion allows mitochondria to exchange genetic material and proteins, thereby promoting complementation and repair. Fission, on the other hand, enables the segregation of damaged mitochondria for subsequent removal by mitophagy.An imbalance between mitochondrial fusion and fission can lead to various cellular abnormalities and diseases. Excessive fusion can result in the formation of hyperfused mitochondrial networks, which may impede mitochondrial motility and hinder the efficient distribution of mitochondria to subcellular compartments. Conversely, excessive fission can lead to mitochondrial fragmentation, which may compromise mitochondrial function and increasethe susceptibility to mitophagy.Causes of Mitochondrial Fusion and Fission Imbalance.Several factors can disrupt the balance between mitochondrial fusion and fission, including:Mutations in mitochondrial fusion and fission genes: Mutations in genes encoding mitochondrial fusion proteins (e.g., Mfn1, Mfn2, OPA1) or fission proteins (e.g., Drp1, Fis1) can impair their function and lead to an imbalance in fusion and fission events.Oxidative stress: Excessive reactive oxygen species (ROS) production can induce mitochondrial fission by activating Drp1 and inhibiting Mfn2.Calcium overload: Elevated intracellular calciumlevels can trigger mitochondrial fission by activating calcineurin, which dephosphorylates Drp1 and promotes its translocation to the mitochondria.Metabolic stress: Nutrient deprivation or hypoxia can induce mitochondrial fission to promote mitophagy and conserve energy.Viral infections: Certain viruses can interfere with mitochondrial fusion and fission processes, leading to mitochondrial dysfunction and cell death.Neurodegenerative diseases: Mitochondrial fusion and fission imbalances have been implicated in the pathogenesis of several neurodegenerative diseases, includingAlzheimer's disease, Parkinson's disease, and amyotrophic lateral sclerosis (ALS).Consequences of Mitochondrial Fusion and Fission Imbalance.Mitochondrial fusion and fission imbalance can have a range of consequences, including:Impaired mitochondrial function: Excessive fusion or fission can disrupt mitochondrial oxidative phosphorylation,ATP production, and calcium homeostasis.Increased susceptibility to apoptosis: Mitochondrial fragmentation can trigger the release of pro-apoptotic factors and promote cell death.Neurological dysfunction: Mitochondrial fusion and fission imbalances have been linked to cognitive decline, synaptic dysfunction, and neuroinflammation.Cardiovascular disease: Impaired mitochondrial fusion and fission can contribute to cardiac dysfunction and heart failure.Metabolic disorders: Mitochondrial fusion and fission imbalances have been implicated in obesity, insulin resistance, and type 2 diabetes.Therapeutic Strategies.Modulating mitochondrial fusion and fission processes holds therapeutic potential for treating a variety ofdiseases. Strategies aimed at restoring the balance between fusion and fission include:Pharmacological interventions: Small molecules that target mitochondrial fusion or fission proteins are being developed as potential therapeutic agents.Gene therapy: Gene therapy approaches aim to correct mutations in mitochondrial fusion and fission genes.Antioxidant therapies: Antioxidants can combat oxidative stress and protect mitochondria from excessive fission.Dietary interventions: Dietary modifications that promote mitochondrial biogenesis and reduce oxidative stress may improve mitochondrial fusion and fission dynamics.Conclusion.Mitochondrial fusion and fission are essentialprocesses for maintaining mitochondrial homeostasis and function. Imbalances between these processes can lead to cellular dysfunction and the development of various diseases. Understanding the mechanisms underlying mitochondrial fusion and fission imbalance and developing therapeutic strategies to restore balance hold promise for treating a range of pathological conditions.。

翻译--西医神经科术语英文翻译

西医神经科术语英文翻译以下是常见的西医神经科术语英文翻译：1. 神经学：Neurology2. 神经系统：Nervous System3. 大脑：Brain4. 脊髓：Spinal Cord5. 神经元：Neuron6. 神经胶质细胞：Glial Cells7. 突触：Synapse8. 轴突：Axon9. 树突：Dendrites10. 髓鞘：Myelin Sheath11. 神经递质：Neurotransmitters12. 神经传导通路：Nerve Conduction Pathways13. 反射：Reflex14. 痛觉：Pain Sensation15. 感觉运动传导通路：Sensorimotor Pathways16. 自主神经系统：Autonomic Nervous System17. 中枢神经系统：Central Nervous System (CNS)18. 外周神经系统：Peripheral Nervous System (PNS)19. 神经肌肉接头：Neuromuscular Junction20. 癫痫：Epilepsy21. 帕金森病：Parkinson's Disease22. 多发性硬化症：Multiple Sclerosis (MS)23. 脑卒中：Stroke24. 脑外伤：Traumatic Brain Injury (TBI)25. 脑瘤：Brain Tumors26. 脑炎：Brain Infections / Encephalitis27. 神经痛：Neuralgia28. 头痛：Headache29. 失眠：Insomnia30. 肌肉萎缩：Muscle Atrophy31. 肌无力：Muscle Weakness32. 神经根病：Radiculopathy33. 神经丛病变：Plexopathy34. 脊髓病变：Myelopathy35. 脑积水：Hydrocephalus36. 脊髓空洞症：Syringomyelia37. 脑电图（EEG）：Electroencephalogram (EEG)38. 肌电图（EMG）：Electromyogram (EMG)39. 经颅磁刺激（TMS）：Transcranial Magnetic Stimulation (TMS)40. 正电子发射断层扫描（PET）：Positron Emission Tomography (PET)41. 功能磁共振成像（fMRI）：Functional Magnetic Resonance Imaging (fMRI)42. 单光子发射计算机断层扫描（SPECT）：Single Photon Emission Computed Tomography (SPECT)43. 经颅多普勒超声（TCD）：Transcranial Doppler Ultrasound (TCD)44. 认知障碍：Cognitive Dysfunction45. 情绪障碍：Mood Disorders46. 神经退行性疾病：Neurodegenerative Diseases47. 中毒性脑病：Toxic Encephalopathy48. 脑死亡：Brain Death49. 昏迷：Coma50. 意识障碍：Disorders of Consciousness。

neutralize obsessional thought

Neutralize Obsessional ThoughtIntroductionObsessional thoughts, also known as intrusive thoughts, are unwanted and repetitive thoughts that cause distress and anxiety. These thoughts can be persistent, intrusive, and difficult to control. They often revolve around themes such as harm, contamination, or doubt. In this article, we will explore various strategies to neutralize obsessional thoughts and regain control over our minds.Understanding Obsessional ThoughtsObsessional thoughts are a common symptom of obsessive-compulsive disorder (OCD). However, it is important to note that not all obsessional thoughts indicate the presence of OCD. Temporary obsessions can occur in individuals without OCD as well. The key difference lies in the severity and impact these thoughts have on daily functioning.Obsessional thoughts can range from mild to severe and may vary in content from person to person. Some common obsessions include:1.Fear of contamination: Thoughts of getting dirty or contaminatedby germs.2.Need for symmetry: A strong desire for things to be organized orarranged perfectly.3.Intrusive violent or sexual images: Unwanted images of causingharm or engaging in inappropriate behavior.4.Religious or moral obsessions: Fear of committing a sin or goingagainst personal beliefs.Strategies to Neutralize Obsessional Thoughts1. Identify and Challenge Irrational BeliefsObsessional thoughts are often fueled by irrational beliefs and exaggerated fears. By identifying these underlying beliefs, we can challenge their validity and reduce their power over us. Some helpful techniques include:•Thought records: Write down the obsessional thought along with any associated emotions and beliefs. Then examine the evidence for and against those beliefs.•Cognitive restructuring: Replace negative or irrational beliefs with more rational ones through evidence-based reasoning.2. Practice Mindfulness MeditationMindfulness meditation involves paying attention to the present moment without judgment or attachment to our thoughts. This practice helps us observe our obsessional thoughts without getting caught up in them. Over time, mindfulness can reduce the intensity and frequency of these thoughts. Tips for practicing mindfulness meditation include:•Find a quiet and comfortable place to sit.•Focus on your breath or a specific object, bringing your attention back whenever it wanders.•Accept any thoughts or sensations that arise without judgment and let them pass.3. Exposure and Response Prevention (ERP)Exposure and Response Prevention is an evidence-based treatment for OCD that involves gradually exposing oneself to feared situations or thoughts while resisting the urge to engage in compulsive behaviors or mental rituals. This technique helps to desensitize the mind to obsessional thoughts and reduces their power. It is recommended to seek guidance from a mental health professional for implementing ERP effectively.4. Develop a Supportive RoutineCreating a structured routine can provide stability and reduce anxiety associated with obsessional thoughts. Some helpful strategies include:•Set specific goals and prioritize tasks.•Include regular exercise, healthy eating, and sufficient sleep in your routine.•Engage in activities that bring joy or relaxation, such as hobbies or spending time with loved ones.5. Seek Professional HelpIf obsessional thoughts significantly interfere with daily life, it is essential to seek professional help. A mental health professional canprovide an accurate diagnosis, offer appropriate treatment options, and guide you through the recovery process. Therapy options may include cognitive-behavioral therapy (CBT), medication, or a combination of both.ConclusionObsessional thoughts can be distressing, but they do not have to control our lives. By implementing strategies such as challenging irrational beliefs, practicing mindfulness meditation, engaging in exposure therapy, developing a supportive routine, and seeking professional help when needed, we can neutralize obsessional thoughts and regain control over our minds. Remember that recovery takes time and patience but with persistence, it is possible to overcome obsessional thoughts and lead a fulfilling life.。

分子动力学英文

The Fascinating World of MolecularDynamicsMolecular dynamics is a simulation technique used to model the behavior of atoms and molecules in different physical and chemical environments. It has become an increasingly popular method due to its ability to provide insights into the atomic-level processes that govern the behavior of materials, including biological systems. In this article, we will explore the intricacies of molecular dynamics and discuss its applications in various fields.Firstly, molecular dynamics simulations rely on the laws of physics to predict the movement and behavior of molecules in a given system. Starting from an initial configuration of atoms, the simulation calculates the forces acting on each atom and predicts their subsequent motions. This process is repeated over time, generating a trajectory of the system's evolution.Molecular dynamics simulations have proved invaluable in studying the properties of various materials, including the physical properties of proteins and DNA, the behavior of nanoparticles and polymers, and the dynamics of reactions insolution. The simulations have also led to the discovery of new materials with unique properties, such as superconductors and catalysts.The versatility of molecular dynamics simulations extends to drug discovery, where researchers use the technique to predict how drugs will interact with their target molecules. Additionally, molecular dynamics simulations have been used to design and optimize chemical processes, leading to more efficient and sustainable solutions in industrial production.However, molecular dynamics simulations are not without limitations. The calculations required for large systems can be computationally intensive, requiring significant processing power and time. Additionally, the accuracy of the simulations is limited by the quality of the force fields used to predict the behavior of molecules.In conclusion, molecular dynamics simulations provide researchers with the ability to explore the behavior of molecules in different environments and under different conditions. The technique has applications in a wide range of fields, from materials science to drug discovery and chemical engineering. While they have limitations, molecular dynamics simulations have proven to be a valuable tool in the pursuit of scientific understanding and innovation.。

The Properties of Diffusion in Liquids and Gases

The Properties of Diffusion in Liquidsand GasesDiffusion is a fundamental process in nature that governs the movement of particles from regions of high concentration to regions of low concentration. This process is important in a wide range of applications, including chemical reactions, biological processes, and industrial processes. In this article, we will discuss the properties of diffusion in liquids and gases.Diffusion in LiquidsDiffusion in liquids is a slow process due to the high degree of intermolecular attraction between the molecules. The rate of diffusion is influenced by several factors, including the temperature, viscosity, and concentration of the solution.Temperature: The rate of diffusion in liquids increases with increasing temperature. This is because at higher temperatures, the kinetic energy of the particles increases, which leads to more collisions and a higher probability of diffusion.Viscosity: The rate of diffusion in liquids decreases with increasing viscosity. This is because as the viscosity of the liquid increases, the molecules are more tightly packed together, which makes it more difficult for them to move and diffuse.Concentration: The rate of diffusion in liquids is directly proportional to the concentration of the solute. This is because the more concentrated the solution, the greater the number of molecules that are available to diffuse.Diffusion in GasesDiffusion in gases is a much faster process than in liquids due to the low degree of intermolecular attraction between the molecules. The rate of diffusion is influenced by several factors, including the temperature, pressure, and molecular weight of the gas.Temperature: The rate of diffusion in gases increases with increasing temperature. This is because at higher temperatures, the molecules have more kinetic energy and move at a faster rate, which leads to a higher probability of diffusion.Pressure: The rate of diffusion in gases is directly proportional to the pressure of the gas. This is because as the pressure increases, the molecules are more tightly packed together, which makes it easier for them to collide and diffuse.Molecular Weight: The rate of diffusion in gases is inversely proportional to the molecular weight of the gas. This is because the lighter gas molecules have a higher average velocity and are able to move more quickly and diffuse more rapidly.ConclusionIn conclusion, diffusion is a process that occurs in both liquids and gases. The rate of diffusion is influenced by several factors, including temperature, viscosity, concentration, pressure, and molecular weight. Understanding the properties of diffusion is important in a wide range of applications, including chemical reactions, biological processes, and industrial processes.。

缺血性脑卒中与神经递质的关系

缺血性脑卒中与神经递质的关系缺血性脑卒中严重危害人类的健康，人们对它的认识虽然在逐步加深，有效的治疗手段仍然十分缺乏。

缺血性脑卒中导致多种神经递质释放紊乱，进而导致一系列功能障碍，药物对神经递质的影响也已成为衡量其药效的一个重要依据。

本文概述了几种重要的神经递质与缺血性脑卒中的关系，以期能为新药开发及临床用药等方面提供依据。

[Abstract]Ischemic stroke brings serious damage to human health. Although our understanding about it is gradually deepening，effective treatments are still deficient. Ischemic stroke causes chaotic releasing of various neurotransmitters，which results in a series of dysfunction. The influence of drugs to neurotransmitters has become an important factor to assess drug effect.This paper summarizes relationship between several important neurotransmitters and Ischemic stroke in hopes to provide evidence to new drugs development and clinical medication.[Key words]Ischemic stroke；Neurotransmitter；Glutamate；Dopamine；GABA脑卒中是导致疾病和死亡的一个重要因素，现已成为继心血管疾病和癌症的第三大死亡原因，迄今为止，脑卒中仍是导致长期运动障碍的首要因素。

电磁式电压互感器铁芯饱和引起的铁磁谐振现象

电磁式电压互感器铁芯饱和引起的铁磁谐振现象发表时间：2012-12-21T15:34:45.170Z 来源：《建筑学研究前沿》2012年9月Under供稿作者：杨晔[导读] 显然，三次谐波谐振也使电压互感器两端出现较高的过电压。

杨晔（无锡市恒驰电力发展有限公司 214161）摘要：运行经验证明，在我国中性点绝缘、中性点经消弧线图接地以及中性点直接接地的3～220kV电网中，都曾发生过由于电磁式电压互感器铁芯饱和引起的铁磁谐振过电压。

例如，江苏某220kV变电所因中性点临时不接地曾引起互感器的谐振过电压；东北电网某154kV经消弧线图接地系统，曾因消弧线圈；临时脱离运行引起互感器的谐振过电压；其中以在中性点绝缘的配电网中出现的较为频繁，是造成事故最多的一种内部过电压，因为其他接地系统只有当它们变成中性点绝缘系统时才有可能发生这种过电压。

关键词：过电压；物理概念；铁磁谐振现象Electromagnetic voltage transformer core saturation caused by ferromagnetic resonance phenomenon YangYe(wuxi constant chi electric power development co., LTD. 214161) Pick to: operating experience proved, in our country neutral insulation, neutral by petersen diagram ground and neutral directly grounded 3 ~ 220 kv power grid, have happened because of electromagnetic voltage transformer core saturation caused by ferromagnetic resonance overvoltage. For example, jiangsu a 220 kv substation for neutral temporary not earth once caused a transformer of resonance overvoltage; Northeast power grid a 154 kv via petersen diagram grounding system, once for arc suppression coil; Temporary from operation cause transformer of resonance overvoltage; Among them with the distribution network in neutral insulation appeared in more frequent, causing accidents is a kind of most internal overvoltage, because other grounding system only when they become neutral insulation system may not happen until the overvoltage. Keywords: overvoltage; Physical concepts; Ferromagnetic resonance phenomenon一、过电压产生的基本物理概念电磁式电压互感器引起的铁磁谐振过电压，从本质上讲，是由于电磁式电压互感器的非线性电感与系统的对地电容构成的铁磁谐振所引起的。

语言学第十二章优秀课件

Gearing impairment B.
Each hemisphere Chapter 12 Language and Brain
human vocal cords B.
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The neurons are the basic information processing units of the nervous .
D.The cortex is separated by the longitudinal
fissure into D.
Chapter 12 Language and Brain
two
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Chapter 12 Language and Brain
cerebral hemispheres( the left and right brain) Human linguistic ability largely depends on_________
It is the cerebral cortex that accounts for the human distinctness in the animal world and the secret of language. Animals have no cortex.
Chapter 12 Language and Brain
语言学第十二章
Chapter 12 Language and Brain
Neurolinguistic is the study of the relationship between brain and language.

关于动物神经系统的英语文章

关于动物神经系统的英语文章The nervous system is the functional regulation system of animal organic body weight.The higher the evolutionary stage,the more developed and complex the nervous system,and the stronger the ability to adapt to the environment.Protozoa have not yet formed a meridian system,but can respond to external stimuli,tending to favorable stimuli and avoiding unfavorable stimuli.Paramecia's thorn bubbles can also release thorn fibers when they encounter stimulation.There are awns in the mesoglia,and some scholars believe that it has the function of nerve conduction.Recently,Chinese scholars have proved through experiments that it is a primitive nerve cell.Chinese scholar Zhang Xiaoyun et al.discovered the primitive nerve cells and nerve substances of sponges.From the evolution of the nervous system,it shows that sponges are between protozoa and metazoans and occupy an irreplaceable position.The epithelium of coelenterates has a nerve-like conduction function,which is a discovery in recent years that electrophysiology techniques and electron microscopy have been used to study coelenterate nerves.Non-nerve conduction or nerve-like conduction was first confirmed in coelenterates.Notmuch is known about innervation in muscles.In recent years,some experiments have shown that the ultrastructure of neuromuscular synapses and neuromuscular connections,the nerve-to-muscle contact parts of coelenterates,are also similar to those of higher animals.。

DISCOVERY OF NEUTRAL CURRENTS

DISCOVERY OF NEUTRAL CURRENTS (GGM-CERN 1973)
GENERAL RE0’s there were good reasons to disbelief the existence of Weak Neutral Currents:
Processes as:

were highly suppressed
(B.R. < 5x 10-5).
Many experiments placed other,similar limits on strangenessChanging neutral currents.
Kracow July 20th 2009 1
Since there was no reason at that time to believe that any relevant distinction existed between strangenesschanging and strangeness-conserving neutral currents, the reasonable conclusion that many physicists reached was that neutral currents simply did not exist. 2)Furthermore experimental limits were established also on strangeness conserving neutral current processes. (See results on the RAMM chamber for p =p and for single production (Columbia Univ. W. Lee) 3)I would like to remember that the original Weinberg Salam theory concerned only LEPTONS and QUARKS played no role at all.

一种新的部分神经进化网络的股票预测(英文)

一种新的部分神经进化网络的股票预测(英文)一种新的部分神经进化网络的股票预测自从股票市场的出现以来，人们一直在寻求能够提前预测股票走势的方法。

许多投资者和研究人员尝试使用各种技术分析工具和模型来预测股票未来的走势，但是股票市场的复杂性和难以预测性使得这变得困难重重。

因此，寻找一种能够准确预测股票走势的方法一直是金融界的热点问题。

近年来，人工智能技术在金融领域的应用日益增多。

其中，神经网络是一种被广泛使用的工具，它可以自动学习和识别模式，并根据所学的模式进行预测。

然而，传统神经网络在预测股票市场方面存在诸多问题，例如过拟合和难以处理大量数据等。

为了克服这些问题，本文提出了一种新的部分神经进化网络（Partial Neural Evolving Network, PNEN）模型来预测股票走势。

PNEN模型将神经网络和进化算法相结合，通过优化和训练来实现更准确的预测结果。

PNEN模型的核心思想是将神经网络的隐藏层拆分为多个小模块，每个小模块只负责处理一部分输入数据。

通过这种方式，模型可以更好地适应不同的市场情况和模式。

同时，采用进化算法来优化模型的参数，可以进一步提高模型的预测性能。

具体而言，PNEN模型包括以下几个步骤：1. 数据准备：从股票市场获取历史交易数据，并对数据进行预处理和归一化处理，以便更好地输入到模型中。

2. 构建模型结构：将神经网络的隐藏层拆分为多个小模块，通过进化算法来确定每个小模块的结构和参数。

进化算法通过优化模型的准确性和稳定性，以获得更好的预测结果。

3. 训练模型：使用历史数据集对模型进行训练，并通过反向传播算法来更新模型的权重和偏置。

同时，通过与进化算法的交互，不断调整模型结构和参数。

4. 预测结果：使用训练好的模型对未来的股票走势进行预测。

通过模型对市场的分析和判断，可以为投资者提供决策参考。

为了验证PNEN模型的效果，我们在实际的股票市场数据上进行了实验。

结果表明，与传统神经网络模型相比，PNEN 模型在预测股票走势方面具有更好的准确性和稳定性。

动态平衡的英语

动态平衡的英语Dynamic EquilibriumEquilibrium is a fundamental concept in science, describing a state of balance where opposing forces or influences are in a state of stability. This concept can be applied to various fields, including physics, chemistry, biology, and even social and economic systems. One particular type of equilibrium is known as dynamic equilibrium, which is the focus of this essay.Dynamic equilibrium refers to a state where there is a continuous exchange or flow of energy, matter, or information, yet the overall system remains in a stable state. This is in contrast to a static equilibrium, where the system is completely at rest and there is no change or movement. Dynamic equilibrium is a common phenomenon in nature and is essential for the functioning of many complex systems.One of the most well-known examples of dynamic equilibrium is the chemical equilibrium observed in reversible chemical reactions. In a reversible reaction, the forward and reverse reactions occur simultaneously, with the rates of the forward and reverse reactionsbeing equal. As a result, the concentrations of the reactants and products remain constant over time, even though the individual molecules are constantly being converted from one form to the other. This dynamic balance is essential for maintaining the stability of chemical systems and is crucial in many industrial and biological processes.Another example of dynamic equilibrium can be found in the human body. The body's internal environment, known as the homeostasis, is maintained through a complex network of regulatory systems that constantly monitor and adjust various physiological parameters, such as temperature, pH, and blood pressure. These regulatory systems work to maintain a delicate balance, ensuring that the body's internal conditions remain within a narrow range, even in the face of external changes or disturbances. This dynamic equilibrium is essential for the proper functioning of the body's cells and organs.In the field of ecology, dynamic equilibrium is observed in the interactions between different species within an ecosystem. In a stable ecosystem, there is a continuous exchange of energy and matter, with producers, consumers, and decomposers all playing essential roles. The populations of different species may fluctuate over time, but the overall structure and function of the ecosystem remain relatively constant. This dynamic equilibrium is crucial for the sustainability of the ecosystem and the maintenance of biodiversity.In the realm of social and economic systems, dynamic equilibrium can be observed in the interactions between various stakeholders, such as governments, businesses, and consumers. These systems are constantly in flux, with new policies, technologies, and market forces constantly shaping the landscape. Yet, despite these changes, the overall system may maintain a state of dynamic equilibrium, where the various components work together to maintain a stable and functional state.One of the key features of dynamic equilibrium is the concept of feedback loops. Feedback loops are mechanisms that allow a system to self-regulate, adjusting its behavior in response to changes in the environment or internal conditions. Positive feedback loops, for example, can amplify changes, while negative feedback loops can dampen them, helping to maintain the overall equilibrium. These feedback mechanisms are essential for the stability and resilience of dynamic systems.Another important aspect of dynamic equilibrium is the concept of adaptation. In order to maintain a state of dynamic equilibrium, systems must be able to adapt to changing conditions and disturbances. This may involve the development of new strategies, the reorganization of internal structures, or the exploitation of new resources. Adaptability is a key characteristic of dynamic systems andis essential for their long-term sustainability.In conclusion, dynamic equilibrium is a fundamental concept that underlies the behavior of many complex systems in the natural and social world. By understanding the principles of dynamic equilibrium, we can gain valuable insights into the functioning of these systems and develop strategies for maintaining their stability and resilience in the face of change and uncertainty. Whether in the realm of chemistry, biology, ecology, or economics, the study of dynamic equilibrium remains a crucial area of scientific inquiry and practical application.。

饱和砂土动三轴实验应力应变滞回环研究

文章编号:1007Ο2993(2003)06Ο0337Ο06饱和砂土动三轴实验应力应变滞回环研究周海林　王星华(广州市中心区交通项目办,广州 510032) 【摘　要】　通过动三轴实验研究砂土液化过程中轴向应变的发展,从理论解析和应力状态变化两个角度分析了砂土液化过程中的应力应变滞回环,分析表明,刚度的衰减与剪胀的出现直接影响了滞回环的形状,解释了砂土液化过程中应力应变滞回环出现的不规则变化情况。

【关键词】　砂土液化;刚度衰减;剪胀【中图分类号】　TV 87113Study on the H ysteresis Loop of the Saturated Sand in Dynamic T riaxial T est【Abstract 】　The development of strain of saturated sand is studied based on the dynamic triaxial test.HmsteresisLoop of the Saturated Sand in the dynamic triaxial Test is studied form view of the theory analytics and stress state.The result of research shows that attenuation of stiffness and appearance of shear dilatation affect the shape of hysteresis loop greatly ,these explain the phenomenon in the dynamic triaxial Test ,then the mechanism of the saturated sand liq 2uefaction is more clear than before.【K ey w ords 】　sand liquefaction ;stiffness attenuation ;shear dilatation0　引　言过去对砂土液化的研究多集中于孔隙水压力、砂土液化强度上,且取得了研究成果[1～4]。

Increasing destructiveness of tropical cyclones over

Increasing destructiveness of tropical cyclones over the past 30yearsKerry Emanuel 1Theory 1and modelling 2predict that hurricane intensity should increase with increasing global mean temperatures,but work on the detection of trends in hurricane activity has focused mostly on their frequency 3,4and shows no trend.Here I deﬁne an index of the potential destructiveness of hurricanes based on the total dissipa-tion of power,integrated over the lifetime of the cyclone,and show that this index has increased markedly since the mid-1970s.This trend is due to both longer storm lifetimes and greater storm intensities.I ﬁnd that the record of net hurricane power dissipa-tion is highly correlated with tropical sea surface temperature,reﬂecting well-documented climate signals,including multi-decadal oscillations in the North Atlantic and North Paciﬁc,and global warming.My results suggest that future warming may lead to an upward trend in tropical cyclone destructive potential,and —taking into account an increasing coastal population —a substantial increase in hurricane-related losses in the twenty-ﬁrst century.Fluctuations in tropical cyclone activity are of obvious importance to society,especially as populations of afﬂicted areas increase 5.Tropical cyclones account for a signiﬁcant fraction of damage,injury and loss of life from natural hazards and are the costliest natural catastrophes in the US 6.In addition,recent work suggests that global tropical cyclone activity may play an important role in driving the oceans’thermohaline circulation,which has an important inﬂuence on regional and global climate 7.Studies of tropical cyclone variability in the North Atlantic reveal large interannual and interdecadal swings in storm frequency thathave been linked to such regional climate phenomena as the El Nin˜o/Southern Oscillation 8,the stratospheric quasi-biennial oscillation 9,and multi-decadal oscillations in the North Atlantic region 10.Varia-bility in other ocean basins is less well documented,perhaps because the historical record is less complete.Concerns about the possible effects of global warming on tropical cyclone activity have motivated a number of theoretical,modelling and empirical studies.Basic theory 11establishes a quantitative upper bound on hurricane intensity,as measured by maximum surface wind speed,and empirical studies show that when accumulated over large enough samples,the statistics of hurricane intensity are strongly controlled by this theoretical potential intensity 12.Global climate models show a substantial increase in potential intensity with anthropogenic global warming,leading to the prediction that actual storm intensity should increase with time 1.This prediction has been echoed in climate change assessments 13.A recent comprehensive study using a detailed numerical hurricane model run using climate predictions from a variety of different global climate models 2sup-ports the theoretical predictions regarding changes in storm inten-sity.With the observed warming of the tropics of around 0.58C,however,the predicted changes are too small to have been observed,given limitations on tropical cyclone intensity estimation.The issue of climatic control of tropical storm frequency is farmore controversial,with little guidance from existing theory.Global climate model predictions of the inﬂuence of global warming on storm frequency are highly inconsistent,and there is no detectable trend in the global annual frequency of tropical cyclones in historical tropical cyclone data.Although the frequency of tropical cyclones is an important scientiﬁc issue,it is not by itself an optimal measure of tropical cyclone threat.The actual monetary loss in wind storms rises roughly as the cube of the wind speed 14as does the total power dissipation (PD;ref.15),which,integrated over the surface area affected by a storm and over its lifetime is given by:PD ¼2pðt 0ðr 0C D r j V j 3r d r d t ð1Þwhere C D is the surface drag coefﬁcient,r is the surface air density,j V j is the magnitude of the surface wind,and the integral is over radius to an outer storm limit given by r 0and over t ,the lifetime of the storm.The quantity PD has the units of energy and reﬂects the total power dissipated by a storm over its life.Unfortunately,the area integral in equation (1)is difﬁcult to evaluate using historical data sets,which seldom report storm dimensions.On the other hand,detailed studies show that radial proﬁles of wind speed are generally geometrically similar 16whereas the peak wind speeds exhibit little if any correlation with measures of storm dimensions 17.Thus variations in storm size would appear to introduce random errors in an evaluation of equation (1)that assumes ﬁxed storm dimen-sions.In the integrand of equation (1),the surface air density varies over roughly 15%,while the drag coefﬁcient is thought to increase over roughly a factor of two with wind speed,but levelling off at wind speeds in excess of about 30m s 21(ref.18).As the integral in equation (1)will,in practice,be dominated by high wind speeds,we approximate the product C D r as a constant and deﬁne a simpliﬁed power dissipation index as:PDI ;ðtV 3max d tð2Þwhere V max is the maximum sustained wind speed at the conven-tional measurement altitude of 10m.Although not a perfect measureof net power dissipation,this index is a better indicator of tropical cyclone threat than storm frequency or intensity alone.Also,the total power dissipation is of direct interest from the point of view of tropical cyclone contributions to upper ocean mixing and the thermohaline circulation 7.This index is similar to the ‘accumulated cyclone energy’(ACE)index 19,deﬁned as the sum of the squares of the maximum wind speed over the period containing hurricane-force winds.The analysis technique,data sources,and corrections to the raw data are described in the Methods section and in Supplementary Methods.To emphasize long-term trends and interdecadal variabil-ity,the PDI is accumulated over an entire year and,individually,overLETTERS1Program in Atmospheres,Oceans,and Climate,Massachusetts Institute of Technology,Cambridge,Massachusetts 02139,USA.686© 2005Nature Publishing Groupeach of several major cyclone-prone regions.To minimize the effect of interannual variability,we apply to the time series of annual PDI a 1-2-1smoother deﬁned by:x 0i ¼0:25ðx i 21þx i þ1Þþ0:5x ið3Þwhere x i is the value of the variable in year i and x 0i is the smoothed value.This ﬁlter is generally applied twice in succession.Figure 1shows the PDI for the North Atlantic and the September mean tropical sea surface temperature (SST)averaged over one of the prime genesis regions in the North Atlantic 20.There is an obvious strong relationship between the two time series (r 2¼0.65),suggesting that tropical SST exerts a strong control on the power dissipation index.The Atlantic multi-decadal mode discussed in ref.10is evident in the SSTseries,as well as shorter period oscillationspossibly related to the El Nin˜o/Southern Oscillation and the North Atlantic Oscillation.But the large upswing in the last decade is unprecedented,and probably reﬂects the effect of global warming.We will return to this subject below.Figure 2shows the annually accumulated,smoothed PDI for the western North Paciﬁc,together with July–November average smoothed SST in a primary genesis region for the North Paciﬁc.As in the Atlantic,these are strongly correlated,with an r 2of 0.63.Someof the interdecadal variability is associated with the El Nin ˜o/Southern Oscillation,as documented by Camargo and Sobel 19.The SST time series shows that the upswing in SSTsince around 1975is unusual by the standard of the past 70yr.There are reasons to believe that global tropical SST trends may have less effect on tropical cyclones than regional ﬂuctuations,as tropical cyclone potential intensity is sensitive to the difference between SST and average tropospheric temperature.In an effort to quantify a global signal,annual average smoothed SST between 308N and 308S is compared to the sum of the North Atlantic and western North Paciﬁc smoothed PDI values in Fig.3.The two time series are correlated with an r 2of 0.69.The upturn in tropical mean surface temperature since 1975has been generally ascribed to global warm-ing,suggesting that the upward trend in tropical cyclone PDI values is at least partially anthropogenic.It is interesting that this trend has involved more than a doubling of North Atlantic plus western North Paciﬁc PDI over the past 30yr.The large increase in power dissipation over the past 30yr or so may be because storms have become more intense,on the average,and/or have survived at high intensity for longer periods of time.The accumulated annual duration of storms in the North Atlantic and western North Paciﬁc has indeed increased by roughly 60%since 1949,though this may partially reﬂect changes in reporting practices,as discussed in Methods.The annual average storm peak wind speed summed over the North Atlantic and eastern and western North Paciﬁc has also increased during this period,by about 50%.Thus both duration and peak intensity trends are contributing to the overall increase in net power dissipation.For ﬁxed rates of intensi-ﬁcation and dissipation,storms will take longer to reach greater peak winds,and also take longer to dissipate.Thus,not surprisingly,stronger storms last longer;times series of duration and peak intensity are correlated with an r 2of 0.74.In theory,the peak wind speed of tropical cyclones shouldincreaseFigure 1|A measure of the total power dissipated annually by tropical cyclones in the North Atlantic (the power dissipation index,PDI)compared to September sea surface temperature (SST).The PDI has been multiplied by 2.1£10212and the SST,obtained from the Hadley Centre Sea Ice and SST data set (HadISST)22,is averaged over a box bounded in latitude by 68N and 188N,and in longitude by 208W and 608W.Both quantities have been smoothed twice using equation (3),and a constant offset has been added to the temperature data for ease of comparison.Note that total Atlantic hurricane power dissipation has more than doubled in the past 30yr.Figure 2|Annually accumulated PDI for the western North Paciﬁc,compared to July–November average SST.The PDI has been multiplied by a factor of 8.3£10213and the HadISST (with a constant offset)is averaged over a box bounded in latitude by 58N and 158N,and in longitude by 1308E and 1808E.Both quantities have been smoothed twice using equation (3).Power dissipation by western North Paciﬁc tropical cyclones has increased by about 75%in the past 30yr.Figure 3|Annually accumulated PDI for the western North Paciﬁc and North Atlantic,compared to annually averaged SST.The PDI has been multiplied by a factor of 5.8£10213and the HadISST (with a constant offset)is averaged between 308S and 308N.Both quantities have been smoothed twice using equation (3).This combined PDI has nearly doubled over the past 30yr.NATURE |Vol 436|4August 2005LETTERS687© 2005Nature Publishing Groupby about5%for every18C increase in tropical ocean temperature1. Given that the observed increase has only been about0.58C,these peak winds should have only increased by2–3%,and the power dissipation therefore by6–9%.When coupled with the expected increase in storm lifetime,one might expect a total increase of PDI of around8–12%,far short of the observed change.Tropical cyclones do not respond directly to SST,however,and the appropriate measure of their thermodynamic environment is the potential intensity,which depends not only on surface temperature but on the whole temperature proﬁle of the troposphere.I used daily averaged re-analysis data and Hadley Centre SST to re-construct the potential maximum wind speed,and then averaged the result over each calendar year and over the same tropical areas used to calculate the average SST.In both the Atlantic and western North Paciﬁc,the time series of potential intensity closely follows the SST,but increases by about10%over the period of record,rather than the predicted 2–3%.Close examination of the re-analysis data shows that the observed atmospheric temperature does not keep pace with SST.This has the effect of increasing the potential intensity.Given the observed increase of about10%,the expected increase of PDI is about40%, taking into account the increased duration of events.This is still short of the observed increase.The above discussion suggests that only part of the observed increase in tropical cyclone power dissipation is directly due to increased SSTs;the rest can only be explained by changes in other factors known to inﬂuence hurricane intensity,such as vertical wind shear.Analysis of the250–850hPa wind shear from reanalysis data,over the same portion of the North Atlantic used to construct Fig.1,indeed shows a downward trend of0.3m s21 per decade over the period1949–2003,but most of this decrease occurred before1970,and at any rate the decrease is too small to have had much effect.Tropical cyclone intensity also depends on the temperature distribution of the upper ocean,and there is some indication that sub-surface temperatures have also been increas-ing21,thereby reducing the negative feedback from storm-induced mixing.Whatever the cause,the near doubling of power dissipation over the period of record should be a matter of some concern,as it is a measure of the destructive potential of tropical cyclones.Moreover,if upper ocean mixing by tropical cyclones is an important contributor to the thermohaline circulation,as hypothesized by the author7,then global warming should result in an increase in the circulation and therefore an increase in oceanic enthalpy transport from the tropics to higher latitudes.METHODSPositions and maximum sustained surface winds of tropical cyclones are reported every six hours as part of the‘best track’tropical data sets.(In the data sets used here,from the US Navy’s Joint Typhoon Warning Center(JTWC) and the National Oceanographic and Atmospheric Administration’s National Hurricane Center(NHC),‘maximum sustained wind’is deﬁned as the one-minute average wind speed at an altitude of10m.)For the Atlantic,and eastern and central North Paciﬁc,these data are available from the NHC,while for the western North Paciﬁc,the northern Indian Ocean,and all of the Southern Hemisphere,data from JTWC were used.Owing to changes in measuring and reporting practices since systematic observations of tropical cyclones began in the mid-1940s,there are systematic biases in reported tropical cyclone wind speeds that must be accounted for in analysing trends.The sources of these biases and corrections made to account for them are described in Supplementary Methods.Received28January;accepted3June2005.Published online31July2005.1.Emanuel,K.A.The dependence of hurricane intensity on climate.Nature326,483–-485(1987).2.Knutson,T.R.&Tuleya,R.E.Impact of CO2-induced warming on simulatedhurricane intensity and precipitation:Sensitivity to the choice of climate model and convective parameterization.J.Clim.17,3477–-3495(2004).ndsea,C.W.,Nicholls,N.,Gray,W.M.&Avila,L.A.Downward trends inthe frequency of intense Atlantic hurricanes during the pastﬁve decades.Geophys.Res.Lett.23,1697–-1700(1996).4.Chan,J.C.L.&Shi,J.-E.Long-term trends and interannual variability in tropicalcyclone activity over the western North Paciﬁc.Geophys.Res.Lett.23,2765–-2767(1996).5.Pielke,R.A.J.,Rubiera,J.,Landsea,C.W.,Fernandez,M.L.&Klein,R.Hurricane vulnerability in Latin America and the Caribbean:Normalizeddamage and loss potentials.Nat.Hazards Rev.4,101–-114(2003).6.Pielke,R.A.J.&Landsea,C.W.Normalized U.S.hurricane damage,1925–-1995.Weath.Forecast.13,621–-631(1998).7.Emanuel,K.A.The contribution of tropical cyclones to the oceans’meridionalheat transport.J.Geophys.Res.106,14771–-14782(2001).8.Pielke,R.A.J.&Landsea, Nin˜a,El Nin˜o,and Atlantic hurricanedamages in the United States.Bull.Am.Meteorol.Soc.80,2027–-2033(1999).9.Gray,W.M.Atlantic seasonal hurricane frequency.Part I:El Nin˜o and30mbquasi-biennial oscillation inﬂuences.Mon.Weath.Rev.112,1649–-1668(1984).10.Goldenberg,S.B.,Landsea,C.W.,Mestas-Nun˜ez,A.M.&Gray,W.M.Therecent increase in Atlantic hurricane activity:Causes and implications.Science 293,474–-479(2001).11.Bister,M.&Emanuel,K.A.Dissipative heating and hurricane intensity.Meteorol.Atmos.Phys.50,233–-240(1998).12.Emanuel,K.A.A statistical analysis of tropical cyclone intensity.Mon.Weath.Rev.128,1139–-1152(2000).13.Henderson-Sellers,A.et al.Tropical cyclones and global climate change:Apost-IPCC assessment.Bull.Am.Meteorol.Soc.79,19–-38(1998).14.Southern,R.L.The global socio-economic impact of tropical cyclones.Aust.Meteorol.Mag.27,175–-195(1979).15.Emanuel,K.A.The power of a hurricane:An example of reckless driving on theinformation superhighway.Weather54,107–-108(1998).16.Mallen,K.J.,Montgomery,M.T.&Wang,B.Re-examining the near-core radialstructure of the tropical cyclone primary circulation:Implications for vortexresiliency.J.Atmos.Sci.62,408–-425(2005).17.Weatherford,C.L.&Gray,W.M.Typhoon structure as revealed by aircraftreconnaissance.Part I:Data analysis and climatology.Mon.Weath.Rev.116,1032–-1043(1988).18.Powell,M.D.,Vickery,P.J.&Reinhold,T.A.Reduced drag coefﬁcients for highwind speeds in tropical cyclones.Nature422,279–-283(2003).19.Camargo,S.J.&Sobel,A.H.Western North Paciﬁc tropical cyclone intensityand ENSO.J.Clim.(in the press).20.Saunders,M.A.&Harris,A.R.Statistical evidence links exceptional1995Atlantic hurricane season to record sea warming.Geophys.Res.Lett.24,1255–-1258(1997).21.Levitus,S.,Antonov,J.I.,Boyer,T.P.&Stephens,C.Warming of the worldocean.Science287,2225–-2229(2000).22.Rayner,N.A.et al.Global analyses of sea surface temperature,sea ice,andnight marine air temperature since the late nineteenth century.J.Geophys.Res.108,4407,doi:10.1029/2002JD002670(2003).Supplementary Information is linked to the online version of the paper at /nature.Acknowledgements The author is grateful for correspondence with S.Camargo, C.Guard,ndsea and A.Sobel.Author Information Reprints and permissions information is available at/reprintsandpermissions.The author declares no competingﬁnancial interests.Correspondence and requests for materials should be addressed to the author at emanuel@.LETTERS NATURE|Vol436|4August2005 688©2005Nature Publishing Group。

Does Dark Matter at the Center and in the Halo of the Galaxy Consist of the Same Particles

a rXiv:as tr o-ph/111536v128Nov21Does Dark Matter at the Center and in the Halo of the Galaxy Consist of the Same Particles?Neven Bili ´c 1,F austin Munyaneza,Gary B.Tupper,and Raoul D.Viollier 2Institute of Theoretical Physics and Astrophysics Department of Physics,University of Cape Town Private Bag,Rondebosch 7701,South Africa After a discussion of the properties of degenerate fermion balls,we analyze the orbits of the star S0-1,which has the smallest projected distance to Sgr A ∗,in the supermassive black hole as well as in the fermion ball scenarios of the Galactic center.It is shown that both scenarios are consistent with the data,as measured during the last six years by Genzel et al.and Ghez et al..We then consider a self-gravitating ideal fermion gas at nonzero temperature as a model for the Galactic halo.The Galactic halo of mass ∼2×1012M ⊙enclosed within a radius of ∼200kpc implies the existence of a supermassive compact dark object at the Galactic center that is in hydrostatic and thermal equilibrium with the halo.The central object has a maximal mass of ∼2.3×106M ⊙within a minimal radius of ∼18mpc or ∼21light-days for fermion masses ∼15keV.We thus conclude that both the supermassive compact dark object and the halo could be made of the same weakly interacting ∼15keV particle.PRESENTED ATCOSMO-01Rovaniemi,Finland,August 29–September 4,20011IntroductionIn the past,self-gravitating degenerate neutrino matter has been suggested as a model for quasars,with neutrino masses in the0.2keV∼<m∼<0.5MeV range[1].Later it was used to describe dark matter in clusters of galaxies and in galactic halos,with neutrino masses in the1∼<m/eV∼<25range[2].More recently,supermassive compact dark objects consisting of weakly interacting degenerate fermionic matter,with fermion masses in the10∼<m/keV∼<20range,have been proposed[3,4,5,6,7]as an alternative to the supermassive black holes that are believed to reside at the centers of many galaxies.It has been pointed out that such degenerate fermion balls could cover[5]the whole range of the supermassive compact dark objects that have been observed so far with masses ranging from106to3×109M⊙[8].Most recently,it has been shown that a weakly interacting dark matter particle in the mass range1∼<m/keV∼<5could solve the problem of the excessive structure generated on subgalactic scales in N-body and hydrodynamical simulations of structure formation in this Universe[9].So far the masses of∼20supermassive compact dark objects at the center of galaxies have been measured using various techniques[8].The most massive compact dark object ever observed is located at the center of M87in the Virgo cluster,and it has a mass of about 3×109M⊙[10].If we identify this object of maximal mass with a degenerate fermion ball at the Oppenheimer-Volkoﬀ(OV)limit[11],i.e.,M OV=0.54M3Pl m−2g−1/2≃3×109M⊙[5],where M Pl=The required weakly interacting fermion of∼15keV mass cannot be an active neu-trino,as it would overclose the Universe by orders of magnitude[14].Moreover,an active neutrino of∼15keV is disfavored by the experimental data on solar and atmospheric neutrinos,as these are most probably oscillating into active neutrinos with smallδm2[15], and theνe mass has been determined to be<3eV[16].However,the∼15keV fermion could very well be a sterile neutrino,contributingΩd≃0.3to the dark matter fraction of the critical density today.Indeed,as has been shown for an initial lepton asymmetry of∼10−3,a sterile neutrino of mass∼10keV may be resonantly produced in the early Universe with near closure density,i.e.Ωd∼1[17].The resulting energy spectrum of the sterile neutrinos is cut oﬀfor energies larger than the resonance energy,thus mimicking a degenerate fermion gas.As an alternative possibility,the∼15keV sterile neutrino could be replaced by the axino[18]or the gravitino[19,20]in soft supersymmetry breaking scenarios.In the recent past,galactic halos have been successfully modeled as a self-gravitating isothermal gas of particles of arbitrary mass,the density of which scales asymptotically as r−2,yieldingﬂat rotation curves[21].As the supermassive compact dark objects at the galactic centers are well described by a gas of fermions of mass m∼15keV at T=0, it is tempting to explore the possibility that one could describe both the supermassive compact dark objects and their galactic halos in a uniﬁed way in terms of a fermion gas atﬁnite temperature.We will show in this paper that this is indeed the case,and that the observed dark matter distribution in the Galactic halo is consistent with the existence of a supermassive compact dark object at the center of the Galaxy which has about the right mass and size,and is in thermal and hydrostatic equilibrium with the halo.2Dynamics of the Stars Near the Galactic Center We now would like to compare the predictions of the black hole and fermion ball scenarios of the Galactic center,for the stars with the smallest projected distances to Sgr A∗,based on the measurements of their positions during the last six years[7,12].The projected orbits of three stars,S0-1(S1),S0-2(S2)and S0-4(S4),show deviations from uniform motion on a straight line during the last six years,and they thus may contain nontrivial information about the potential.For our analysis we have selected the star,S0-1,because its projected distance from Sgr A∗in1995.53,4.4mpc or5.3light-days,makes it most likely that it could be orbiting within a fermion ball of radius∼18mpc or∼21light-days. We thus may in principle distinguish between the black hole and fermion ball scenarios for this star.The dynamics of the stars in the gravitationalﬁeld of the supermassive compact dark object can be studied solving Newton’s equation of motion,taking into account the initial position and velocity vectors at,e.g.,t0=1995.4yr,i.e., r(t0)≡(x,y,z)and ˙ r(t0)≡(v x,v y,v z).For the fermion ball the source of gravitationalﬁeld is the mass M(r) enclosed within a radius r[3,7]while for the black hole it is M c=M(R c)=2.6×106M⊙.2The x-axis is chosen in the direction opposite to the right ascension(RA),the y-axis inthe direction of the declination,and the z-axis points towards the sun.The black hole and the center of the fermion ball are assumed to be at the position of Sgr A∗which isalso the origin of the coordinate system at an assumed distance of8kpc from the sun.In Figs.1and2the right ascension(RA)and declination of S0-1are plotted as a function of time for various unobservable z’s and v z=0in1995.4,for the black hole andfermion ball scenarios.The velocity components v x=340km s−1and v y=−1190kms−1in1995.4have beenﬁxed from observations.In the case of a black hole,both RA and declination depend strongly on z in1995.4,while the z-dependence of these quantities inthe fermion ball scenario is rather weak.We conclude that the RA and declination data of S0-1are wellﬁtted with|z|≈0.25′′in the black hole scenario,and with|z|∼<0.1′′in thefermion ball case(1′′=38.8mpc=46.2light-days at8kpc).Of course,we can also trytoﬁt the data varying both the unknown radial velocity v z and the unobservable radial distance z.The results are summarized in Fig.3,where the z−v z phase-space of1995.4,thatﬁts the data,is shown.The small range of acceptable|z|and|v z|values in the black hole scenario(solid vertical line)reﬂects the fact that the orbit of S0-1depend stronglyon z.The weak sensitivity of the orbit on z in the fermion ball case is the reason forthe much larger z−v z phase-spaceﬁtting the data of S0-1[12],as shown by the dashed box.The dashed and solid curves describe the just bound orbits in the fermion ball andblack hole scenarios,respectively.The star S0-1is unlikely to be unbound,because inthe absence of close encounters with stars of the central cluster,S0-1would have to fall in with an initial velocity that is inconsistent with the velocity dispersion of the stars atinﬁnity.Fig.4shows some typical projected orbits of S0-1in the black hole and fermion ballscenarios.The data of S0-1may beﬁtted in both scenarios with appropriate choices of v x,v y,z and v z in1995.4.The inclination angles of the orbit’s plane,θ=arccos L z/| L| , with L=m r×˙ r,are shown next to the orbits.The minimal inclination angle thatdescribes the data in the black hole case isθ=70o,while in the fermion ball scenario it isθ=0o.In the black hole case,the minimal and maximal distances from Sgr A∗are r min =0.25′′and r max=0.77′′,respectively,for the orbit with z=0.25′′and v z=0which has a period of T0≈161yr.The orbits with z=0.25′′and v z=400km s−1or z=0.25′′and v z=700km s−1have periods of T0≈268yr or T0≈3291yr,respectively.In the fermion ball scenario,the open orbit with z=0.1′′and v z=0has a“period”of T0≈77yr with r min=0.13′′and r max=0.56′′.The open orbits with z=0.1′′and v z=400 km s−1or z=0.1′′and v z=900km s−1have“periods”of T0≈100yr or T0≈1436yr, respectively.In concluding,it is important to note that,based on the data of the star S0-1[12],the fermion ball scenario cannot be ruled out.In fact,in view of the z−v z phase space,that is much larger in the fermion ball scenario than in the black hole case,there is reason to treat the fermion ball scenario of the supermassive compact dark object at the center of our Galaxy with the respect it deserves.33Dark Matter in the Center and the Halo of the GalaxyDegenerate fermion balls are well understood in terms of the Thomas-Fermi theory applied to self-gravitating fermionic matter at T=0[3].Extending this theory to nonzero temperature[22,23,24],it has been shown that at some critical temperature T=T c, a self-gravitating ideal fermion gas,having a mass below the OV limit enclosed in a spherical cavity of radius R,may undergo aﬁrst-order gravitational phase transition from a diﬀuse state to a condensed state.This is best seen plotting the energy and free energy as functions of the temperature which are three-valued in some temperature interval, exhibiting a Maxwell-Boltzmann branch at high temperatures and the degenerate branch at low temperatures.However,thisﬁrst-order phase transition can only take place if the Fermi gas is able to get rid of the large latent heat which is due to the binding energy of the fermion ball.As the short-range interactions of the fermions are negligible,the gas cannot release its latent heat;it will thus be trapped for temperatures T<T c in a thermodynamic quasi-stable supercooled state close to the point of gravothermal collapse. The Fermi gas will be caught in the supercooled state even if the total mass of the gas exceeds the OV limit,as a stable condensed state does not exist in this case.The formation of a supercooled state close to the point of gravothermal collapse,may be understood as a process similar to that of violent relaxation,which was introduced to describe rapid virialization of stars of diﬀerent mass in globular clusters[25,26]with-out invoking binary collisions of the stars,as these would not contribute signiﬁcantly to thermalization on a scale of the age of the Universe.Through the gravitational collapse of a cold overdenseﬂuctuation,∼1Gyr after the Big Bang,part of gravitational energy transforms into the kinetic energy of random motion of small-scale densityﬂuctuations. The resulting virialized cloud will thus be well approximated by a gravitationally stable thermalized halo.In order to estimate the mass-temperature ratio,we assume that the cold overdense cloud of the mass of the Galaxy M stops expanding at the time t m,reach-ing its maximal radius R m and minimal average densityρm=3M/(4πR3m).The total energy per particle is just the gravitational energyE=−3R m.(1)Assuming spherical collapse[27]one arrives atρm=9π216Ωdρ0(1+z m)3,(2)where¯ρ(t m)is the background density at the time t m or cosmological redshift z m,and ρ0≡3H20/(8πG)is the present critical density.We now approximate the virialized cloud by a singular isothermal sphere[26]of mass M and radius R,characterized by a constant4circular velocity Θ=(2T/m )1/2and the density proﬁle ρ(r )=Θ2/4πGr 2.Its total energy per particle is the sum of gravitational and thermal energies,i.e.,E =−1R =−15G (6Ωd ρ0M 2)1/3(1+z m ).(4)Taking Ωd =0.3,M =2×1012M ⊙,z m =4,and H 0=65km s −1Mpc −1,we ﬁnd Θ≃220km s −1,which corresponds to the mass-temperature ratio m/T ≃4×106.Next,we brieﬂy discuss the general-relativistic extension of the Thomas-Fermi theory[23]for a self-gravitating gas of N fermions with mass m and degeneracy factor g at the temperature T enclosed in a sphere of radius R .We denote by p ,ρ,and n the pressure,energy density,and particle number density of the gas,respectively.In the following we use the units in which G =1.The metric generated by the mass distribution is static,spherically symmetric,and asymptotically ﬂat,i.e.,ds 2=ξ2dt 2−(1−2M /r )−1dr 2−r 2(dθ2+sin θdφ2).(5)For numerical convenience,we introduce the parameter α=µ/T and the substitution ξ=(ϕ+1)−1/2µ/m ,where µis the chemical potential associated with the conserved particle number N .The equation of state for a self-gravitating gas may thus be represented in parametric form [28]asn =11+exp {[(y 2+1)1/2/(ϕ+1)1/2−1]α},(6)ρ=11+exp {[(y 2+1)1/2/(ϕ+1)1/2−1]α},(7)p =11+exp {[(y 2+1)1/2/(ϕ+1)1/2−1]α},(8)where appropriate length and mass scales a and b ,respectively,have been chosen such that a =b =(2/g )1/2/m 2.Restoring ¯h ,c ,and G ,we havea =g¯h M Pl2m 2km ,(9)b = g M 3Pl 2m2M ⊙.(10)5Thus fermion mass,degeneracy factor,and chemical potential are eliminated from the equation of state.Einstein’sﬁeld equations for the metric(5)are given bydϕr(r−2M),(11)d Mdr=4πr2(1−2M/r)−1/2n(13) imposing particle-number conservation as a condition at the boundaryN(R)=N.(14) Eqs.(11)-(13)should be integrated using the boundary conditions at the origin,i.e.,ϕ(0)=ϕ0>−1,M(0)=0,N(0)=0.(15) It is useful to introduce the degeneracy parameterη=αϕ/2,which,in the Newtonian limit,approachesηnr=(µnr−V)/T,withµnr=µ−m being the nonrelativistic chemical potential and V the Newtonian potential.Asϕis monotonously decreasing with increas-ing r,the strongest degeneracy is obtained at the center withη0=αϕ0/2.The parameter η0,uniquely related to the central density and pressure,will eventually beﬁxed by the requirement(14).For r≥R,the functionϕyields the usual empty-space Schwarzschildsolutionϕ(r)=µ2r −1−1,(16)withM=M(R)= R0dr4πr2ρ(r).(17) Given the temperature T,the set of self-consistency equations(6)-(13),with the bound-ary conditions(14)-(17)deﬁnes the general-relativistic extension of the Thomas-Fermi equation.4Numerical ResultsThe numerical procedure is now straightforward.For aﬁxed,arbitrarily chosenα,weﬁrst integrate Eqs.(11)and(12)numerically on the interval[0,R]toﬁnd the solutions for various central values of the degeneracy parameterη0.Integrating(13)simultaneously,6yields N(R)as a function ofη0.We then select the value ofη0for which N(R)=N.The chemical potentialµcorresponding to this particular solution is given by Eq.(16)which in turn yields the parametric dependence on the temperature throughα=µ/T.The quantities N,T,and R are free parameters of our model and their range of values are dictated by the physics of the problem at hand.At T=0the number of fermions N is restricted by the OV limit N OV=2.89×109radius is at which the r−2asymptotic behavior of the density begins.Theﬂattening of the Galactic rotation curve begins in the range1∼<r/kpc∼<10,hence the solution(3’) most likely describes the Galaxy’s halo.This may be veriﬁed by calculating the rotational curves in our model.We know already from the estimate(4)that our model yields the correct asymptotic circular velocity of220km/s.In order to make a more realistic com-parison with the observed Galactic rotation curve,we must include two additional matter components:the bulge and the disk.The bulge is modeled as a spherically symmetric matter distribution of the form[31]ρb(s)=e−hs[(u+1)8−1]1/2,(18)where s=(r/r0)1/4,r0is the eﬀective radius of the bulge and h is a parameter.We adopt r0=2.67kpc and h yielding the bulge mass M b=1.5×1010M⊙[32].In Fig.8the mass of halo and bulge enclosed within a given radius is plotted for variousη0.Here,the gravitational backreaction of the bulge on the fermionic halo has been taken into account. The data points,indicated by squares,are the mass M c=2.6×106M⊙within18mpc, estimated from the motion of the stars near Sgr A∗[12],and the mass M50=5.4+0.2−3.6×1011 within50kpc,estimated from the motions of satellite galaxies and globular clusters[30]. Variation of the central degeneracy parameterη0between24and32does not change the essential halo features.In Fig.9we plot the circular velocity components of the halo,the bulge,and the disk. The contribution of the disk is modeled as[33]Θd(r)2=Θd(r o)21.97(r/r o)1.22one important diﬀerence:in the Maxwell-Boltzmann case the curve continues to spiral inwards ad inﬁnitum approaching the point of the singular isothermal sphere,that is characterized by an inﬁnite central density.In Fermi-Dirac case the spiral consists of two almost identical curves.The inwards winding of the spiral begins for some negative central degeneracy and stops at the point T=2.3923×10−7m,E=−1.1964×10−7m, whereη0becomes zero.This part of the curve,which basically depicts the behavior of a nondegenerate gas,we call Maxwell-Boltzmann branch.By increasing the central de-generacy parameter further to positive values,the spiral begins to unwind outwards very close to the inwards winding curve.The outwards winding curve will eventually depart from the Maxwell-Boltzmann branch for temperatures T∼>10−3m.Further increase of the central degeneracy parameter brings us to a region,where general-relativistic eﬀects become important.The curve will exhibit another spiral for temperatures and energies of the order of a few10−3m approaching the limiting temperature T∞=2.4×10−3m and energy E∞=3.6×10−3m with both the central degeneracy parameter and the central density approaching inﬁnite values.It is remarkable that gravitationally stable conﬁgura-tions with arbitrary large central degeneracy parameters exist atﬁnite temperature even though the total mass exceeds the OV limit by several orders of magnitude.5ConclusionsIn summary,using the Thomas-Fermi theory,we have shown that a weakly interacting fermionic gas atﬁnite temperature yields a mass distribution that successfully describes both the center and the halo of the Galaxy.For a fermion mass m≃15keV,a reasonable ﬁt to the rotation curve is achieved with the temperature T=3.75meV and the degen-eracy parameter at the centerη0=28.With the same parameters,we obtain the mass M50=5.04×1011M⊙and M200=2.04×1012M⊙within50and200kpc,respectively. These values agree quite well with the mass estimates based on the motions of satellite galaxies and globular clusters[30].Moreover,the mass of M c≃2.27×106M⊙,enclosed within18mpc,agrees reasonably well with the observations of the compact dark object at the center of the Galaxy.We thus conclude that both the Galactic halo and center could be made of the same fermions.An observational consequence of this uniﬁed scenario of fermion ball and fermion halo atﬁnite temperature could be the direct observation of the radiative decay of the fermion (assumed here to be a sterile neutrino)into a standard neutrino,i.e.,f→νγ.The X-ray luminosity of the compact dark object is most easily observed.If the lifetime for the decay f→νγis0.82×1019yr,the luminosity of a M c=2.6×106M⊙fermion ball would be0.9×1034erg s−1.This is consistent with the upper limit of the X-ray luminosity of∼(0.5 -0.9)×1034erg s−1of the source with radius0.5′′≃23light-days,whose center nearly coincides with Sgr A∗,as seen by the Chandra satellite in the2to7keV band[36].The lifetime is proportional to sin−2θ,θbeing the unknown mixing angle of the sterile with active neutrinos.With a lifetime of0.82×1019yr we obtain an acceptable value for the9mixing angle squared ofθ2=1.4×10−11.The X-rays originating from such a radiative decay would contribute about two orders of magnitude less than the observed diﬀuse X-ray background at this wavelength if the sterile neutrino is the dark matter particle of the Universe.The signal observed at the Galactic center would be a sharp X-ray line at ∼7.5keV for g=2and∼6.3keV for g=4.This line could be misinterpreted as the Fe Kαline at6.67keV.Scattering with baryonic matter within the Galactic center could distribute the energy more evenly in the2to7keV band.The X-ray luminosity would be tracing the fermion matter distribution,and it could thus be an important test of the fermion ball scenario.Of course the angular resolution would need to be∼<0.1′′and the sensitivity would have to extend beyond7keV.ACKNOWLEDGEMENTSThis research is in part supported by the Foundation of Fundamental Research(FFR) grant number PHY99-01241and the Research Committee of the University of Cape Town. The work of N.B.is supported in part by the Ministry of Science and Technology of the Republic of Croatia under Contract No.00980102.References[1]M.A.Markov,Phys.Lett.10,122(1964).[2]G.Marx and A.S.Szalay,in Neutrino’72,1,191(Technoinform,Budapest,1972);R.Cowsik and J.McClelland,Astrophys.J.180,7(1973);R.Ruﬃni,Lett.Nuovo Cim.29,161(1980).[3]R.D.Viollier,D.Trautmann and G.B.Tupper,Phys.Lett.B306,79(1993);R.D.Viollier,Prog.Part.Nucl.Phys.32,51(1994).[4]N.Bili´c,D.Tsiklauri and R.D.Viollier,Prog.Part.Nucl.Phys.40,17(1998);N.Bili´c and R.D.Viollier,Nucl.Phys.(Proc.Suppl.)B66,256(1998).[5]N.Bili´c,F.Munyaneza and R.D.Viollier,Phys.Rev.D59,024003(1999).[6]D.Tsiklauri and R.D.Viollier,Astropart.Phys.12,199(1999);F.Munyaneza andR.D.Viollier,astro-ph/9907318.[7]F.Munyaneza,D.Tsiklauri and R.D.Viollier,Astrophys.J.509,L105(1998);ibid.526,744(1999);F.Munyaneza and R.D.Viollier,astro-ph/0103466,Astrophys.J.563,0000(2001).[8]L.C.Ho and J.Kormendy,astro-ph/0003267;astro-ph/0003268.10[9]P.Bode,J.P.Ostriker,and N.Turok,Astrophys.J.556,93(2001),astro-ph/0010389.[10]F.Macchetto et al.,Astrophys.J.489,579(1997).[11]J.R.Oppenheimer and G.M.Volkoﬀ,Phys.Rev.55,374(1939).[12]A.Eckart and R.Genzel,Mon.Not.R.Astron.Soc.284,576(1997);A.M.Ghez,B.L.Klein,M.Morris and E.E.Becklin,Astrophys.J.509,678(1998).[13]R.Mahadevan,Nature394,651(1998).[14]E.W.Kolb and M.S.Turner,The Early Universe(Addison-Wesley,San Francisco,1989).[15]S.Fukuda et al.,Phys.Rev.Lett.85,3999(2000).[16]D.E.Groom et al.,Review of Particle Physics,Eur.Phys.J.C15,1(2000).[17]X.Shi and G.M.Fuller,Phys.Rev.Lett.82,2832(1999);K.Abazajian,G.M.Fuller,and M.Patel,Phys.Rev.D64,023501(2001),astro-ph/0101524;G.B.Tupper,R.J.Lindebaum,and R.D.Viollier,Mod.Phys.Lett.A15,1221(2000).[18]T.Goto and M.Yamaguchi,Phys.Lett.B276,123(1992);L.Covi,J.E.Kim,andL.Roszkowski,Phys.Rev.Lett.82,4180(1999),hep-ph/9905212;L.Covi,H.-B.Kim,J.E.Kim,and L.Roszkowski,hep-ph/0101009.[19]M.Dine and A.E.Nelson,Phys.Rev.D48,1277(1993),hep-ph/9303230;M.Dine,A.E.Nelson and Y.Shirman,Phys.Rev.D51,1362(1995),hep-ph/9408384;M.Dine,A.E.Nelson,Y.Nir and Y.Shirman,Phys.Rev.D53,2658(1996),hep-ph/9507378;D.H.Lyth,Phys.Lett.B488,417(2000),hep-ph/9911257.[20]H.Murayama,Phys.Rev.Lett.79,18(1997),hep-ph/9705271;S.Dimopoulos,G.Dvali,R.Rattazzi and G.F.Giudice,Nucl.Phys.B510,12(1998),hep-ph/9705307;E.A.Baltz and H.Murayama,astro-ph/0108172.[21]S.Cole and cey,Mon.Not.R.Astron.Soc.281,716(1996)and referencestherein.[22]N.Bili´c and R.D.Viollier,Phys.Lett.B408,75(1997).[23]N.Bili´c and R.D.Viollier,Gen.Rel.Grav.31,1105(1999);Eur.Phys.J.B11,173(1999).[24]W.Thirring,Z.Physik235,339(1970);P.Hertel,H.Narnhofer and W.Thirring,Comm.Math.Phys.28,159(1972);J.Messer,J.Math.Phys.22,2910(1981). [25]D.Lynden-Bell,Mon.Not.R.Astron.Soc.136,101(1967).11[26]J.Binney and S.Tremaine,Galactic Dynamics(Princeton University Press,Prince-ton,New Jersey,1987),and references cited therein.[27]T.Padmanabhan,Structure formation in the Universe(Cambridge University Press,Cambridge,1993).[28]J.Ehlers,in Relativity,Astrophysics and Cosmology,edited by W.Israel(D.ReidelPublishing Company,Dordrecht/Boston1973).[29]P.-H.Chavanis and J.Sommeria,Mon.Not.R.Astron.Soc.296,569(1998).[30]M.I.Wilkinson and N.W.Evans,Mon.Not.R.Astron.Soc.310,645(1999).[31]P.J.Young,Astrophys.J.81,807(1976);G.de Vaucouleurs and W.D.Pence,As-trophys.J.83,1163(1978).[32]P.D.Sackett,Astrophys.J.483,103(1997).[33]M.Persic,P.Salucci,and F.Stell,Mon.Not.R.Astron.Soc.281,27(1986).[34]R.P.Olling and M.R.Merriﬁeld,Mon.Not.R.Astron.Soc.311,361(2000).[35]W.Y.Chau,ke,J.Stone,Astrophys.J.281,560(1984).[36]F.K.Baganoﬀet al.,astro-ph/0102151.12Figure1:Right ascension of S0-1versus time for various|z|and v x=340km s−1, v y=−1190km s−1and v z=0in1995.4.Figure2:Declination of S0-1versus time for various|z|and v x=340km s−1,v y=−1190 km s−1and v z=0in1995.4.Figure3:The z−v z phase-space thatﬁts the S0-1data.Figure4:Examples of typical orbits of S0-1.Figure5:Number of particles versus central degeneracy parameter for m/T=4×106 (solid),3.5×106(short dashs),4.5×106(long dashs),and5×106(dot-dashed line). Figure6:The density proﬁle of the halo for a central degeneracy parameterη0=0 (dotted line)and for the sixη0-values discussed in the text.Conﬁgurations with negative η0((1)-(3))are depicted by the dashed and those with positiveη0((1’)-(3’))by the solid line.Figure7:Mass of the halo M h(r)enclosed within a radius r for various central degeneracy parametersη0as in Fig.6.Figure8:Enclosed mass of halo plus bulge versus radius forη0=24(dashed),28(solid), and32(dot-dashed line).Figure9:Fit to the rotation curve of the Galaxy.The data points are from[34]for R0=8.5kpc andΘ0=220km/s.Figure10:Energy(shifted by12×10−8m)versus temperature(shifted by−24×10−8m), both in units of10−10m,forﬁxed N=2×1012M⊙/m13。

神经调控英语

神经调控英语NeuromodulationThe field of neuromodulation has emerged as a revolutionary approach to addressing a wide range of neurological and psychiatric disorders. Neuromodulation, a term that encompasses various techniques aimed at altering the activity of the nervous system, has the potential to transform the way we understand and treat a multitude of complex conditions.At the heart of neuromodulation lies the principle of targeted intervention. By directly influencing the neural pathways and circuits responsible for specific functions or dysfunctions, clinicians and researchers can achieve remarkable outcomes. This approach offers a promising alternative to traditional pharmacological or surgical interventions, often providing more precise and personalized treatment options.One of the primary applications of neuromodulation is in the field of pain management. Chronic pain, a debilitating condition that affects millions of individuals worldwide, has long been a challenge for healthcare providers. Neuromodulation techniques, such as spinalcord stimulation and peripheral nerve stimulation, have demonstrated remarkable success in managing various types of chronic pain, including neuropathic pain, failed back surgery syndrome, and complex regional pain syndrome. By selectively modulating the neural pathways responsible for pain transmission, these interventions can effectively alleviate symptoms and improve the quality of life for patients.Beyond pain management, neuromodulation has also made significant strides in the treatment of neurological disorders. Conditions like Parkinson's disease, essential tremor, and dystonia have been the focus of extensive research and clinical trials utilizing deep brain stimulation (DBS) – a technique that involves the implantation of electrodes into targeted regions of the brain. By precisely delivering electrical impulses to specific neural structures, DBS has been shown to improve motor function, reduce tremors, and alleviate other debilitating symptoms associated with these neurological disorders.In the realm of psychiatric disorders, neuromodulation has also emerged as a promising approach. Conditions such as treatment-resistant depression, obsessive-compulsive disorder, and post-traumatic stress disorder have been the subject of numerous studies exploring the potential of techniques like transcranial magnetic stimulation (TMS) and vagus nerve stimulation (VNS). These non-invasive or minimally invasive interventions aim to modulate the neural circuits involved in the regulation of mood, cognition, and emotional processing, offering new hope for individuals who have not responded well to traditional pharmacological or psychotherapeutic treatments.The advancements in neuromodulation have also extended to the field of rehabilitation and neurological recovery. Patients who have suffered from strokes, spinal cord injuries, or traumatic brain injuries often face significant challenges in regaining lost functions and independence. Neuromodulation techniques, such as transcranial direct current stimulation (tDCS) and epidural spinal cord stimulation, have demonstrated the ability to enhance neuroplasticity, facilitate motor learning, and improve functional outcomes in these populations.Importantly, the field of neuromodulation is not limited to the treatment of specific disorders; it also holds promise for enhancing cognitive and sensory capabilities in healthy individuals. Researchers are exploring the use of non-invasive brain stimulation techniques to improve memory, attention, and decision-making, as well as to enhance sensory perception and motor skills. These applications, often referred to as "neuroenhancement," raise intriguing ethical and societal considerations that require thoughtful discussion and regulation.As the field of neuromodulation continues to evolve, it is essential to acknowledge the complexities and challenges that accompany these advancements. The precise targeting of neural structures, the optimization of stimulation parameters, and the long-term safety and efficacy of these interventions are all areas that require ongoing research and clinical evaluation. Additionally, the integration of neuromodulation into existing healthcare systems and the development of accessible and affordable treatment options are crucial considerations for ensuring equitable access to these innovative therapies.Despite these challenges, the potential of neuromodulation to transform the lives of individuals living with neurological and psychiatric conditions is undeniable. By harnessing the remarkable plasticity and adaptability of the nervous system, clinicians and researchers are poised to unlock new frontiers in the understanding and management of a wide range of complex disorders. As the field continues to evolve, the promise of neuromodulation holds the potential to revolutionize the way we approach neurological and mental health care, offering hope and improved quality of life for those in need.。