Billiards and boundary traces of eigenfunctions
DNA的复制课件-2023-2024学年高一下学期生物人教版(2019)必修2
C.这M个子代T₂噬菌体中,含32P的T₂噬菌体所占的比例为1/M
有2个,故其所占的比例为2/M,C 错误。培养足够长的时间,会出现 不含32P而含35S的T₂噬菌体,但一般不会出现含32P的大肠杆菌,D 错 误。
3 、在一个密闭的容器里,用含有同位素13C的脱氧核苷酸合成一个 DNA分子,然后加入普通的含12C的脱氧核苷酸,经n次复制后,所得DNA 分子中含¹2C的脱氧核苷酸链数与含13C的脱氧核苷酸链数之比是
特点 半保留复制;边解旋边复制;多起点复制;双向复制
结果 子链与母链结合,构成两个相同的新的DNA分子
意义 保持了遗传信息的连续性
总结规律
规律1:若 一 个DNA 复制n次。
1.在子代中共形成 2n 个DNA, 其中含有亲代
DNA链的DNA分子数 2 0 2.含有亲代DNA链的DNA分子数占DNA分子总
D . 含 1 0 0 个 碱 基 对 ( 其 中 胞 嘧 啶 6 0 个 ) 的 DNA复 制 3 次 共 需 要 2 8 0 个
2、用 DNA双链均被32P标记的一个T,噬菌体侵染被35S标记的大肠杆菌, 一段时间后释放出出了M个子代T₂噬菌体。下列有关叙述正确的是
()
A.用32P标记T₂噬菌体的方法与用35S标记大肠杆菌的方法相同
It has not eseaped our notice that the specife pairing we have postulated immediately guggests a p⁰ssible copying mechanism for the genetic materiaI.
Full details of the structure,including the conditions assumed in building it,together with a set of co-ordinatos for the atoms,will be published elsewhere.
鲁棒控制与故障诊断 第二章
A∈ Rn×n with distinct eigenvalues can be diagonalized:
A[x1 x2 ⋯ xn ] = [x1 x2
⎡λ1 ⎤ ⎢ ⎥ λ 2 ⎥. ⋯ xn ]⎢ ⎢ ⎥ ⋱ ⎢ ⎥ λn ⎦ ⎣
and has the following spectral decomposition:
�
Sylvester equation
AX+XB = C
with A∈ Fn×n, B∈ Fm×m , and C∈ Fn×m has a unique solution X∈ Fn×m, if and only if λi(A)+λj(B)≠0, ∀i=1,2,…,n and j=1,2,…,m.
�
Example: Let A be such that
A [x1 x2 x3 x 4 ] = [x1 x2 x3
⎡ λ1 ⎢ x 4 ]⎢ ⎢ ⎢ ⎣ 1 λ1 ⎤ ⎥ ⎥. ⎥ ⎥ λ4 ⎦
λ3
with Re λ1<0, λ3<0, and λ4>0. Then it is easy to verify that
⎡ A11 ⎢A ⎣ 21 ⎡ A11 ⎢0 ⎣
−1 0 ⎤ ⎡ A11 = ⎢ −1 −1 A22 ⎥ − A A A ⎦ ⎣ 22 21 11 −1
−1
0 ⎤ −1 ⎥ A22 ⎦
−1 −1 −1 A12 ⎤ ⎡ A11 ⎤ − A11 A12 A22 =⎢ ⎥. ⎥ −1 A22 ⎦ A22 ⎣ 0 ⎦
S1=span{x1}, S12=span{x1 , x2}, S123=span{x1 , x2 , x3}, S3=span{x3}, S13=span{x1 , x3}, S124=span{x1 , x2 , x4}, S4=span{x4}, S14=span{x1 , x4}, S34=span{x3 , x4}
一维奇异p-Laplacian三点边值问题正解的存在性
一维奇异p-Laplacian三点边值问题正解的存在性白杰;祖力【摘要】利用非线性Leray-Schauder抉择定理和锥不动点定理,在假设条件下证明一维非线性奇异p-Laplacian三点边值问题解的存在性.结果表明,在区间(O,1]上至少存在一个正解.%By means of nonlinear Leray-Schauder alternative theorem and fixed point theorem in cones, thernauthors proved the existence of the solutions for one-dimensional singular p-Laplacian three-point boundaryrnvalue problems under assumptive conditions. There is at least one positive value in the interval from zero tornone.【期刊名称】《吉林大学学报(理学版)》【年(卷),期】2012(050)004【总页数】7页(P621-627)【关键词】Leray-Schauder抉择定理;锥不动点定理;奇异边值问题;正解的存在性【作者】白杰;祖力【作者单位】东北师范大学人文学院信息技术学院,长春130117;长春大学理学院,长春130022;东北师范大学数学与统计学院,长春130024【正文语种】中文【中图分类】O175.140 引言关于一维p-Laplacian边值问题的研究目前已有许多结果[1-10]. 翁世有等[8]利用Schauder不动点原理和非线性Leray-Schauder抉择定理建立了一维p-Laplacian奇异边值问题解的一些存在性原则; Agarwal等[11-12]利用Leray-Schauder抉择定理得到了p=2时正解的存在性.考虑如下奇异边值问题:(1)其中: Φ(s)=s; p>1; q(t)在t=0处有奇性; 非线性项f可能在u=0 处有奇性. 本文应用文献[11-12]的方法, 证明p>1时问题(1)存在正解.1 预备知识假设:(H1) q(t): (0,1)→(0,∞)连续, 并且存在0≤α<p-1, 使得tαq(t)dt<∞成立;(H2) f(u)=g(u)+h(u), 其中: g>0在(0,∞)上连续且单调不增; h≥0在[0,∞)上连续; 且h/g在(0,∞)上单调不减;(H3) 存在一个常数r>0, 使得(2)成立, 其中Φ-1(u) ∶=sgn u是Φ(u)的反函数.例如, 当α∈(a-1,p-1)∩[0,p-1)时, 函数q(t)=t-a(0<t<1, 0≤a<p)满足条件(H1). 注1 容易验证条件(H1)表明若函数u(t)满足下列条件, 则u(t)是问题(1)的一个正解:1) u∈C[0,1]∩C1(0,1];2) 对任意的t∈(0,1], 有u(t)>0, 并且u(0)=0, u(1)=u(ξ), 0<ξ<1;3) Φ(u′(t))在(0,1)上一致绝对连续, 且(Φ(u′))′+q(t)f(u(t))=0, 0<t<1.定义1[13] 设X为实Banach空间, K是X中的闭凸子集, 若K满足下列条件, 则称K是X中的闭锥(简称锥):1) 若x∈K, λ≥0, 则λx∈K;2) 若x∈K, -x∈K, 则x=0.引理1(非线性Leray-Schauder抉择定理)[14] 假设K为Banach空间E的一个凸集, Ω为K的一个相对开子集, 0∈Ω, 映射为一个紧算子, 则下列条件必有一个成立:1) A在上有一个不动点;2) 存在x∈∂Ω和0<λ<1, 使得x=λA(x).定义C[0,1]中锥K为: K ∶={u∈C[0,1]: u(t)是非负的凹函数}.引理2 令h(t): (0,1)→(0,∞)连续, 且存在0≤α<p-1, 使得tαh(t)dt<∞, 则(3)存在唯一的正解V∈C[0,1]∩C1(0,1].证明:先证解的存在性.当0<t≤1时, 设显然, 由注1知, y(t)在(0,1]上连续严格增, 且y(ξ)<0<y(1). 因此, y(t)在(0,1)上只有一个零点. 令σ是y(t)在(0,1)上的唯一零点. 则令(4)则V在(0,1]上有定义, 且在(0,1]上V(t)>0. 进一步, 有(5)由(H2)知, 对0<t≤σ, 有则V(0)=0.类似可得V(1)=V(ξ). 因此, V(t)在[0,1]上连续, 且V(0)=0, V(1)=V(ξ); [Φ(V′(t))]′=-h(t), t∈(0,1).由比较原理易证唯一性. 证毕.令n≥4是一个固定的自然数. 对每个u∈K, 考虑如下问题:(6)其中F(u)=g*(u)+h(u), 满足注2 g*(u)≤g(u), ∀u∈(0,∞).由引理2, 可得:引理3 对每个固定的u∈K, 边值问题(6)存在唯一的解:w(t)=(Ψu)(t), w∈K,其中(7)σu∈(0,1)为如下方程在0≤τ≤1时的唯一解:对u∈K, 由w和Ψ的定义知:1)2) 在(0,1)中, (Φ(w′(t)))′=-q(t)F(u(t)), 且w(0)=1/n, w(1)=w(ξ);3) w=Ψu∈K, ‖w‖=w(σu).表明w(t)是问题(6)的一个解, 且为定义在[0,1]上的凹函数.类似文献[7]中引理2.6~引理2.9的证明方法, 可得下列引理.引理4 令wi(t)是F=Fi(i=1,2)时问题(6)的一个解. 如果F1≤F2, 则w1(t)≤w2(t).引理5 设[a,1]⊂(0,1]是一紧区间, 且令w(t)是F(u)≤M时问题(6)的一个解, 则w′(t)≤C(a,M), a≤t≤1.其中: M是一个正常数; C(a,M)是一个与a,M有关的正常数.注3 设w(t)是F(u)≤M时问题(6)的一个解, 则w(t)≤1/n+VM(t), 即(Ψu)(t)≤1/n+VM(t).注4 设w(t)是F(u)≥m时问题(6)的一个解, 则w(t)≥1/n+Vm(t), 即(Ψu)(t)≥1/n+Vm(t).引理6 对任意有界闭子集Ω⊂K, 集合Ψ(Ω)在[0,1]上等度连续.引理7 对任意的有界闭子集Ω⊂K, 映射Ψ: Ω→K是连续的.综合引理3~引理7, 可得:引理8 Ψ: K→K是全连续的.2 主要结果定理1 假设条件(H1)~(H3)成立, 则在区间(0,1]上, 系统(1)至少存在一个解u∈C[0,1]∩C1(0,1], 满足u>0, 且‖u‖<r.证明: 先用引理1证明解的存在性. 选择ε>0, 且ε<r, 使得(9)选择n0∈{1,2,…}, 使得1/n0<ε. 令N+={n0,n0+1,…}.下面证明边值问题:(10)在(0,1]上有一个解: 且‖un‖<r.∀n∈N+, 为证式(10)有一个解, 需考虑如下边值问题:(11)其中F的定义见式(6).固定n∈N+. 定义为式(7), 式(7)中σu∈(0,1)为如下方程的唯一解:由引理8, 可得是全连续的.下面证明u≠λΨu, λ∈(0,1), u∈∂Ωr.(12)假设式(12)不成立, 即存在一个λ∈(0,1)和u∈∂Ωr, 使得u=λΨu, 则有(13)显然存在σn∈(0,1), 使得在(0,σn)上, u′(t)≥0; 在(σn,1)上, u′(t)≤0, 且u(σn)=‖u‖=r. 再注意到F(u(t))≤g(u(t))+h(u(t)), t∈(0,1),则当z∈(0,1)时,(14)对式(14)从t(0<t≤σn)到σn积分, 得(15)则有(16)再从0到σn积分得(17)即(18)因此(19)这与条件(9)矛盾, 于是式(12)成立.由引理1可知Ψ有一个不动点即1/n≤‖un‖≤r(注意到, 如果‖un‖=r, 则与式(14)~(19)的证明同理可得矛盾). 因为un≥1/n, 所以un(t)也是问题(10)的一个解. 由(H2), 当r>0时,g(un(t))≥g(r), f(un)=h(un)+g(un)≥g(r).则由注4, 可得(20)注5 注意到在区间(0,1]上, Vg(r)(t)>0, 则un(t)>0, t∈(0,1].下面证明{un}n∈N+在[0,1]上一致有界且等度连续. 由式(14)(用un代替u), 可得(21)因为在[0,1]上, un(t)≥1/n, 则在(0,σn)上存在σn∈(0,1), 使得而在(σn,1)上, 且un(σn)=‖un‖≤r.对式(21)从t(0<t<σn)到σn积分得(22)下面证明存在a0>0, 使得a0<inf{σn: n∈N+}≤1.(23)如果式(23)不成立, 则存在N+的子列S, 使得当S中的n→∞时, σn→ 0. 对式(22)从0到σn积分得(24)其中n∈S. 因为当n→∞时, σn→ 0, 则由式(24)可得, 当n→∞时, un(σn)→ 0. 又因为un在[0,1]上σn处取得最大值, 所以当n→∞时, C[0,1]中的函数un→ 0. 这与式(20)矛盾. 表明(25)其中W(t)=q(z)dz. 由注2知, Φ-1(W)∈L1[0,1].对式(21)从σn(σn<t<1)到t积分得当σn≤t≤1时, 有(26)则式(25),(26)表明, 当t∈(0,1)时,(27)定义I: [0,∞)→[0,∞)为I(z) 注意到I: [0,∞)→[0,∞)是单调增的映射, 且I(∞)=∞, 这是因为g(u)>0在(0,∞)上单调不减, 且对任意的B>0, I在[0,B]上连续.{I(un)}n∈N+在[0,1]上一致有界且等度连续, 其等度连续性可从下式得到(这里t,s∈[0,1]):由不等式(28)、 I-1的一致连续性及un(t)-un(s)=I-1(I(un(t)))-I(un(s))可知{un}n∈N+在[0,1]上一致有界且等度连续.由Arzela-Ascoli定理, N+存在一个子列N⊂N+, 使得当n∈N, n→∞时, 存在u∈C[0,1], 使得un在[0,1]上一致收敛于u. 则由式(20)知, 在[0,1]上,un(t)≥Vg(r)(t). 特别地, 在(0,1]上, u(t)>0.固定t∈(0,1], 有(29)由式(26), 有则有一个收敛子列; 为方便, 仍用表示该子列, 并且令r0∈R表示其极限. 则对上面固定的t∈(0,1], 在N上, 令n→∞(注意到q f在紧子区间[t,1]×(0,r]上一致连续)得(30)t取遍(0,1]可得因此r0=u′(1), 从而有(Φ(u′))′+q(t)f(u(t))=0, 0<t<1, u(0)=u(1)-u(ξ)=0.最后易证‖u‖<r(注意到如果‖u‖=r, 与式(14)~(19)的证明同理可推出矛盾). 从而证明了问题(1)至少有一个正解u(t)∈C[0,1]∩C1(0,1], 且‖u‖<r. 证毕.3 应用实例考虑奇异边值问题:(31)其中: 0≤m<p; σ>0; α>0; β>p-1.设则b0=σ1/(p-1)b1.应用定理1可知, 如果存在r>0满足(32)则问题(31)存在一个正解.设则选择r=x0, 则式(32)成立. 显然, 定理1中的(H1)~(H3)成立. 因此, 问题(31)存在一个解u∈C[0,1]∩C1(0,1], 使得在(0,1]上, u>0且‖u‖<r=x0.参考文献【相关文献】[1] XU Xian. Multiplicity Results for Positive Solutions of Some Semi-position Three-Point Boundary Value Problems [J]. J Math Anal Appl, 2004, 291(2): 673-689.[2] SUN Jing-xian, XU Xian, O’Regan D. Nodal Solutions for m-Point Boundary Value Problems Using Bifurcation Methods [J]. Nonlinear Anal: Theory, Method & Applications, 2008, 68(10): 3034-3046.[3] Gupta C P. Existence and Uniqueness Theorems for the Bending of an Elastic Beam Equations [J]. Appl Anal, 1988, 26(4): 289-304.[4] Gupta C P. Solvability of a Three-Point Nonlinear Boundary Value Problem for a Second Order Ordinary Differential Equation [J]. J Math Anal Appl, 1992, 168(2): 540-551.[5] KONG Ling-bin, WANG Jun-yu. Multiple Positive Solutions for the One-Dimensional p-Laplacian [J]. Nonlinear Anal: Theory, Method & Applications, 2000, 42(8): 1327-1333. [6] Agarwal R P, O’Regan D. Twin Solutions to Sin gular Dirichlet Problems [J]. J Math Anal Appl, 1999, 240(2): 433-445.[7] JIANG Da-qing, XU Xiao-jie. Multiple Positive Solutions to a Class of Singular Boundary Value Problems for the One-Dimensional p-Laplacian [J]. Comput Math Appl, 2004,47(4/5): 667-681.[8] WENG Shi-you, GAO Hai-yin, ZHANG Xiao-ying, et al. Existence Principles for Singular Boundary Value Prolems of One Dimension p-Laplacian [J]. Journal of Jilin University: Science Edition, 2006, 44(3): 351-356. (翁世有, 高海音, 张晓颖, 等. 一维p-Laplacian奇异边值问题的存在性原则 [J]. 吉林大学学报: 理学版, 2006, 44(3): 351-356.)[9] YUAN Cheng-jun, WEN Xiang-dan, MENG Qing-yuan. Existence and Uniqueness of Positive Solutions of Fourth-Order Nonlinear Singular Discrete Boundary Value Problems with p-Lapacian Operator [J]. Journal of Northeast Normal University: Natural Science Edition, 2010, 42(1): 5-9. (苑成军, 文香丹, 孟庆元. 奇异四阶p-Lapacian差分方程边值正解的存在唯一性 [J]. 东北师大学报: 自然科学版, 2010, 42(1): 5-9.)[10] YUAN Cheng-jun, WEN Xiang-dan. Existence and Uniqueness of Positive Solutions for Fourth-Order Nonlinear Singular Continuous Boundary Value Problems with p-Lapacian Operator [J]. Journal of Natural Science of Heilongjiang University, 2009, 26(2): 190-193. (苑成军, 文香丹. 奇异四阶p-Lapacian微分方程边值正解的存在惟一性 [J]. 黑龙江大学自然科学学报, 2009, 26(2): 190-193.)[11] Agarwal R P, O’Regan D. Existence Theory for Single and Multiple Solutions to Singular Positone Boundary Value Problems [J]. J Differential Equations, 2001, 175(2): 393-414.[12] Agarwal R P, O’Regan D. Twin Solutions to Singular Boundary Value Problems [J]. Proc Amer Math Soc, 2000, 128: 2085-2094.[13] 钟承奎, 范先令, 陈文源. 非线性泛函分析引论 [M]. 兰州: 兰州大学出版社, 1998.[14] Agarwal R P, O’Regan D. Nonlinear Superlinear Singular and Nonsingular Second Order Boundary Value Problems [J]. J Differential Equations, 1998, 143(1): 60-95.。
Astructurefordeoxyribosenucleicacid
Complexity: The sequence of bases on one strand of DNA determines the sequence on the potential strand through the principle of completeness This means that the order of bases on one strand is complete to the order on the other strand, with A pairing with T and G pairing with C
Structural constraint elements
The structural constraint elements of DNA include the nucleotides, which are made up of a phase group, a deoxyribose sugar, and a nitrogen base (A, T, G, or C)
In addition to the nucleotides, DNA also contains epigenetic marks such as methylation and acetylation, which can affect gene expression without altering the DNA sequence itself
薛定谔—麦克斯韦尔方程径向解的存在性和多重性(英文)
In 1887, the German physicist Erwin Schrödinger proposed a radial solution to the Maxwell-Schrödinger equation. This equation describes the behavior of an electron in an atom and is used to calculate its energy levels. The radial solution was found to be valid for all values of angular momentum quantum number l, which means that it can describe any type of atomic orbital.The existence and multiplicity of this radial solution has been studied extensively since then. It has been shown that there are infinitely many solutions for each value of l, with each one corresponding to a different energy level. Furthermore, these solutions can be divided into two categories: bound states and scattering states. Bound states have negative energies and correspond to electrons that are trapped within the atom; scattering states have positive energies and correspond to electrons that escape from the atom after being excited by external radiation or collisions with other particles.The existence and multiplicity of these solutions is important because they provide insight into how atoms interact with their environment through electromagnetic radiation or collisions with other particles. They also help us understand why certain elements form molecules when combined together, as well as why some elements remain stable while others decay over time due to radioactive processes such as alpha decay or beta decay.。
dynamical system
Dynamical systemThe Lorenz attractor is an example of a non-lineardynamical system. Studying this system helped giverise to Chaos theory.A dynamical system is a concept in mathematics where a fixedrule describes the time dependence of a point in a geometricalspace. Examples include the mathematical models that describethe swinging of a clock pendulum, the flow of water in a pipe, andthe number of fish each springtime in a lake.At any given time a dynamical system has a state given by a set ofreal numbers (a vector) that can be represented by a point in anappropriate state space (a geometrical manifold). Small changes inthe state of the system create small changes in the numbers. Theevolution rule of the dynamical system is a fixed rule thatdescribes what future states follow from the current state. The ruleis deterministic; in other words, for a given time interval only onefuture state follows from the current state.OverviewThe concept of a dynamical system has its origins in Newtonian mechanics. There, as in other natural sciences and engineering disciplines, the evolution rule of dynamical systems is given implicitly by a relation that gives the state of the system only a short time into the future. (The relation is either a differential equation, difference equation or other time scale.) To determine the state for all future times requires iterating the relation many times —each advancing time a small step. The iteration procedure is referred to as solving the system or integrating the system .Once the system can be solved, given an initial point it is possible to determine all its future positions, a collection of points known as a trajectory or orbit .Before the advent of fast computing machines, solving a dynamical system required sophisticated mathematical techniques and could be accomplished only for a small class of dynamical systems. Numerical methods implemented on electronic computing machines have simplified the task of determining the orbits of a dynamical system.For simple dynamical systems, knowing the trajectory is often sufficient, but most dynamical systems are too complicated to be understood in terms of individual trajectories. The difficulties arise because:•The systems studied may only be known approximately —the parameters of the system may not be knownprecisely or terms may be missing from the equations. The approximations used bring into question the validity or relevance of numerical solutions. To address these questions several notions of stability have been introduced in the study of dynamical systems, such as Lyapunov stability or structural stability. The stability of the dynamical system implies that there is a class of models or initial conditions for which the trajectories would be equivalent.The operation for comparing orbits to establish their equivalence changes with the different notions of stability.•The type of trajectory may be more important than one particular trajectory. Some trajectories may be periodic,whereas others may wander through many different states of the system. Applications often require enumerating these classes or maintaining the system within one class. Classifying all possible trajectories has led to the qualitative study of dynamical systems, that is, properties that do not change under coordinate changes. Linear dynamical systems and systems that have two numbers describing a state are examples of dynamical systems where the possible classes of orbits are understood.•The behavior of trajectories as a function of a parameter may be what is needed for an application. As a parameter is varied, the dynamical systems may have bifurcation points where the qualitative behavior of the dynamical system changes. For example, it may go from having only periodic motions to apparently erratic behavior, as inthe transition to turbulence of a fluid.•The trajectories of the system may appear erratic, as if random. In these cases it may be necessary to compute averages using one very long trajectory or many different trajectories. The averages are well defined for ergodic systems and a more detailed understanding has been worked out for hyperbolic systems. Understanding the probabilistic aspects of dynamical systems has helped establish the foundations of statistical mechanics and of chaos.It was in the work of Poincaré that these dynamical systems themes developed.Basic definitionsA dynamical system is a manifold M called the phase (or state) space endowed with a family of smooth evolution functions Φ t that for any element of , the time, map a point of the phase space back into the phase space.The notion of smoothness changes with applications and the type of manifold. There are several choices for the set T. When T is taken to be the reals, the dynamical system is called a flow; and if T is restricted to the non-negative reals, then the dynamical system is a semi-flow. When T is taken to be the integers, it is a cascade or a map; and the restriction to the non-negative integers is a semi-cascade.ExamplesThe evolution function Φ t is often the solution of a differential equation of motionThe equation gives the time derivative, represented by the dot, of a trajectory x(t) on the phase space starting at some point x. The vector field v(x) is a smooth function that at every point of the phase space M provides the velocity 0vector of the dynamical system at that point. (These vectors are not vectors in the phase space M, but in the tangent M of the point x.) Given a smooth Φ t, an autonomous vector field can be derived from it.space TxThere is no need for higher order derivatives in the equation, nor for time dependence in v(x) because these can be eliminated by considering systems of higher dimensions. Other types of differential equations can be used to define the evolution rule:is an example of an equation that arises from the modeling of mechanical systems with complicated constraints.The differential equations determining the evolution function Φ t are often ordinary differential equations: in this case the phase space M is a finite dimensional manifold. Many of the concepts in dynamical systems can be extended to infinite-dimensional manifolds—those that are locally Banach spaces—in which case the differential equations are partial differential equations. In the late 20th century the dynamical system perspective to partial differential equations started gaining popularity.Further examples•Logistic map•Dyadic transformation•Tent map•Double pendulum•Arnold's cat map•Horseshoe map•Baker's map is an example of a chaotic piecewise linear map•Billiards and outer billiards•Hénon map•Lorenz system•Circle map•Rössler map•List of chaotic maps•Swinging Atwood's machine•Quadratic map simulation system•Bouncing ball dynamicsLinear dynamical systemsLinear dynamical systems can be solved in terms of simple functions and the behavior of all orbits classified. In a linear system the phase space is the N-dimensional Euclidean space, so any point in phase space can be represented by a vector with N numbers. The analysis of linear systems is possible because they satisfy a superposition principle: if u(t) and w(t) satisfy the differential equation for the vector field (but not necessarily the initial condition), then so will u(t) + w(t).FlowsFor a flow, the vector field Φ(x) is a linear function of the position in the phase space, that is,with A a matrix, b a vector of numbers and x the position vector. The solution to this system can be found by using the superposition principle (linearity). The case b ≠ 0 with A = 0 is just a straight line in the direction of b:When b is zero and A ≠ 0 the origin is an equilibrium (or singular) point of the flow, that is, if x= 0, then the orbitremains there. For other initial conditions, the equation of motion is given by the exponential of a matrix: for an initial point x,When b = 0, the eigenvalues of A determine the structure of the phase space. From the eigenvalues and the eigenvectors of A it is possible to determine if an initial point will converge or diverge to the equilibrium point at the origin.The distance between two different initial conditions in the case A ≠ 0 will change exponentially in most cases, either converging exponentially fast towards a point, or diverging exponentially fast. Linear systems display sensitive dependence on initial conditions in the case of divergence. For nonlinear systems this is one of the (necessary but not sufficient) conditions for chaotic behavior.Linear vector fields and a few trajectories.MapsA discrete-time, affine dynamical system has the formwith A a matrix and b a vector. As in the continuous case, the change of coordinates x → x + (1 − A) –1b removes the term b from the equation. In the new coordinate system, the origin is a fixed point of the map and the solutions are of . The solutions for the map are no longer curves, but points that hop in the phase space. The the linear system A n xorbits are organized in curves, or fibers, which are collections of points that map into themselves under the action of the map.As in the continuous case, the eigenvalues and eigenvectors of A determine the structure of phase space. For is an eigenvector of A, with a real eigenvalue smaller than one, then the straight lines given by the example, if u1points along α u, with α ∈ R, is an invariant curve of the map. Points in this straight line run into the fixed point.1There are also many other discrete dynamical systems.Local dynamicsThe qualitative properties of dynamical systems do not change under a smooth change of coordinates (this is sometimes taken as a definition of qualitative): a singular point of the vector field (a point where v(x) = 0) will remain a singular point under smooth transformations; a periodic orbit is a loop in phase space and smooth deformations of the phase space cannot alter it being a loop. It is in the neighborhood of singular points and periodic orbits that the structure of a phase space of a dynamical system can be well understood. In the qualitative study of dynamical systems, the approach is to show that there is a change of coordinates (usually unspecified, but computable) that makes the dynamical system as simple as possible.RectificationA flow in most small patches of the phase space can be made very simple. If y is a point where the vector field v(y) ≠ 0, then there is a change of coordinates for a region around y where the vector field becomes a series of parallel vectors of the same magnitude. This is known as the rectification theorem.The rectification theorem says that away from singular points the dynamics of a point in a small patch is a straight line. The patch can sometimes be enlarged by stitching several patches together, and when this works out in the whole phase space M the dynamical system is integrable. In most cases the patch cannot be extended to the entire phase space. There may be singular points in the vector field (where v(x) = 0); or the patches may become smaller and smaller as some point is approached. The more subtle reason is a global constraint, where the trajectory starts out in a patch, and after visiting a series of other patches comes back to the original one. If the next time the orbit loops around phase space in a different way, then it is impossible to rectify the vector field in the whole series of patches. Near periodic orbitsIn general, in the neighborhood of a periodic orbit the rectification theorem cannot be used. Poincaré developed anin the orbit γ and approach that transforms the analysis near a periodic orbit to the analysis of a map. Pick a point x). These points are a Poincaréconsider the points in phase space in that neighborhood that are perpendicular to v(x), of the orbit. The flow now defines a map, the Poincaré map F : S → S, for points starting in S and section S(γ, xreturning to S. Not all these points will take the same amount of time to come back, but the times will be close to the .time it takes xThe intersection of the periodic orbit with the Poincaré section is a fixed point of the Poincaré map F. By a translation, the point can be assumed to be at x = 0. The Taylor series of the map is F(x) = J · x + O(x2), so a change of coordinates h can only be expected to simplify F to its linear partThis is known as the conjugation equation. Finding conditions for this equation to hold has been one of the major tasks of research in dynamical systems. Poincaré first approached it assuming all functions to be analytic and in theprocess discovered the non-resonant condition. If λ1, ..., λνare the eigenvalues of J they will be resonant if oneeigenvalue is an integer linear combination of two or more of the others. As terms of the form λi– ∑ (multiples ofother eigenvalues) occurs in the denominator of the terms for the function h, the non-resonant condition is also known as the small divisor problem.Conjugation resultsThe results on the existence of a solution to the conjugation equation depend on the eigenvalues of J and the degree of smoothness required from h. As J does not need to have any special symmetries, its eigenvalues will typically becomplex numbers. When the eigenvalues of J are not in the unit circle, the dynamics near the fixed point xof F is called hyperbolic and when the eigenvalues are on the unit circle and complex, the dynamics is called elliptic.In the hyperbolic case the Hartman–Grobman theorem gives the conditions for the existence of a continuous function that maps the neighborhood of the fixed point of the map to the linear map J · x. The hyperbolic case is also structurally stable. Small changes in the vector field will only produce small changes in the Poincaré map and these small changes will reflect in small changes in the position of the eigenvalues of J in the complex plane, implying that the map is still hyperbolic.The Kolmogorov–Arnold–Moser (KAM) theorem gives the behavior near an elliptic point.Bifurcation theoryWhen the evolution map Φt (or the vector field it is derived from) depends on a parameter μ, the structure of the phase space will also depend on this parameter. Small changes may produce no qualitative changes in the phasespace until a special value μis reached. At this point the phase space changes qualitatively and the dynamical system is said to have gone through a bifurcation.Bifurcation theory considers a structure in phase space (typically a fixed point, a periodic orbit, or an invariant torus) and studies its behavior as a function of the parameter μ. At the bifurcation point the structure may change its stability, split into new structures, or merge with other structures. By using Taylor series approximations of the maps and an understanding of the differences that may be eliminated by a change of coordinates, it is possible to catalog the bifurcations of dynamical systems.The bifurcations of a hyperbolic fixed point x0 of a system family Fμcan be characterized by the eigenvalues of thefirst derivative of the system DFμ(x) computed at the bifurcation point. For a map, the bifurcation will occur whenthere are eigenvalues of DFμon the unit circle. For a flow, it will occur when there are eigenvalues on the imaginary axis. For more information, see the main article on Bifurcation theory.Some bifurcations can lead to very complicated structures in phase space. For example, the Ruelle–Takens scenario describes how a periodic orbit bifurcates into a torus and the torus into a strange attractor. In another example, Feigenbaum period-doubling describes how a stable periodic orbit goes through a series of period-doubling bifurcations.Ergodic systemsIn many dynamical systems it is possible to choose the coordinates of the system so that the volume (really a ν-dimensional volume) in phase space is invariant. This happens for mechanical systems derived from Newton's laws as long as the coordinates are the position and the momentum and the volume is measured in units of (position) × (momentum). The flow takes points of a subset A into the points Φ t(A) and invariance of the phase space means thatIn the Hamiltonian formalism, given a coordinate it is possible to derive the appropriate (generalized) momentum such that the associated volume is preserved by the flow. The volume is said to be computed by the Liouville measure.In a Hamiltonian system not all possible configurations of position and momentum can be reached from an initial condition. Because of energy conservation, only the states with the same energy as the initial condition are accessible. The states with the same energy form an energy shell Ω, a sub-manifold of the phase space. The volume of the energy shell, computed using the Liouville measure, is preserved under evolution.For systems where the volume is preserved by the flow, Poincaré discovered the recurrence theorem: Assume the phase space has a finite Liouville volume and let F be a phase space volume-preserving map and A a subset of the phase space. Then almost every point of A returns to A infinitely often. The Poincaré recurrence theorem was used by Zermelo to object to Boltzmann's derivation of the increase in entropy in a dynamical system of colliding atoms. One of the questions raised by Boltzmann's work was the possible equality between time averages and space averages, what he called the ergodic hypothesis. The hypothesis states that the length of time a typical trajectory spends in a region A is vol(A)/vol(Ω).The ergodic hypothesis turned out not to be the essential property needed for the development of statistical mechanics and a series of other ergodic-like properties were introduced to capture the relevant aspects of physical systems. Koopman approached the study of ergodic systems by the use of functional analysis. An observable a is a function that to each point of the phase space associates a number (say instantaneous pressure, or average height). The value of an observable can be computed at another time by using the evolution function φ t. This introduces an operator U t, the transfer operator,By studying the spectral properties of the linear operator U it becomes possible to classify the ergodic properties of Φ t. In using the Koopman approach of considering the action of the flow on an observable function, the finite-dimensional nonlinear problem involving Φ t gets mapped into an infinite-dimensional linear problem involving U.The Liouville measure restricted to the energy surface Ω is the basis for the averages computed in equilibrium statistical mechanics. An average in time along a trajectory is equivalent to an average in space computed with the Boltzmann factor exp(−βH). This idea has been generalized by Sinai, Bowen, and Ruelle (SRB) to a larger class of dynamical systems that includes dissipative systems. SRB measures replace the Boltzmann factor and they are defined on attractors of chaotic systems.Nonlinear dynamical systems and chaosSimple nonlinear dynamical systems and even piecewise linear systems can exhibit a completely unpredictable behavior, which might seem to be random. (Remember that we are speaking of completely deterministic systems!). This seemingly unpredictable behavior has been called chaos. Hyperbolic systems are precisely defined dynamical systems that exhibit the properties ascribed to chaotic systems. In hyperbolic systems the tangent space perpendicular to a trajectory can be well separated into two parts: one with the points that converge towards the orbit (the stable manifold) and another of the points that diverge from the orbit (the unstable manifold).This branch of mathematics deals with the long-term qualitative behavior of dynamical systems. Here, the focus is not on finding precise solutions to the equations defining the dynamical system (which is often hopeless), but rather to answer questions like "Will the system settle down to a steady state in the long term, and if so, what are the possible attractors?" or "Does the long-term behavior of the system depend on its initial condition?"Note that the chaotic behavior of complicated systems is not the issue. Meteorology has been known for years to involve complicated—even chaotic—behavior. Chaos theory has been so surprising because chaos can be foundwithin almost trivial systems. The logistic map is only a second-degree polynomial; the horseshoe map is piecewise linear.Geometrical definitionA dynamical system is the tuple , with a manifold (locally a Banach space or Euclidean space),the domain for time (non-negative reals, the integers, ...) and an evolution rule t → f t (with ) such thatf t is a diffeomorphism of the manifold to itself. So, f is a mapping of the time-domain into the space of diffeomorphisms of the manifold to itself. In other terms, f(t) is a diffeomorphism, for every time t in the domain .Measure theoretical definitionSee main article Measure-preserving dynamical system.A dynamical system may be defined formally, as a measure-preserving transformation of a sigma-algebra, thequadruplet . Here, X is a set, and Σ is a sigma-algebra on X, so that the pair is a measurable space. μ is a finite measure on the sigma-algebra, so that the triplet is a probability space. A map is said to be Σ-measurable if and only if, for every , one has . A map τ is said topreserve the measure if and only if, for every , one has . Combining the above, a mapτ is said to be a measure-preserving transformation of X , if it is a map from X to itself, it is Σ-measurable, and is measure-preserving. The quadruple , for such a τ, is then defined to be a dynamical system.The map τ embodies the time evolution of the dynamical system. Thus, for discrete dynamical systems the iterates for integer n are studied. For continuous dynamical systems, the map τ is understood to be a finite time evolution map and the construction is more complicated.Examples of dynamical systemsInternal links•Arnold's cat map•Baker's map is an example of a chaotic piecewise linear map•Circle map•Double pendulum•Billiards and Outer Billiards•Hénon map•Horseshoe map•Irrational rotation•List of chaotic maps•Logistic map•Lorenz system•Rossler mapExternal links•Interactive applet for the Standard and Henon Maps [1] by A. LuhnMultidimensional generalizationDynamical systems are defined over a single independent variable, usually thought of as time. A more general class of systems are defined over multiple independent variables and are therefore called multidimensional systems. Such systems are useful for modeling, for example, image processing.References[1]http://complexity.xozzox.de/nonlinmappings.htmlFurther readingWorks providing a broad coverage:•Ralph Abraham and Jerrold E. Marsden (1978). Foundations of mechanics. Benjamin–Cummings.ISBN 0-8053-0102-X. (available as a reprint: ISBN 0-201-40840-6)•Encyclopaedia of Mathematical Sciences (ISSN 0938-0396) has a sub-series on dynamical systems with reviews of current research.•Anatole Katok and Boris Hasselblatt (1996). Introduction to the modern theory of dynamical systems. Cambridge.ISBN 0-521-57557-5.•Christian Bonatti, Lorenzo J. Díaz, Marcelo Viana (2005). Dynamics Beyond Uniform Hyperbolicity: A Global Geometric and Probabilistic Perspective. Springer. ISBN 3-540-22066-6.•Stephen Smale (1967). "Differential dynamical systems". Bulletin of the American Mathematical Society73: 747–817. doi:10.1090/S0002-9904-1967-11798-1.Introductory texts with a unique perspective:•V. I. Arnold (1982). Mathematical methods of classical mechanics. Springer-Verlag. ISBN 0-387-96890-3.•Jacob Palis and Wellington de Melo (1982). Geometric theory of dynamical systems: an introduction.Springer-Verlag. ISBN 0-387-90668-1.•David Ruelle (1989). Elements of Differentiable Dynamics and Bifurcation Theory. Academic Press.ISBN 0-12-601710-7.•Tim Bedford, Michael Keane and Caroline Series, eds. (1991). Ergodic theory, symbolic dynamics and hyperbolic spaces. Oxford University Press. ISBN 0-19-853390-X.•Ralph H. Abraham and Christopher D. Shaw (1992). Dynamics—the geometry of behavior, 2nd edition.Addison-Wesley. ISBN 0-201-56716-4.Textbooks•Steven H. Strogatz (1994). Nonlinear dynamics and chaos: with applications to physics, biology chemistry and engineering. Addison Wesley. ISBN 0-201-54344-3.•Kathleen T. Alligood, Tim D. Sauer and James A. Yorke (2000). Chaos. An introduction to dynamical systems.Springer Verlag. ISBN 0-387-94677-2.•Morris W. Hirsch, Stephen Smale and Robert Devaney (2003). Differential Equations, dynamical systems, and an introduction to chaos. Academic Press. ISBN 0-12-349703-5.•Julien Clinton Sprott (2003). Chaos and time-series analysis. Oxford University Press. ISBN 0-19-850839-5.•Oded Galor (2011). Discrete Dynamical Systems. Springer. ISBN 978-3-642-07185-0.Popularizations:•Florin Diacu and Philip Holmes (1996). Celestial Encounters. Princeton. ISBN 0-691-02743-9.•James Gleick (1988). Chaos: Making a New Science. Penguin. ISBN 0-14-009250-1.•Ivar Ekeland (1990). Mathematics and the Unexpected (Paperback). University Of Chicago Press.ISBN 0-226-19990-8.•Ian Stewart (1997). Does God Play Dice? The New Mathematics of Chaos. Penguin. ISBN 0140256024.External links• A collection of dynamic and non-linear system models and demo applets (.au/ simulations/non-linear/) (in Monash University's Virtual Lab)•Arxiv preprint server (/list/math.DS/recent) has daily submissions of (non-refereed) manuscripts in dynamical systems.•DSWeb (/) provides up-to-date information on dynamical systems and its applications.•Encyclopedia of dynamical systems (/article/Encyclopedia_of_Dynamical_Systems) A part of Scholarpedia — peer reviewed and written by invited experts.•Nonlinear Dynamics (http://www.egwald.ca/nonlineardynamics/index.php). Models of bifurcation and chaos by Elmer G. Wiens•Oliver Knill () has a series of examples of dynamical systems with explanations and interactive controls.•Sci.Nonlinear FAQ 2.0 (Sept 2003) (/faculty/jdm/faq-Contents.html) provides definitions, explanations and resources related to nonlinear scienceOnline books or lecture notes:•Geometrical theory of dynamical systems (/pdf/math.HO/0111177). Nils Berglund's lecture notes for a course at ETH at the advanced undergraduate level.•Dynamical systems (/online_bks/coll9/). George D. Birkhoff's 1927 book already takes a modern approach to dynamical systems.•Chaos: classical and quantum (). An introduction to dynamical systems from the periodic orbit point of view.•Modeling Dynamic Systems (/2000/0008/0008feat2.htm). An introduction to the development of mathematical models of dynamic systems.•Learning Dynamical Systems (/research/ai/dynamics/tutorial/home.html).Tutorial on learning dynamical systems.•Ordinary Differential Equations and Dynamical Systems (http://www.mat.univie.ac.at/~gerald/ftp/book-ode/ ). Lecture notes by Gerald TeschlResearch groups:•Dynamical Systems Group Groningen (http://www.math.rug.nl/~broer/), IWI, University of Groningen.•Chaos @ UMD (/). Concentrates on the applications of dynamical systems.•Dynamical Systems (/dynamics/), SUNY Stony Brook. Lists of conferences, researchers, and some open problems.•Center for Dynamics and Geometry (/dynsys/), Penn State.•Control and Dynamical Systems (/), Caltech.•Laboratory of Nonlinear Systems (http://lanoswww.epfl.ch/), Ecole Polytechnique Fédérale de Lausanne (EPFL).•Center for Dynamical Systems (http://www.math.uni-bremen.de/ids.html/), University of Bremen•Systems Analysis, Modelling and Prediction Group (/samp/), University of Oxford •Non-Linear Dynamics Group (http://sd.ist.utl.pt/), Instituto Superior Técnico, Technical University of Lisbon •Dynamical Systems (http://www.impa.br/), IMPA, Instituto Nacional de Matemática Pura e Applicada.。
埃塞俄比亚芥和芥菜型油菜全套染色体精确识别
埃塞俄比亚芥和芥菜型油菜全套染色体精确识别埃塞俄比亚芥(Brassica carinata,2n=4x=34)和芥菜型油菜(B.juncea,
2n=4x=36)是芸薹属重要的油料和经济作物。
相对于芸薹属其他重要成员,如白菜、甘蓝和甘蓝型油菜等,埃塞俄比亚芥和芥菜型油菜的全套染色体精确识别体系还没有建立。
本文利用rDNA重复序列(5S rDNA和45S rDNA)、着丝粒重复序列(CentBrl 和CentBr2)、亚端粒重复序列pBrSTR以及B基因组特异的重复序列pBNBH35作为探针,通过两次连续的多色荧光原位杂交,使埃塞俄比亚芥和芥菜型油菜的每
一条染色体分别呈现出不同的信号特征,了解了这些重复序列在基因组各染色体上的组成和分布,精确地识别了两物种的所有染色体。
同时,对四个不同地域来源的埃塞俄比亚芥进行了核型比较,发现这些重复序列在基因组上的组成和分布相对保守,四个不同地域来源的埃芥染色体核型基本一致。
此外,利用8个来自B. rapa(2n=2x=20,AA)已知连锁群的BAC克隆,结合我
们建立的染色体识别和BAC-FISH技术,发现来源于A基因组的BAC克隆可以在埃塞俄比亚芥的B和C基因组上产生杂交信号。
从而找到了B和C基因组来源的部分同源染色体的对应关系;建立了埃塞俄比亚芥染色体识别图和遗传连锁图的联系。
最后,利用建立的染色体识别体系,观察了埃塞俄比亚芥花粉母细胞分裂过
程中染色体行为,发现减数分裂终变期大多数同源染色体配对形成二价体,也有
少数部分同源染色体形成三价体、四价体、多价体。
微生物外文翻译之三
Microbial degradation of PAHs and other hydrophobic substrates is believed to be limited by the amounts dissolved in the water phase (Ogram et al., 1985; Rijnaarts et al., 1990; Volkering et al., 1992; Volkering et al., 1993; Harms and Bosma, 1997; Bosma et al., 1997), with sorbed, crystalline, and non-aqueous phase liquid (NAPL)-dissolved PAHs being unavailable to PAH-degrading organisms. Bioavailability is considered a dynamic process, determined by the rate of substrate mass-transfer to microbial cells relative to their intrinsic catabolic activity (Bosma et al., 1997; Harms and Bosma, 1997). It has been described by a bioavailability number, Bn, (Koch, 1990; Bosma et al., 1997), which is a measure of a microorganism’s substrate degradation efficiency in a given environment. Bn is defined as the capacity of an organism’s or a population’s environment to provide a chemical, divided by the capacity of the organism or population to transform that chemical. At high mass transfer rates, the overall biodegradation rate is controlled by the metabolic activity of the bacteria (Bn > 1), i.e. by both the specific activity of the cells and the population density. At Bn ¼ 1, the biodegradation rate is equally controlled by the physical transport and the microbial activity. When the transport of the substrate decreases or the bacterial population grows, the mass transfer becomes the factor that limits the biodegradation (Bn ! 1).
谱方法和边界值法求解二维薛定谔方程硕士学位论文1 精品推荐
硕士学位论文谱方法和边界值法求解二维薛定谔方程摘要薛定谔方程是物理系统中量子力学的基础方程,它可以清楚地说明量子在系统中随时间变化的规律。
通过求解微观系统所对应的薛定谔方程,我们能够得到其波函数以及对应的能量,从而计算粒子的分布概率,进一步来了解其性质。
在化学和物理等诸多科学研究领域当中,薛定谔方程求解的结果都与实际很相符。
近年来,很多学者通过各种方法研究具有复杂势函数的薛定谔方程,解释了很多重要的物理现象,因此对薛定谔方程的求解具有相当重要的意义。
本文主要是用Galerkin-Chebyshev谱方法和边界值法求解二维薛定谔方程。
首先运用Galerkin-Chebyshev谱方法来对空间导数进行近似,离散二维薛定谔方程,从而将原问题转化为复数域上的线性常微分方程组。
然后用边界值法求解该方程组,所求得的数值解即为原问题的解,之后进行误差分析。
最后利用Matlab进行数值模拟,给出数值解的图像以及误差曲面图像,结果显示此方法精度高且具有很好的稳定性。
关键词:薛定谔方程;Galerkin-Chebyshev谱方法;边界值法;数值解;精度高;稳定AbstractThe Schrödinger equation is the basic equations of quantum mechanics in the physical system. It can clearly describe the regular of the quantum evolves over time. By solving the Schrödinger equation which the micro system correspond, we can get the wave function and energy, and thus calculate the probability distribution of the particles, further understand the nature of it.In chemistry, physics and other fields of scientific research, the results of solving the Schrodinger equation are basically consistent with the actual.In recent years, many researchers used a variety of methods to investigate the Schrödinger equation with complex potential function, and explained a lot of important phenomena.Thus solving the Schrödinger equation has very important significance.The main purpose of this paper is to solve the two dimensional Schrödinger equation through the Galerkin-Chebyshev spectral method and the boundary value method. First we use the spectral method to approximate the spatial derivation, discretize the two dimensional Schrödinger equation,and transform the original problem into a set of linear ordinary differential equations in the complex number field.Then by using the boundary value method to solve the equations, that the numerical solutions is the solutions of the original problem, and then analyze the error. Finally we use Matlab to conduct the numerical simulation, and give the images of the numerical solutions and errors, which show that the methods have high precision and good stability.Keywords: Schrödinger equation, Galerkin-Chebyshev spectral method, boundary value method, numerical solutions, high precision, stability目录摘要 (I)Abstract ................................................................................................................. I II 第1章绪论. (5)1.1课题研究的背景和意义 (5)1.2国内外研究现状 (6)1.3本文的主要研究内容 (6)第2章预备知识 (8)2.1克罗内克积的简介 (8)2.2Chebyshev多项式介绍及其性质 (9)2.3Chebyshev正交逼近的性质 (10)2.4投影算子的性质 (11)2.5本章小结 (12)第3章Galerkin-Chebyshev谱方法和边界值法 (13)3.1用Galerkin-Chebyshev谱方法求解椭圆型方程 (13)3.2用边界值法求解常微分方程 (14)3.3本章小结 (18)第4章求解二维薛定谔方程 (19)4.1区域和边界条件的处理 (19)4.1.1 区域的处理 (19)4.1.2 边界条件的处理 (21)4.2二维薛定谔方程的求解 (24)4.3误差分析 (25)4.4本章小结 (30)第5章数值模拟 (31)结论 (36)参考文献 (37)哈尔滨工业大学学位论文原创性声明及使用授权说明 .....错误!未定义书签。
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Chaotic billiards
Chaotic BilliardsNikolai ChernovRoberto MarkarianDepartment of Mathematics,University of Alabama at Birming-ham,Birmingham,AL35294,USAE-mail address:chernov@Instituto de Matem´a tica y Estad´ıstica“Prof.Ing.Rafael La-guardia”F acultad de Ingenier´ıa,Universidad de la Rep´u blica, C.C. 30,Montevideo,UruguayE-mail address:roma@.uyTo Yakov Sinai on the occasion of his70th birthdayThe authors are grateful to many colleagues who have read the manuscript and made numerous useful remarks,in particular P.Balint,D.Dolgopyat,C.Liverani,G.Del Magno,and H.-K.Zhang.It is a pleasure to acknowledge the warm hospitality of IMPA(Rio de Janeiro),where thefinal version of the book was prepared.We also thank the anonymous referees for helpful st but not the least,the book was written at the suggestion of Sergei Gelfand and thanks to his constant encouragement.Thefirst author was partially supported by NSF grant DMS-0354775(USA).The second author was partially supported by aProyecto PDT-Conicyt(Uruguay).ContentsPreface vii Symbols and notation ix Chapter1.Simple examples11.1.Billiard in a circle11.2.Billiard in a square51.3.A simple mechanical model91.4.Billiard in an ellipse111.5.A chaotic billiard:pinball machine15 Chapter2.Basic constructions192.1.Billiard tables192.2.Unbounded billiard tables222.3.Billiardflow232.4.Accumulation of collision times242.5.Phase space for theflow262.6.Coordinate representation of theflow272.7.Smoothness of theflow292.8.Continuous extension of theflow302.9.Collision map312.10.Coordinates for the map and its singularities322.11.Derivative of the map332.12.Invariant measure of the map352.13.Mean free path372.14.Involution38 Chapter3.Lyapunov exponents and hyperbolicity413.1.Lyapunov exponents:general facts413.2.Lyapunov exponents for the map433.3.Lyapunov exponents for theflow453.4.Hyperbolicity as the origin of chaos483.5.Hyperbolicity and numerical experiments503.6.Jacobi coordinates513.7.Tangent lines and wave fronts523.8.Billiard-related continued fractions553.9.Jacobian for tangent lines573.10.Tangent lines in the collision space583.11.Stable and unstable lines593.12.Entropy60iiiiv CONTENTS3.13.Proving hyperbolicity:cone techniques62 Chapter4.Dispersing billiards674.1.Classification and examples674.2.Another mechanical model694.3.Dispersing wave fronts714.4.Hyperbolicity734.5.Stable and unstable curves754.6.Proof of Proposition4.29774.7.More continued fractions834.8.Singularities(local analysis)864.9.Singularities(global analysis)884.10.Singularities for type B billiard tables914.11.Stable and unstable manifolds934.12.Size of unstable manifolds954.13.Additional facts about unstable manifolds974.14.Extension to type B billiard tables99 Chapter5.Dynamics of unstable manifolds1035.1.Measurable partition into unstable manifolds1035.2.u-SRB densities1045.3.Distortion control and homogeneity strips1075.4.Homogeneous unstable manifolds1095.5.Size of H-manifolds1115.6.Distortion bounds1135.7.Holonomy map1185.8.Absolute continuity1215.9.Two growth lemmas1255.10.Proofs of two growth lemmas1275.11.Third growth lemma1325.12.Fundamental theorem136 Chapter6.Ergodic properties1416.1.History1416.2.Hopf’s method:heuristics1416.3.Hopf’s method:preliminaries1436.4.Hopf’s method:main construction1446.5.Local ergodicity1476.6.Global ergodicity1516.7.Mixing properties1526.8.Ergodicity and invariant manifolds for billiardflows1546.9.Mixing properties of theflow and4-loops156ing4-loops to prove K-mixing1586.11.Mixing properties for dispersing billiardflows160 Chapter7.Statistical properties1637.1.Introduction1637.2.Definitions1637.3.Historic overview1677.4.Standard pairs and families169CONTENTS v7.5.Coupling lemma1727.6.Equidistribution property1757.7.Exponential decay of correlations1767.8.Central Limit Theorem1797.9.Other limit theorems1847.10.Statistics of collisions and diffusion1867.11.Solid rectangles and Cantor rectangles1907.12.A‘magnet’rectangle1937.13.Gaps,recovery,and stopping1977.14.Construction of coupling map2007.15.Exponential tail bound205 Chapter8.Bunimovich billiards2078.1.Introduction2078.2.Defocusing mechanism2078.3.Bunimovich tables2098.4.Hyperbolicity2108.5.Unstable wave fronts and continued fractions2148.6.Some more continued fractions2168.7.Reduction of nonessential collisions2208.8.Stadia2238.9.Uniform hyperbolicity2278.10.Stable and unstable curves2308.11.Construction of stable and unstable manifolds2328.12.u-SRB densities and distortion bounds2358.13.Absolute continuity2388.14.Growth lemmas2428.15.Ergodicity and statistical properties248 Chapter9.General focusing chaotic billiards2519.1.Hyperbolicity via cone techniques2529.2.Hyperbolicity via quadratic forms2549.3.Quadratic forms in billiards2559.4.Construction of hyperbolic billiards2579.5.Absolutely focusing arcs2609.6.Continued fractions2659.7.Singularities2669.8.Application of Pesin and Katok-Strelcyn theory2709.9.Invariant manifolds and absolute continuity2739.10.Ergodicity via‘regular coverings’274 Afterword279 Appendix A.Measure theory281 Appendix B.Probability theory291 Appendix C.Ergodic theory299 Index309vi CONTENTSBibliography311PrefaceBilliards are mathematical models for many physical phenomena where one or more particles move in a container and collide with its walls and/or with each other.The dynamical properties of such models are determined by the shape of the walls of the container,and they may vary from completely regular(integrable)to fully chaotic.The most intriguing,though least elementary,are chaotic billiards. They include the classical models of hard balls studied by L.Boltzmann in the XIX century,the Lorentz gas introduced to describe electricity in1905,as well as modern dispersing billiard tables due to Ya.Sinai and the stadium.Mathematical theory of chaotic billiards was born in1970when Ya.Sinai pub-lished his seminal paper[Sin70];and now it is only35years old.But during these years it grew and developed at a remarkable speed,and became a well-established andflourishing area within the modern theory of dynamical systems and statistical mechanics.It is no surprise that many young mathematicians and scientists attempt to learn chaotic billiards,in order to investigate some of them or explore related phys-ical models.But such studies are used to be prohibitively difficult for too many a novice and an outsider,not only because the subject itself is intrinsically quite complex,but to a large extend because of the lack of comprehensive introductory texts.True,there are excellent books covering general mathematical billiards[Ta95, KT91,KS86,GZ90,CFS82],but these barely touch upon chaotic models.There are surveys devoted to chaotic billiards as well,see[Sin00,Sz00,CM03],but those are expository,they only sketch selective arguments and rarely go down to‘nuts and bolts’.For the readers who want to look‘under the hood’and become professional (and we speak of graduate students and young researchers here),there is not much choice left:either learning from their advisors or other experts by way of personal communication,or reading the original publications(most of them very long and technical articles translated from Russian).Then students quickly discover that some essential facts and techniques can only be found in the middle of long dense papers.Worse yet,some of those facts have never even been published–they exist as folklore.This book attempts to present the fundamentals of the mathematical theory of chaotic billiards in a systematic way.We cover all the basic facts,provide full proofs,intuitive explanations and plenty of illustrations.Our book can be used by students and self-learners–it starts with the most elementary examples and formal definitions,and then takes the reader step by step into the depth of Sinai’s theory of hyperbolicity and ergodicity of chaotic billiards,as well as more recent achievements related to their statistical properties(decay of correlations and limit theorems).viiviii PREF ACEThe reader should be warned that our book is designed for active learning.It contains plenty exercises of various kinds;some constitute small steps in the proofs of major theorems,some others present interesting examples and counterexamples, yet others are given for the reader’s practice(some exercises are actually quite challenging).The reader is strongly encouraged to do exercises when reading the book,as this is the best way to grasp the main concepts and eventually master the techniques of billiard theory.The book is restricted to two-dimensional chaotic billiards,primarily dispersing tables by Sinai and circular-arc-tables by Bunimovich(with some other planar chaotic billiards reviewed in the last chapter).We have several compelling reasons for such a confinement.First,Sinai’s and Bunimovich’s billiards are the oldest and best explored(for instance,statistical properties are established only for them and for no other billiard model);the current knowledge of other chaotic billiards is much less complete;the work on some of them(most notably,hard ball gases)is currently under way and should be perhaps the subject of future textbooks.Second,the two classes presented here constitute the core of the entire theory of chaotic billiards,all its apparatus is built upon the original works by Sinai and Bunimovich;but their fundamental works are hardly accessible to today’s students or researchers;there have been no attempts to update or republish their results since the middle1970s (after Gallavotti’s book[Ga74]).Our book makes such an attempt.We do not cover polygonal billiards,even though some of them are mildly chaotic(ergodic); for surveys of polygonal billiards see[Gut86,Gut96].We assume that the reader is familiar with standard graduate courses in math-ematics–linear algebra,measure theory,topology,Riemannian geometry,complex analysis,probability theory.We also assume knowledge of ergodic theory;although the latter is not a standard graduate course,it is absolutely necessary for reading this book;we do not attempt to cover it here,though,as there are many excellent texts around[Wa82,Man83,KH95,Pet83,CFS82,Sin00,BrS02,Dev89, Sin76](see also our previous book[CM03]).For the reader’s convenience,we pro-vide basic definitions and facts from ergodic theory,probability theory,and measure theory in Appendices.Symbols and notationD billiard table Section2.1Γboundary of the billiard table 2.1Γ+union of dispersing components of the boundaryΓ 2.1Γ−union of focusing components of the boundaryΓ 2.1Γ0union of neutral(flat)components of the boundaryΓ 2.1˜Γregular part of the boundary of billiard table 2.1Γ∗Corner points on billiard table 2.1 degree of smoothness of the boundaryΓ=∂D 2.1 n normal vector to the boundary of billiard table 2.3 T tangent vector to the boundary of billiard table 2.6 K(signed)curvature of the boundary of billiard table 2.1Φt billiardflow 2.5Ωthe phase space of the billiardflow 2.5˜ΩPart of phase space where dynamics is defined at all times 2.5πq,πv projections ofΩto the position and velocity subspaces 2.5ωangular coordinate in phase spaceΩ 2.6η,ξJacobi coordinates in phase spaceΩ 3.6µΩinvariant measure for theflowΦt 2.6 F collision map or billiard map 2.9 M collision space(phase space of the billiard map) 2.9˜M part of M where all iterations of F are defined 2.9ˆM part of M where all iterations of F are smooth 2.11 r,ϕcoordinates in the collision space M 2.10µinvariant measure for the collision map F 2.12 S0boundary of the collision space M 2.10 S±1singularity set for the map F±1 2.10 S±n singularity set for the map F±n 2.11 S±∞same as∪n≥1S±n 4.11 Q n(x)connected component of M\S n containing x 4.11 V(=dϕ/dr)slope of smooth curves in M 3.10τreturn time(intercollision time) 2.9¯τmean return time(mean free path) 2.12λ(i)x Lyapunov exponent at the point x 3.1E s x,E u x stable and unstable tangent subspaces at the point x 3.1 C s x,C u x stable and unstable cones at the point x 3.13Λ(minimal)factor of expansion of unstable vectors 4.4 B the curvature of wave fronts 3.7ixx SYMBOLS AND NOTATIONR collision parameter 3.6 H k homogeneity strips 5.3 S k lines separating homogeneity strips 5.3 k0minimal nonzero index of homogeneity strips 5.3 M H new collision space(union of homogeneity strips) 5.4 h holonomy map 5.7 I involution map 2.14 m Lebesgue measure on lines and curves 5.9 |W|length of the curve W 4.5 |W|p length of the curve W in the p-metric 4.5 J W F n(x)Jacobian of the restriction of F n to the curve W at the point x∈W 5.2 r W(x)distance from x∈W to the nearest endpoint of the curve W 4.12 r n(x)distance from F n(x)to the nearest endpoint of the componentof F n(W)that contains F n(x) 5.9 p W(x)distance from x∈W to the nearest endpoint of W in the p-metric 4.13ρW(x)u-SRB density on unstable manifold W 5.2 ‘same order of magnitude’ 4.3 L ceiling function for suspensionflows 2.9CHAPTER1Simple examplesWe start with a few simple examples of mathematical billiards,which will help us introduce basic features of billiard dynamics.This chapter is for the complete beginner.The reader familiar with some billiards may safely skip it–all the formal definitions will be given in Chapter2.1.1.Billiard in a circleLet D denote the unit disk x2+y2≤1.Let a point-like(dimensionless)particle move inside D with constant speed and bounce offits boundary∂D according to the classical rule the angle of incidence is equal to the angle of reflection,see below.Denote by q t=(x t,y t)the coordinates of the moving particle at time t and by v t=(u t,w t)its velocity vector.Then its position and velocity at time t+s can be computed byx t+s=x t+u t s u t+s=u t(1.1)y t+s=y t+w t s w t+s=w tas long as the particle stays inside D(makes no contact with∂D).When the particle collides with the boundary∂D={x2+y2=1},its velocity vector v gets reflected across the tangent line to∂D at the point of collision,see Fig.1.1.2 1.SIMPLE EXAMPLESAfter the reflection,the particle resumes its free motion(1.1)inside the disk D,until the next collision with the boundary∂D.Then it bounces offagain,and so on.The motion can be continued indefinitely,both in the future and the past.For example,if the particle runs along a diameter of the disk,its velocity vector will get reversed at every collision;and the particle will keep running back and forth along the same diameter forever.Other examples of periodic motion are shown on Fig.1.2,where the particle traverses the sides of some regular polygons.Figure1.2.Periodic motion in a circle.In the studies of dynamical systems,the primary goal is to describe the evolu-tion of the system over long time periods and its asymptotic behavior in the limit t→∞.We will focus on such a description.Let us parameterize the unit circle x2+y2=1by the polar(counterclockwise) angleθ∈[0,2π](sinceθis a cyclic coordinate,its values0and2πare identified). Also,denote byψ∈[0,π]the angle of reflection as shown on Fig.1.1.Remark1.2.We note thatθis actually an arc length parameter on the circle ∂D;when studying more general billiard tables D we will always parameterize the boundary∂D by its arc length.Instead ofψ,a reflection can also be described by the angleϕ=π/2−ψ∈[−π/2,π/2]that the postcollisional velocity vector makes with the inward normal to∂D.In fact,all principal formulas in this book will be given in terms ofϕ,rather thanψ,but for the moment we proceed withψ.For every n∈Z,letθn denote the n th collision point andψn the corresponding angle of reflection.Exercise1.3.Show thatθn+1=θn+2ψn(mod2π)(1.3)ψn+1=ψnfor all n∈Z.We make two important observations now:•All the distances between reflection points are equal.•The angle of reflection remains unchanged.1.1.BILLIARD IN A CIRCLE3Corollary 1.4.Let(θ0,ψ0)denote the parameters of the initial collision.Thenθn=θ0+2nψ0(mod2π)ψn=ψ0.Every collision is characterized by two numbers:θ(the point)andψ(the angle).All the collisions make the collision space with coordinatesθandψon it.It is a cylinder becauseθis a cyclic coordinate,see Fig.1.3.We denote the collision spaceby M.The motion of the particle,from collision to collision,corresponds to a map F:M→M,which we call the collision map.For a circular billiard it is given by equations(1.3).Observe that F leaves every horizontal level Cψ={ψ=const}of the cylinder M invariant.Furthermore,the restriction of F to Cψis a rotation of the circle Cψthrough the angle2ψ.The angle of rotation continuously changes from circle to circle,growing from0at the bottom{ψ=0}to2πat the top{ψ=π}(thus the top and bottom circles are actually keptfixed by F).The cylinder M is“twisted upward”(“unscrewed”)by the map F,see Fig.1.3.1A sequence of points xn∈C on a circle C is said to be uniformly distributed if for any interval I⊂C we have lim N→∞#{n:0<n<N,a n∈I}/N=length(I)/length(C).4 1.SIMPLE EXAMPLESExercise 1.6.Show that every segment of the particle’s trajectory between consecutive collisions is tangent to the smaller circle S ψ={x 2+y 2=cos 2ψ}concentric to the disk D .Show that if ψ/πis irrational,the trajectory densely fills the ring between ∂D and the smaller circle S ψ(see Fig.1.4).Remark:one can clearly see on Fig.1.4that the particle’s trajectory looks denser near the inner boundary of the ring (it “focuses”on the inner circle).If the particle’s trajectory were the path of a laser ray and the border of the unit disk were a perfect mirror,then it would feel “very hot”there,on the inner circle.For this reason,the inner circle is called a caustic (which means “burning”in Greek).Figure 1.4.A nonperiodic trajectory.Exercise 1.7.Can the trajectory of the moving particle be dense is the entire disk D ?(Answer:No.)Exercise 1.8.Does the map F :M →M preserve any absolutely continuous invariant measure dµ=f (θ,ψ)dθdψon M ?Answer:any measure whose density f (θ,ψ)=f (ψ)is independent of θis F -invariant.Next,we can fix the speed of the moving particle due to the following facts.Exercise 1.9.Show that v t =const,so that the speed of the particle remains constant at all times.Exercise 1.10.Show that if we change the speed of the particle,say we set v new =c v old with some c >0,then its trajectory will remain unchanged,up to a simple rescaling of time:q new t =q old ct and v new t =v old ct for all t ∈R .Thus,the speed of the particle remains constant and its value is not important.It is customary to set the speed to one: v =1.Then the velocity vector at time t can be described by an angular coordinate ωt so that v t =(cos ωt ,sin ωt )and ωt ∈[0,2π]with the endpoints 0and 2πbeing identified.Now,the collision map F :M →M represents collisions only.To describe the motion of the particle inside D ,let us consider all possible states (q,v ),where q ∈D is the position and v ∈S 1is the velocity vector of the particle.The space of all states (called the phase space )is then a three-dimensional manifold Ω:=D ×S 1,which is,of course,a solid torus (doughnut).The motion of the billiard particle induces a continuous group of transforma-tions of the torus Ωinto itself.Precisely,for every (q,v )∈Ωand every t ∈R the billiard particle starting at (q,v )will come to some point (q t ,v t )∈Ωat time t .1.2.BILLIARD IN A SQUARE5 Thus we get a map(q,v)→(q t,v t)onΩ,which we denote byΦt.The family of maps{Φt}is a group,i.e.Φt◦Φs=Φt+s for all t,s∈R.This family is called the billiardflow on the phase space.Let us consider a modification of the circular billiard.Denote by D+the upper half disk x2+y2≤1,y≥0,and let a point particle move inside D+and bounce off∂D+.(A delicate question arises here:what happens if the particle hits∂D+at (1,0)or(−1,0),since there is no tangent line to∂D+at those points?We address this question in the next section.)6 1.SIMPLE EXAMPLESFigure1.6.Billiard in a square.then stops and its trajectory terminates.We will discuss this exceptional situation later,first we consider regular trajectories that never hit the vertices.Let v t=(u t,w t)denote the velocity vector of the moving particle at time t(in the x,y coordinates).If it hits a vertical side of D at time t,then u t changes sign (u t+0=−u t−0)and w t remains unchanged.If the particle hits a horizontal side of D,then w t changes sign(w t+0=−w t−0)and u t remains unchanged.Thus, (1.4)u t=(−1)m u0and w t=(−1)n w0,where m and n denote the number of collisions with vertical and,respectively, horizontal sides of D during the time interval(0,t).Exercise1.13.Show that if u0=0and w0=0(and assuming the particle never hits a vertex),then all the four combinations(±u0,±w0)appear along the particle’s trajectory infinitely many times.Next we make use of the trick shown on Fig.1.5.Instead of reflecting the trajectory of the billiard particle in a side of∂D,we reflect the square D across that side and let the particle move straight into the mirror image of D.If we keep doing this at every collision,our particle will move along a straight line through the multiple copies of D obtained by successive reflections(the particle“pierces”a chain of squares,see Fig.1.7).This construction is called the unfolding of the billiard trajectory.To recover the original trajectory in D,one folds the resulting string of adjacent copies of D back onto D.We denote the copies of D by(1.5)D m,n={(x,y):m≤x≤m+1,n≤y≤n+1}Exercise1.14.Show that if m and n are even,then the folding procedure transforms D m,n back onto D=D0,0by translations x→x−m and y→y−n, thus preserving orientation of both x and y.If m is odd,then the orientation of x is reversed(precisely,x→m+1−x).If n is odd,then the orientation of y is reversed(precisely,y→n+1−y).Observe that these rules do not depend on the particular trajectory that was originally unfolded.The squares D m,n with m,n∈Z tile,like blocks,the entire plane R2.Any regular billiard trajectory unfolds into a directed straight line on the plane,and any directed line(which avoids the sites of the integer lattice)folds back into a billiard trajectory.A trajectory hits a vertex of D iffthe corresponding line runs into a site of the integer lattice.1.2.BILLIARD IN A SQUARE78 1.SIMPLE EXAMPLESThe phase space of the billiard system in the unit square D is the three-dimensional manifoldΩ=D×S1,cf.the previous section.The billiardflow Φt is defined for all times−∞<t<∞on regular trajectories.On exceptional trajectories(which hit a vertex of D at some time),theflow is only defined until the trajectory terminates in a vertex.Exercise1.18.Show that the set of exceptional trajectories is a countable union of2D surfaces inΩ.We see that the set of exceptional trajectories is negligible in the topological and measure-theoretic sense(it has zero Lebesgue measure and is an Fσset,i.e.a countable union of nowhere dense closed subsets),but still its presence is bother-some.For the billiard in a square,though,one can get rid of them altogether by extending the billiardflow by continuity:Exercise1.19.Show that theflowΦt can be uniquely extended by continuity to all exceptional trajectories.In that case every trajectory hitting a vertex of D will simply reverse its course and run straight back,see Fig.1.8.Figure1.8.Extension of theflow near a vertex.The above extension defines the billiardflowΦt on the entire phase spaceΩand makes it continuous everywhere.We will assume this extension in what follows.We remark,however,that in generic billiards such nice extensions are rarely possible–see Section2.8.Now,the action of theflowΦt on the phase spaceΩcan be fully described as follows.For every unit vector v0=(u0,w0)∈S1,consider the setL v0={(q,v)∈Ω:q∈D,v=(±u0,±w0)}(the two signs are,of course,independent).Due to(1.4),each set L vremains invariant under theflowΦt.Supposefirst that u0=0and v0=0,then L v0is the union of four squares, obtained by“slicing”Ωat the four“levels”corresponding to the vectors(±u0,±w0), see Fig.1.9.Exercise1.20.Check that the four squares constituting the set L vcan be glued together along their boundaries and obtain a smooth closed surface without boundary(a2×2torus)T2v,on which the billiardflow will coincide with the linear flow along the vector v0(i.e.theflow on T2vwill be defined by differential equations˙x=u0,˙y=w0).Hint:the assembly of the torus T2v0from the squares of L visvery similar to the reduction of the billiard dynamics in D to the geodesicflow on the2×2torus described above(in fact,these two procedures are equivalent).1.3.A SIMPLE MECHANICAL MODEL9x1x201Figure1.10.Two particles in a unit interval.Next we describe collisions.When a particle hits a wall,it simply reverses its velocity.When the two particles collide with each other,we denote by u−i the precollisional velocity and by u+i the postcollisional velocity of the i th particle,i= 1,2.The law of elastic collisions requires the conservation of the total momentum, i.e.m1u+1+m2u+2=m1u−1+m2u−210 1.SIMPLE EXAMPLESand the total kinetic energy,i.e.(1.6)m1[u+1]2+m2[u+2]2=m1[u−1]2+m2[u−2]2.Solving these equations gives2m2u+1=u−1+(u−1−u−2)m1+m2(we recommend the reader derives these formulas for an exercise).Note that if m1=m2,then the particles simply exchange their velocities:u+1=u−2and u+2= u−1.The variables x i and u i are actually inconvenient,we will work with new vari-ables defined by(1.7)q i=x i√m ifor i=1,2.Now the positions of the particles are described by a point q= (q1,q2)∈R2(it is called a configuration point).The set of all configuration points (called the configuration space)is the right triangleD={q=(q1,q2):0≤q1/√m2≤1}.The velocities of the particles are described by the vector v=(v1,v2).Note that the energy conservation law(1.6)implies that v =const,thus we can set v =1.The state of the system is described by a pair(q,v).The configuration point q moves in D with velocity vector v.When thefirst particle collides with the wall (x1=0),the configuration point hits the left side q1=0of the triangle D.When the second particle collides with the wall(x2=1),the point q hits the upper side q2/√m1=q2/√1.4.BILLIARD IN AN ELLIPSE11a 2+y 2F 1F 2Figure 1.12.A trajectory passing through the foci.12 1.SIMPLE EXAMPLESExercise1.23.Show that every trajectory passing through the foci F1and F2converges to the major axis of the ellipse(the x axis).By the way,the major and the minor axes of the ellipse are clearly two periodic trajectories–they run back and forth between their endpoints.In Section1.1we used the coordinatesψandθto describe collisions in a circular billiard,and the cyclic coordinateθwas actually the arc length parameter on the circle(Remark1.2).Here we use two coordinatesψand r,whereψis the same angle of reflection as in Section1.1and r is an arclength parameter on the ellipse. We choose the reference point r=0as the rightmost point(a,0)on the ellipse and orient r counterclockwise.Note that0≤r≤|∂D|and0≤ψ≤π.The collision space M is again a cylinder whose base is the ellipse and whose height isπ.It is shown on Fig.1.13as a rectangle[0,|∂D|]×[0,π],but we keep in mind that the left and right sides of this rectangle must be identified.The motion of the billiard particle,from collision to collision,induces the collision map F:M→M.Exercise1.24.Verify that the trajectories passing through the foci lie on a closed curve on the surface M.Determine its shape.Answer:it is the∞-shaped curve on Fig.1.13that separates the white and grey areas.Thus,the trajectories passing through the foci make a special(one-dimensional) family in M.。
人前脱脂蛋白A-I的表达[发明专利]
专利名称:人前脱脂蛋白A-I的表达
专利类型:发明专利
发明人:波兰·阿莱克斯,哥伯特·简,伍尔福特·厄恩斯特申请号:CN88103118
申请日:19880527
公开号:CN88103118A
公开日:
19881214
专利内容由知识产权出版社提供
摘要:本发明提供包括为人前脱脂蛋白A-I编码DNA序列的重组DNA序列,其中部分天然编码序列被为同样氨基酸编码但由不同核苷酸序列组成的DNA片段取代,从而降低或防止了发夹形成;还提供含这些DNA序列的克隆和表达载体,用这些表达载体转化的细胞培养物或微生物,以及用其生产前脱脂蛋白A-I的方法。
申请人:UCB公司
地址:比利时布鲁塞尔
国籍:BE
代理机构:中国国际贸易促进委员会专利代理部
代理人:李瑛
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Lees-Edwards boundary conditions for lattice Boltzmann
1
Boltzmann methods there is the additional problem that the maximum shear rate is limited by the existence of a maximum shear rate of order 1/Ly with Ly the transverse system size. This is required to ensure that all fluid velocities remain small compared to unity in lattice units[3]. The original LEbc were implemented for a system of particles, with positions (rx , ry , rz ) and velocities (vx , vy , vz ) within a box of dimension (Lx , Ly , Lz ). Lees and Edwards showed that by implementing a new kind of boundary condition in a MD simulation a state with a uniform shear velocity profile u = γy ˙ ex (where ex is the unity vector in x-direction) could be achieved. The method consists of a simple recipe that imposes periodic boundary conditions on particles leaving the simulation box in the directions perpendicular to the velocity gradient ey . Particles leaving the box in the ey direction (ry > Ly ) will be reinserted with their x-velocity increased by the shear velocity ∆u = γL ˙ y ex added at a position given by their periodic image displaced by the time-dependent offset dx = t∆ux , t being the time elapsed from an appropriate origin of times. If the particles leave the box in the −ey direction (ry < 0) they reappear at the position of their periodic image displaced by −dx and have the shear velocity ∆u subtracted from their x-velocity. We can describe the boundary conditions by providing the new positions r′ and velocities v ′ of the particles after the particles have been moved by the dynamics of the algorithm rz ≥ Lz (rx mod Lx ) + dx ′ (rx mod Lx ) 0 ≤ rz < Lz rx = (rx mod Lx ) − dx rz < 0
等离子体书籍全集
等离⼦体书籍全集Plasma Physics Books(⾮常全的等离⼦体物理书单)General PublicP. I. John, Plasma Sciences and the Creation of Wealth, Tata-McGraw-Hill, New Delhi, 2005.Yaffa & Shalom Eliezer, The Fourth State of Matter, Hilger, Bristol, 1989 (2nd edition, 2001).John W. Freeman, Storms in Space, Cambridge, 2001.Kenneth R. Lang, The Cambridge Encyclopedia of the Sun, Cambridge Press, 2001.Hans Wilhelmsson, Fusion: A Voyage Through the Plasma Universe, IOP, 1999. Steven T. Suess and Bruce T. Tsurutani, From the Sun: Auroras, Magnetic Storms, Solar Flares, Cosmic Rays, American Geophysical Union, 1998. T. Kenneth Fowler, The Fusion Quest, Johns Hopkins Press, 1997. Kenneth R. Lang, Sun, Earth and Sky, Springer-Verlag, Berlin, 1995, 1997. Gareth Wynn-Williams, The Fullness of Space, Cambridge, 1992.Paul D. Thompson, Gases & Plasmas, Lippincott Company, Philadelphia, 1966 (out of print)IntroductoryPlasma Science: Basic Physics of the Local Cosmos, National Academy Press, Washington D.C., 2004.A. A. Harms et al., Principles of Fusion Energy, World Scientific, 2000. R. Hazeltine and F. Waelbroeck, The Framework of Plasma Physics, Perseus Books, 1998.Plasma Science: From Fundamental Research to Technological Applications, National Academy Press, Washington D.C., 1995.R. J. Goldston and P. H. Rutherford, Introduction to Plasma Physics, IOP, 1995.Richard Dendy, Plasma Physics, Cambridge, 1993, 1995.Francis Chen, Introduction to Plasma Physics and Controlled Fusion, Plenum Press, 1974, 1988.Basic Plasma PhysicsNonequilibrium Phenomena in Plasmas, A. Surjalal Sharma and Predhiman Kaw, eds., Springer, 2005.Takashi Fujimoto, Plasma Spectroscopy, Clarendon Press, Oxford, 2004. J. Goedbloed and S. Poedts, Principles of Magnetohydrodynamics: With Applications to Laboratory and Astrophysical Plasmas, Cambridge, 2004. T. Tajima, Computational Plasma Physics with Applications to Fusion and Astrophysics, Westview Press, 2004.William Kruer, The Physics of Laser Plasma Interactions, Westview Press,2003.Y. Elskens and D. Escande, Microscopic Dynamics of Plasmas and Chaos, IOP, 2002.Per Helander and Dieter J. Sigmar, Collisional Transport in Magnetized Plasmas, Cambridge, 2002.Paul Gibbon, Short Pulse Laser Interactions with Matter, Imperial College Press, 2000.R. Davidson, The Physics of Nonneutral Plasmas, Imperial College Press, 2001.V. A. Rozhansky and L. D. Tsendin, Transport Phenomena in Partially Ionized Plasma, Taylor & Francis Group, London, 2001.H. Wilhelmsson and E. Lazzaro, Reaction-Diffusion in the Physics of Hot Plasmas, IOP, 2000.J. T. Mendonca, Theory of Photon Acceleration, IOP, 2000.Paul Gibbon, Short Pulse Laser Interactions with Matter, Imperial College Press, 2000.J. Weiland, Collective Modes in Inhomogeneous Plasmas, IOP, 1999. S. S. Moiseev, V. Oraevsky, and V. Pungin, Non-Linear Instabilities in Plasmas and Hydrodynamics, IOP Press, 1999.Vladimir Fortov and Igor Iakubov, The Physics of Non-Ideal Plasma, Imperial College Press, 1999.Plasma Chemistry, L. S. Polak and Yu A. Lebedev, eds., Cambridge, 1999. V. V. Antsiferov and G. I. Smirnov, Coherent Radiation Processes in Plasmas, Cambridge, 1999.M. Brambilla, Kinetic Theory of Plasma Waves: Homogeneous Plasmas, Oxford, 1998.Hans R.Griem, Principles of Plasma Spectroscopy,Cambridge, 1997.W. Horton and Y-H Ichikawa, Chaos and Structures in Nonlinear Plasmas, World Scientific, 1996.The Physics of Dusty Plasmas, P. Shukla, D. Mendis & V. Chow, editors, World Scientific, 1996.Toshiro Ohnuma, Radiation Phenomena in Plasmas, World Scientific, 1996.C. S. Liu and V. K. Tripathi, Interaction of Electromagnetic Waves with Electron Beams and Plasmas, World Scientific, 1995 E. A. Oks, Plasma Spectroscopy, Springer-Verlag, 1995.W. Lochte-Holtgreven, Plasma Diagnostics, North-Holland, 1968, APS 1995. Dusty and Dirty Plasmas, Noise, and Chaos in Space and in the Laboratory, H. Kikuchi, editor, Plenum Press, NY, 1995.Sanborn C. Brown, Basic data of plasma physics, AIP Press, 1994.V. Shevelko and L. Vainshtein, Atomic Physics for Hot Plasmas, Oxford, 1993.Setsuo Ichimaru, Statistical Plasma Physics, Vol. 1. Basic Principles, Vol. 2. Condensed Plasmas, Perseus Books, 1992, 1994.Thomas Stix, Waves in Plasmas, AIP Press, 1992.Nonlinear and Relativistic Effects in Plasmas, V. Stefan, ed., AIP, 1992.A. Mikhailovskii, Electromagnetic Instabililties in an Inhomogeneous Plasma, IOP, 1992.R. A. Cairns, Radiofrequency Heating of Plasmas, IOP, 1991.Ronald Davidson, An Introduction to the Physics of Nonneutral Plasma, Addison-Wesley, 1990.W. Manheimer and C. Lashmore-Davies, MHD and Microinstabilities in Confined Plasmas, IOP, 1989.R. C. Cross, An Introduction to Alfven Waves, Hilger, Bristol, 1988.A. Galeev and R. Sudan, Basic Plasma Physics, North-Holland, 1989(selections from Handbook of Plasma Physics, Vol. 1 & 2, 1983, 1984) J. P. Freidberg, Ideal Magnetohydrodynamics, Plenum Pr., NY, 1987. Plasma Waves and Instabilities, C. L. Grabbe, ed., American Assoc. of Physics Teachers, 1986. Dwight Nicholson, Introduction to Plasma Theory, Wiley, 1983.E. Lifshitz and L. Pitaevskii, Physical Kinetics: Volume 10, Elsevier, 1981.N. Krall and A. Trivelpiece, Principles of Plasma Physics, McGraw-Hill, 1973.Fusion PlasmasJeffrey Freidberg, Plasma Physics and Fusion Energy, Cambridge Univ. Press, 2007.Plasma Physics: Confinement, Transport and Collective Effects, A. Dinklage et al., eds., Springer-Verlag, 2005.G. McCracken and P. Stott, Fusion: The Energy of the Universe, Elsevier, 2005.J. Wesson, Tokomaks, 3rd ed., Oxford Univ. Press, 2004.C. Braams and P. Stott, Nuclear Fusion: Half a Century of Magnetic Confinement Fusion, IOP Press, 2002.R. Davidson and H. Qin, Physics of Intense Charged Particle Beams in High Energy Accelerators, Imperial College Press,2001.P. C. Stangeby, The Plasma Boundary of Magnetic Fusion Devices, IOP Press, 2000.Paul M. Bellan, Spheromaks, Imperial College Press, 2000.M. Liberman, J. Degroot, A. Toor, and L. Spielman, Physics of High-Density Z-Pinch Plasmas, Springer, 1999.M. Wakatani, Stellerator and Heliotron Devices, Oxford Univ. Press, 1998. J. Lindl, Inertial Confinement Fusion, Springer, 1998.A. B. Mikahilovskii, Instabilities in a Confined Plasma, IOP, 1998. Laser Plasma Interactions 5: Inertial Confinement Fusion, M. Hooper, ed., IOP, 1996.Physics of High Energy Particles in Toroidal Systems, T. Tajima and M.Okamoto, eds., AIP Press, 1994.M. N. Rosenbluth, New Ideas in Tokamak Confinement, Springer, 1994. B. Kadomtsev and I. Kurchatov, Tokamak Plasma: A Complex Physics System, IOP, 1993.M. Nezlin and I. Kurchatov, Physics of Intense Beams in Plasmas, IOP, 1993.H. L. Berk, Fusion, Magnetic Confinement, in Encyclopedia of Applied Physics, Vol. 6, pp. 575-607, VCH Publishers, Inc., 1993.Richard D. Hazeltine and James D. Meiss, Plasma Confinement, Perseus Books, 1992.High-frequency Plasma Heating, ed. A. Litvak, AIP, 1992.K. Nishikawa, and M. Wakatani, Plasma Physics: Basic Theory with Fusion Applications, Springer-Verlag, 1990.Kenro Miyamoto, Plasma Physics for Nuclear Fusion, MIT Press, 1980, 1989. J. Reece Roth, Introduction to Fusion Energy, Lincoln Rembrandt, Charlottesville, 1986.Weston Stacey, Fusion: An Introduction to the Physics and Techniques of Magnetic Confinement Fusion, Wiley, 1984. Space PlasmasThe Mars Plasma Environment, C. T. Russell, ed., Springer, 2007. Cosmic Magnetic Fields, R. Wielebinski and R. Beck, eds., Springer-Verlag, 2005.Wolfgang Kundt, Astrophysics: A New Approach, Springer-Verlag, 2005.D. A. Gurnett and A. Bhattacharjee, Introduction to Plasma Physics with Space and Laboratory Applications, Cambridge, 2005.James Lequeux, The Interstellar Medium, Springer-Verlag, 2005.A. C. Das, Space Plasma Physics: An Introduction, Narosa Publishing House, New Delhi, 2004.Gunther Rudiger and Rainer Hollerbach, The Magnetic Universe: Geophysical and Astrophysical Dynamo Theory , Wiley-VCH, 2004.Gerd W. Prolss, Physics of the Earth's Space Environment, Springer-Verlag, 2004.Solar and Space Weather Radiophysics, Astrophysics and Space Science Library, Vol. 314, Dale Gary and C. Keller, eds., Kluwer, 2004.J. Goedbloed and S. Poedts, Principles of Magnetohydrodynamics: With Applications to Laboratory and Astrophysical Plasmas, Cambridge, 2004. M-B Kallenrode, Space Physics: An Intro to Plasmas and Particles in the Heliosphere and Magnetospheres, Springer-Verlag, 2001, 2004.Toshi Tajima, Computational Plasma Physics: With Applications to Fusion & Astrophysics, Perseus, 2004.Markus Aschwanden, Physics of the Solar Corona, Springer-Verlag, 2004. Exploration of the Outer Heliosphere and the Local Interstellar Medium,NAS/NRC, National Academies Press, Washington, DC, 2004.Space Plasma Simulation, Jorg Buchner et al., eds.,Springer-Verlag, 2003 Alan C. Tribble, The Space Environment: Implications for Spacecraft Design, Princeton, 2003.Arnoldo O. Benz, Plasma Astrophysics: Kinetic Processes in Solar and Stellar Coronae, Kluwer Academic Publ., 2002. Syun-Ichi Akasofu, Exploring the Secrets of the Aurora, Kluwer Academic Publ., 2002.Arnold Hanslmeier, The Sun and Space Weather, Kluwer Academic Publ., 2002.H. Kikuchi, Electrodynamics in Dusty and Dirty Plasmas -Gravito-Electrodynamics and EHD, Kluwer, 2001.Space Weather, Paul Song, Howard J. Singer, and George L. Siscoe, eds., Geophys. Mono. 125, American Geophysical Union, 2001.Plasma Astrophysics, B. Coppi et al, eds.Vol. 142, Int'l School of Physics Enrico Fermi, 2000.E. Priest and T. Forbes, Magnetic Reconnection: MHD Theory and Applications, Cambridge, 2000.F. Verheest, Waves in Dusty Space Plasmas, Kluwer, 2000.A. Choudhuri, The Physics of Fluids and Plasmas: An Intro for Astrophysicists, Cambridge, 1999.Jorg Buchner, Plasma Astrophysics and Space Physics, Kluwer Academic Publ., 1999.Vinod Krishan, Astrophysical Plasmas and Fluids, Kluwer Academic Publ., 1999.Magnetic Helicity in Space and Laboratory Plasmas, M. R. Brown, R. C. Canfield, A. A. Pevtsov, eds., Geophys. Mono. 111, American Geophysical Union, 1999.Sun-Earth Plasma Connections, J. L. Burch, R. Carovillano, S. Antiochos, ed., Geophys. Mono. 109, American Geophysical Union, 1999. Measurement Techniques in Space Plasmas, Particles, ...Fields, R. F. Pfaff, J. Borovsky, D. Young, eds., Geophys. Mono. volumes 102, 103, American Geophysical Union, 1998.D. Bryant, Electron Acceleration in the Aurora and Beyond, IOP, 1998. M.-B. Kallenrode, Space Physics: Plasmas and particles in the Heliosphere and Magnetosheres, Springer, 1998.T. Tajima and K. Shibata, Plasma Astrophysics, Addison-Wesley, 1997. R. A. Treumann and W. Baumjohann, Advanced Space Plasma Physics, World Scientific, 1997.J. F. Lemaire, D. Heynderickx, and D. N. Baker, eds., Radiation Belts: Models and Standards, Geophys. Mono. 97, American Geophysical Union, 1996. W. Baumjohann and R. A. Treumann, Basic Space Plasma Physics, World Scientific, 1996.V. V. Zheleznyakov, Radiation in Astrophysical Plasmas, Kluwer Academic Publ., Dordrecht, 1996.The Physics of Dusty Plasmas, P. Shukla, D. Mendis and V. Chow, eds., World Scientific, 1996.Plasma Astrophysics and Cosmology, Anthony L. Peratt, ed., Kluwer, 1995. Margaret Kivelson and Chris Russell, Introduction to Space Physics, Cambridge, 1995.J. Buchner, Physics of Space Plasmas, MIT Press, 1995.Charles F. Kennel, Convection and Substorms, Oxford Univ. Press, 1995. Leonard F. Burlaga, Interplanetary Magnetohydrodynamics, Oxford Press, 1995.P. Sturrock, Plasma Physics: An Intro to the Theory of Astrophysical, Geophysical, and Laboratory Plasmas, Cambridge, 2004.J. G. Kirk, D. B. Melrose, and E. R. Priest, Plasma Astrophysics, Springer-Verlag, 1994.Sergei Sazhin, Whistler-mode Waves in a Hot Plasma, Cambridge Univ. Press, 1993.S. Peter Gary, Theory of Space Plasma Microinstabilities, Cambridge, 1993. Anthony Peratt, Physics of the Plasma Universe, Springer-Verlag, 1992. George Parks, Physics of Space Plasmas, Addison-Wesley, 1991. Modeling Magnetospheric Plasma Processes, G. Wilson, ed., American Geophysical Union, 1991.F. Curtis Michel, Theory of Neutron Star Magnetospheres, U. Chicago, 1991. Numerical Simulation of Space Plasmas, B. Lembege and J. Eastwood, eds., North-Holland, 1988.Donald Melrose, Instabilities in Space and Laboratory Plasmas, Cambridge, 1986.Eric Priest, Solar Magnetohydrodynamics, Reidel, 1985.Plasma TechnologyPlasma Technology for Textiles, R. Shishoo, ed., Woodhead Publ., Cambridge, 2007.The Physics and Technology of Ion Sources, Ian Brown, ed., Wiley, 2004.A. Fridman and L. Kennedy, Plasma Physics and Engineering, Taylor and Francis, 2004.Emerging Applications of Vacuum-ARC-Produced Plasma, Ion, and Electron Beams, E. Oks and I. Brown, eds, Kluwer, 2003.Bundesministerium fur Bildung und Forschung, Plasma Technology, BMBF (www.bmbf.de), Germany, 2001 (in German and English -www.bmbf.de/pub/plasma_technology.pdf)J. Reece Roth, Industrial Plasma Engineering, Vol. 2 - Applications, IOP, 2001.E. Bazelyan and Y. Raizer, Lightning Physics and Lightning Protection, IOP, 2000.K. Muraoka and M. Maeda, Laser Aided Diagnostics of Gases and Plasmas,IOP, 2000.Yu. M. Aliev, H. Schluter, and A. Shivarova, Guided-Wave-Produced Plasmas, Springer, 2000.W. N. G. Hitchon, Plasma Processes for Semiconductor Fabrication, Cambridge, 1999.Dusty Plasmas: Physics, Chemistry and Technological Impacts in Plasma Processing, Andre Bouchoule, ed., Zukov and O. Solonenko, eds., Lavoisier, 1999.Thermal Plasmas and New Materials Technology, vol 1&2, M. Zukov and O. Solonenko, eds., Cambridge, 1999.H. Zhang, Ion Sources, AIP, 1999.M. Sugawara, Plasma Etching: Fundamentals and Applications, Oxford, 1998. Microlithography: Science and Technology, J. R. Sheats and B. W. Smith, eds., Marcel Dekker, NY, 1998.I. C. E. Turcu and J. B. Dance, X-Rays from Laser Plasmas, Wiley, 1998. Generation and Application of High Power Microwaves, R. Cairns and A. Phelps, eds., IOP, 1997.Environmental Aspects in Plasma Science, Sugiyama, L., T. Stix, and W. Mannheimer, eds., AIP Press, 1997.Y. P. Raizer and J. E. Allen, Gas Discharge Physics, AIP, 1997. Plasma Science and the Environment, W. Manheimer, L. Sugiyama, and T. Stix, eds., AIP, 1996.R. Geller, Electron Cyclotron Resonance Ion Sources and ECR Plasmas, IOP, 1996.Dynamics of Transport in Plasmas and Charged Beams, G. Maino and M. Ottaviani, eds., World Scientific, 1996.12th International Symposium on Plasma Chemistry, J. V. Heberlein, D. W. Ernie, and J. T. Roberts, Int'l Union of Pure and Applied Chemistry, Univ. of Minnesota Pr., Minneapolis, Aug., 1995. Rimini, E., Ion Implantation: Basics to Device Fabrication, Kluwer Academic Publishing, Boston,1995.Stephen O. Dean and N. Poltoratskaya, "Applications of Fusion and Plasma Device Technologies," in Plasma Devices and Operations, Vol. 4, 1995. J. Reece Roth, Industrial Plasma Engineering, Vol. 1 - Principles, IOP, 1995.Michael Lieberman and Allan Lichtenberg, Principles of Plasma Discharges and Materials Processing, Wiley & Sons, 1994. Alfred Grill, Cold Plasma in Materials Fabrication, IEEE Press, 1994. J. C. Miller, Laser Ablation, Springer-Verlag,1994. Plasma Spraying: Theory and Applications, ed. R. Suryanarayanan, World Scientific, 1993.Non-thermal Plasma Techniques for Pollution Control, B. M. Penetrante and S. E. Schulteis, eds., NATO-ASI Series G, Vol. 34, Parts A and B, 1993.Plasma Technology: Fundamentals and Applications, eds. M. Capitelli and C. Gorse, Plenum Press, 1992.Dry Etching for VLSI, eds. A. J. van Roosmalen, J. A. G. Baggerman, S.J.H. Brader, Plenum Press, NY, 1991.Handbook of Plasma Processing Technology, eds. S. Rossnagel, J. Cuomo, and W. Westwood, Noyes Publications, 1990. Plasma Polymerization and Plasma Interactions with Polymeric Materials, ed. H. Yasuda, Wiley & Sons, 1990.Plasma Diagnostics, eds. O. Auciello and D. Flamm, Academic Press, 1989. Plasma Etching, eds. D. Manos and D. Flamm, Academic Press, 1989.A. Chambers, R. Fitch, Walmley, S. Coldfield, andB. Halliday, Basic Vacuum Technology, IOP Publ., 1989.Russ Morgan, Plasma Etching in Semiconductor Fabrication, Elsevier, 1985. Plasma Diagnostic Techniques, eds. R. Hudlestone and S. Leonard, Academic Press, 1978. Techniques and Applications of Plasma Chemistry, eds. J. Hollahan and A. Bell, Wiley & Sons, 1974.Computational Plasma PhysicsT. Tajima, Computational Plasma Physics: With Applications to Fusion and Astrophysics, Addison Wesley, 1989.C. K. Birdsall, and A. B. Langdon, Plasma Physics via Computer Simulation, McGraw-Hill, 1985, 1991.Hockney and Eastwood, Computer Simulation using Particles, Adam Hilger, 1988._______________________________________________________New and Special SourcesPlasma science materials from Russia and other FSU statesare a specialty of Cambridge International Science Publishing.William Beaty's Nikola Tesla and Tesla Coil pageand resources on ball lightningVladimir Rokov and Martin Uman, Lightning Physics and Effects, Cambridge Press, 2003.。
文化走廊图
• 试管婴儿不是在试管中培育 出婴儿来。而是将人的 精子、卵子取出体外,在 试管或培养皿中受精、卵裂, 然后再把受精卵移植入母体 子宫,让它正常地发育成成 熟的婴儿。事实上,应该把 它叫做体外受精—胚胎移植 技术。
器官移植
• • 病患者的福音 • • 器官移植中“僧多粥少” 的局面 • 买卖人体器官是受人谴责 的交易,它的存在带来了 许多的社会道德和法律上 的问题。 • • 我们如何面对这一难题 • 各种医学委员会明文规定 禁止器官买卖提倡捐献;
(1)重组DNA技术的建立
• 1972 • P. Berg USA, lays the foundation of recombinant-DNA technology. (*1980) • for his fundamental studies of the biochemistry of nucleic acids, with particular regard to recombinant-DNA
• Alfred G. Gilman,Martin Rodbell (1941-) (1925-1998) • The Nobel Prize in Physiology or Medicine 1994 • for their discovery of G-proteins and the role of these proteins in signal transduction in cells
• 试管婴儿技术解决 了很多夫妇的不孕 不育问题。在很多 国家,这项技术都 得到了蓬勃的发 展,并给很多家庭 创造了希望和欢 乐。
• • • •
反对: • 种族延续和遗传受到某些 人的利用和操纵。 • 侵害了婚姻关系价值,威 胁 • 家庭和社会稳定。 • • 用这种手段去满足某些人 的 • 生育的欲望是不道德的。
基因克隆原理
3. Nucleic Acid Synthesis
a. Both DNA and RNA chain are produced by copying of template DNA strand
b. Nucleic Acid strands grow in the 5’ 3’
c. RNA polymerases can initiate strand growth but DNA polymerases cannot RNA or DNA primer
d. Many DNA Molecules Are Circular
e. Local Unwinding of DNA Induces Supercoiling
f. RNA Molecules Exhibit Varied Conformations and Functions
2. General Rules for Synthesis of Biopolymers
Cloning vectors
• Plasmids • lambda (l) vectors • cosmids • BACs and YACs
质粒是细菌染色体外小型双链环状DNA复制子,对细菌的 某些代谢活动和抗药性表型具有一定的作用。
质粒载体是在天然质粒的基础上人工改造拼接而成。
作为载体的质粒一般具备以下特点: 1)分子相对较小; 2)具有松弛型复制子; 3)在复制子之外适当位置, 存在多克隆位点; 4)具有筛选标记。
f. Eukaryotic primary RNA transcripts are processed to form functional mRNAs
4. Three roles of RNA in Protein Synthesis
数据库检索系统SRS
实用生物信息技术课程第3次作业
数据库检索系统SRS
姓名________ 学号______________ 组号_____ 座位号_____
________年___月___日
1.简述SRS数据库检索系统主要特点,与UniProt数据库有哪些异同?
2.列表说明国际上主要SRS服务器。
欧洲生物信息学研究所(EBI)SRS数据库检索系统
包括哪几大类数据库?安装了哪几类分析工具?德国癌症研究所(DKFZ)SRS数据库检索系统安装的数据库有何特点?
3.说明查看SRS数据库检索系统中你感兴趣的某个数据库的内容、来源、版本,及其条目
数的方法。
4.说明利用SRS快速检索方法在UniProt/Swiss-Prot蛋白质数据库中检索斑头雁(bar-headed
goose)血红蛋白的方法和结果。
5.说明利用SRS标准检索界面在UniProt/Swiss-Prot蛋白质数据库中检索斑头雁(bar-headed
goose)血红蛋白的方法和结果。
6.说明利用SRS标准检索界面在UniProt/Swiss-Prot蛋白质数据库中检索人血红蛋白9个亚
基的方法和结果。
7.说明利用SRS标准检索界面在UniProt/Swiss-Prot蛋白质数据库中检索所有物种血红蛋白
beta亚基的方法和结果,其中哪些物种的beta亚基具有对应的核酸序列?哪些物种的beta 亚基的三维空间结构已经测定?
8.以欧洲生物信息学研究所(EBI)SRS数据库检索系统为例,以你熟悉的蛋白质和核酸序
列为例,说明快速检索(Quick Search)、标准检索(Standard Query)和扩展检索(Extended Query)的用法及检索结果。
1。
关于2009年诺贝尔生理学或医学奖的了解与认识
关于2009年诺贝尔生理学或医学奖的了解与认识李想1042043010四川大学生命科学院,四川610225摘要三位美国科学家(Elizabeth H.Blackburn,Carol W.Greider和Jack W.Szostak)因发现“端粒和端粒酶是如何保护染色体的”获得了2009年的诺贝尔生理学或医学奖.关键词端粒端粒酶1.关于获奖者1.1伊丽莎白·布莱克本(ElizabethH.Blackburn)布莱克本拥有美国和澳大利亚双重国籍,她1948年生于澳大利亚,在墨尔本大学获学士和硕士学位,1975年在英国剑桥大学获博士学位,随后前往美国耶鲁大学从事分子和细胞生物学研究,现执教于加利福尼亚大学旧金山分校。
1.2卡罗尔•格雷德(Carol W.Greider)卡萝尔·格雷德,美国人。
她于1961年出生在美国加利福尼亚州,曾先后就读于加利福尼亚大学圣巴巴拉分校和伯克利分校,并于1987年获得博士学位,其导师正是伊丽莎白·布莱克本。
格雷德曾在美国科尔德斯普林实验室从事博士后研究,从1997年起她开始担任约翰斯·霍普金斯大学医学院教授。
1.3杰克·绍斯塔克(Jack W.Szostak)杰克·绍斯塔克,美国人。
1952年生于伦敦,在加拿大长大。
他曾先后就读于加拿大麦基尔大学和美国康奈尔大学,并于1977年在康奈尔大学获得博士学位。
绍斯塔克自1979年开始在哈佛大学医学院任教,是马萨诸塞综合医院遗传学教授,并同时任职于美国霍华德·休斯医学研究所。
2.关于研究的成果卡罗林斯卡医学院方面称,这三人“解决了生物学上的一个重大问题”,即在细胞分裂时染色体如何进行完整复制,如何免于退化。
其中奥秘全部蕴藏在端粒和端粒酶上。
由染色体根冠制造的端粒酶(telomerase)是染色体的自然脱落物,能引发衰老和癌症。
端粒也被科学家称作“生命时钟”,在新细胞中,细胞每分裂一次,端粒就缩短一次,当端粒不能再缩短时,细胞就无法继续分裂而死亡。
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The problems and results we present are to some extent analogues for boundary traces of results of [SZ, ZZw] on eigenfunctions on boundaryless manifolds or on interior eigenfunctions of manifolds with boundary. In the next section, we state the main results at this time. Besides A. Hassell and C. Sogge, the author would like to thank M. Zworski for helpful discussions about non-convex domains and ghost orbits, and D. Tataru for checking the statements of his results in Section 5 (they are stated differently in [T]). 2. Statement of results Let Ω be a bounded subdomain of Rn with closure Ω. Definition: We say that Ω ⊂ Rn is a piecewise smooth manifold if the boundary Y = ∂ Ω is strongly Lipschitz, and can be written as a disjoint union Y := ∂ Ω = H1 ∪ · · · ∪ Hm ∪ Σ, where each Hi is an open, relatively compact subset of a smooth embedded hypersurface Si , and Σ is a closed set of (n − 1)-measure zero. The sets Hi are called boundary hypersurfaces of Ω. We call Σ the singular set, and write Y o = Y \ Σ for the regular part of the boundary.
1. Introduction This is a report on recent work (and work in progress) with A. Hassell and C. Sogge on boundary traces of eigenfunctions of the Laplacian on bounded domains and their relations to the dynamics of the billiard map. Boundary traces of eigenfunctions arise when one makes a reduction of the interior/exterior eigenvalue problem or wave equation of a domain to its boundary. In the classical approach to boundary problems of Neumann and Fredholm, the boundary reduction is made via layer potentials and associated boundary integral operators. Our results are based on an analysis of these operators as semiclassical Fourier integral operators. We work on a Lipschitz domain Ω ⊂ Rn which is assumed to be compact and piecewise smooth with corners. We give a precise definition below. The interior eigenvalue problem is: uj , uk = δjk −∆uj = λ2 j uj in Ω, (1.1) Buj = 0 on ∂ Ω, where (1.2) Buj = uj |∂ Ω (Dirichlet), resp. ∂ν uj + Ku|∂ Ω , (Neumann, Robin)
Theorem 1. Let Ω ⊂ Rn be a bounded piecewise smooth manifold (see Definition 2) with ergodic billiard map. Let {ub j } be the boundary traces f the eigenfunctions {uj } of ∆B on L2 (Ω) in the sense of the table above. Let Ah be a semiclassical operator of order zero on Y . Then there is a subset S of the positive integers, of density one, such that (2.2)
Date : February 1, 2008. #DMS-DMS-0071358.
1 b ub j (q ) = ∂ν uj |∂ Ω (Dirichlet), resp. uj (q ) = uj |∂ Ω
(Neumann, Robin).
2
STEVE ZELDITCH
where dA(y ) is the induced boundary area form, and where G0 (z, z ′ , λ) = (2π )−n λn−2 eiλ(z−z
′
)· ξ
1 |ξ |2 −1−i0 (1)
dξ
= Cλn−2 (λ|z − z ′ |)−(n−2)/2 Han/2−1 (λ|z − z ′ |). is the free outgoing Green’s function Rn . Here, Han/2−1 , is the Hankel function. Thus, the Dirichlet boundary integral operator has kernel Fλ (y, y ′ ) = (2π )−n λn−1 eiλ(y−y
Here, ∂ν denotes the unit interior normal derivative. By the boundary trace ub j of an interior eigenfunction uj , we mean the complementary operator to B , namely (1.3) These boundary traces are a reduction to the boundary of the eigenfuncions, and they become eigenfunctions of a reduction to the boundary of the resolvent, given by the boundary integral operator Fλ on ∂ Ω defined by ∂ ′ ′ ′ −2 ∂ Ω ∂νy′ G0 (y, y , λ)f (y )dA(y ), N eumann (1.4) Fλ f (y ) = ∂ 2 G (y, y ′ , λ)f (y ′ )dA(y ′ ), Dirichlet ∂ Ω ∂νy 0
b • Asymptotics of matrix elements A(hj )ub j , uj ; p b • Asymptotics of L norms: ||uj ||Lp . b • Asymj ||Lp /||uj ||L2
where νz is the unit inward pointing normal at z . It is elementary and classical (from Green’s formula) that b N eumann uj , = (1.5) Fλj ub j −ub j , Dirichlet.
B Dirichlet Neumann Robin
Ψ1 -Robin (∂ν u − Ku)|Y
Boundary Values Bu ub u|Y ∂ν u|Y ∂ν u|Y u|Y (∂ν u − κu)|Y u|Y u|Y
dµB γ (q )dσ γ (q )−1 dσ γ (q )−1 dσ γ (q )dσ γ (q )2 + k(q )2
BILLIARDS AND BOUNDARY TRACES OF EIGENFUNCTIONS
3
2.1. Statement of results on quantum ergodicity. Our main results concern the quantum ergodicity of boundary traces. To state the results, we need some notation. In the table, κ denotes a C ∞ function on Y while k is the principal symbol of the operator K ∈ Ψ1 (Y ), and dσ is the natural symplectic volume measure on B ∗ Y . We also define the function γ (q ) on B ∗ Y by (2.1) γ (q ) = 1 − |η |2 , q = (y, η ).