Polar complex numbers in n dimensions

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剑桥蝉联世界大学排名榜首

剑桥蝉联世界大学排名榜首

纯数3第十七章内容解析下面为参加A-level考试的同学介绍一下数学纯数的17章!第十七章内容为极坐标中的复数,Complex numbers in polar form。

Modulus and argument极坐标中我们用向量的模与幅角表示一个向量,以上两个概念,大家可以看出就是模与幅角。

Multiplication and division在极坐标中复数是如何乘除的呢,就看这一知识点吧。

下面的几个知识点,同学们在学习时要注意理解,因为有些东西不太好懂。

好在可能考试时涉及不多。

Spiral enlargementSquare roots of complex numbersThe exponential form更多a-l e v e l教材请登录a-l e v e l杂货铺h t t p://a-l e v e l m a r t.t a o b a o.c o m纯数3第十八章内容解析对纯数部分的内容解析只差最后一章了,洋高考的同学们如果有什么问题可以及时地提出来,这里有很多老师给大家解答。

十八章内容是对复杂积分的处理。

这些内容真的很需要技巧的,大家一定认真学习,然后多多练习,这样才能有收获。

这一章的特点就是需要多练习。

以下是对复杂积分的处理方法。

Direct substitution直接替代Definite integralsReverse substitution正着处理会做,反过来也要会做。

Integration by parts分部积分,可以根据product rule反向得到分部积分。

更多a-l e v e l教材请登录a-l e v e l杂货铺h t t p://a-l e v e l m a r t.t a o b a o.c o m剑桥蝉联世界大学排名榜首根据著名的QS高教信息中心公布的2011-2012年世界大学排行榜,英国剑桥大学(Univeristy of Cambridge)连续第二年高居榜首。

极坐标下二维非线性薛定谔方程的有限差分方法

极坐标下二维非线性薛定谔方程的有限差分方法

收稿日期:2020-09-13作者简介:杨程程(1996-),女,辽宁铁岭人,硕士研究生。

极坐标下二维非线性薛定谔方程的有限差分方法杨程程,张荣培(沈阳师范大学数学与系统科学学院,辽宁沈阳110034)摘要:对圆形区域上的二维非线性薛定谔方程进行了研究。

首先,用极坐标方式表示拉普拉斯算子,将计算区域分别沿r 和θ方向进行网格划分,运用中心差分的方法进行空间离散,离散格式用Kronecker 积表示,并写成非线性常微分方程组的形式。

然后,应用积分因子方法进行时间离散,在实现过程中采用Kroylov 子空间的方法求解指数矩阵与向量的乘积。

最后,在数值试验中给出爆破解的数值算例,证明了该方法可以有效地捕捉爆破现象。

关键词:二维非线性薛定谔方程;极坐标;中心差分;Kroylov 子空间中图分类号:TP273文献标识码:A文章编号:1673-1603(2021)01-0092-05DOI :10.13888/ki.jsie (ns ).2021.01.018第17卷第1期2021年1月Vol.17No.1Jan.2021沈阳工程学院学报(自然科学版)Journal of Shenyang Institute of Engineering (Natural Science )非线性薛定谔方程是量子力学中最重要的方程之一,在等离子物理、非线性光学、激光晶体中的自聚焦、晶体中热脉冲的传播以及在极低温度下的Bose -Einstein 凝聚体的动力学等领域内有着重要的应用[1-4]。

近年来,许多学者在求解非线性薛定谔方程时应用了许多数值方法,例如有限差分方法[5]、有限元法[6]、谱方法[7]和紧致积分因子法[8]等等。

但这些方法均在直角坐标系下求解,而在极坐标下求解的非线性薛定谔方程的文章比较少[9],本文考虑在圆形区域上求解极坐标下的二维非线性薛定谔方程。

考虑计算区域为Ω={}()x ,y :x 2+y 2<1的二维非线性薛定谔方程:iu t +Δu +||u 2u =0(1)式中,u ()x ,y 为复函数;i 2=-1为虚数单位;Δu =u xx +u yy 为拉普拉斯算子。

极谱值英文专业表达

极谱值英文专业表达

极谱值英文专业表达Polarographic Values: A Technical Overview.Polarography, often referred to as voltammetry, is an electrochemical analytical technique used to determine the concentration of various substances in a solution. Itrelies on the measurement of the current-voltagerelationship as a working electrode is scanned through a range of potentials in the presence of the analyte. The resulting polarogram, which is a plot of current against potential, provides information about the electrochemical behavior of the analyte and can be used to quantify its concentration.Polarographic values, or more specifically, the peak current and peak potential values obtained from polarograms, are crucial parameters in the analysis of substances using this technique. These values are directly related to the electrochemical properties of the analyte and can be usedto identify and quantify different compounds in a sample.Peak Current in Polarography.Peak current, denoted as Ip, is the maximum current value observed in a polarogram when the working electrode passes through the potential at which the analyte undergoes an electrochemical reaction. The magnitude of the peak current is dependent on several factors, including the concentration of the analyte, the nature of the electrochemical reaction, and the rate of electron transfer at the electrode surface.The peak current is proportional to the concentration of the analyte, assuming that other conditions such as temperature, electrode surface area, and solution composition remain constant. This relationship can be expressed as:Ip = nFAvC.where:Ip is the peak current.n is the number of electrons transferred in the electrochemical reaction.F is Faraday's constant (96,485 C/mol)。

复变函数——精选推荐

复变函数——精选推荐

复变函数The Complex PlaneComplex numbers are points in the planeIn the same way that we think of real numbers as being points on a line, it is natural to identify a complex number z=a+ib with the point (a,b) in the cartesian plane. Expressions such as ``the complex number z'', and ``the point z'' are now interchangeable.We consider the a real number x to be the complex number x+ 0i and in this way we can think of the real numbers as a subset of the complex numbers. The reals are just the x-axis in the complex plane.The modulus of the complex number z= a+ ib now can be interpreted as the distance from z to the origin in the complex plane.Since the hypotenuse of a right triangle is longer than the other sides, we havefor every complex number z.We can also think of the point z= a+ ib as the vector (a,b). From this point of view, the addition of complex numbers is equivalent to vector addition in two dimensions and we can visualize it as laying arrows tail to end. (Picture)We see in this way that the distance between two points z and w in the complex plane is |z-w|.Exercise: Prove this last statement algebraically.Writingwe havewhich is the euclidean distance between the points (a,b) and (c,d) in the plane.Exercise: Prove the ``Parallellogram law''The identity is called the parallelogram law because if we think of z and w as vectors (or points) in the plane, then it tells us that the sum of the squares of the lengths of the sides of a parallelogram is equal to the sum of the squares of the lengths of its diagonals.To prove the identity, just writeThe last equality follows from .Similarly, we haveAdding these gives the identity.The ``Triangle'' inequalityis easily seen to hold.The triangle inequality says that the shortest distance between two points in the plane is the length of the straight line between them.As in the last exercise,Now sincewe havefrom which the triangle inequality follows.Exercise: Prove the Triangle inequality for n complex numbersWe know the inequality when n=1 and when n=2 by the last exercise. We will show that the truth of the inequality for n=k implies it for n=k+1 when k is any integer. That will finish the proof. This is an example of proof by induction.By the triangle inequality (in the simplest case n=2),So the inductive hypothesis thatimplieswhich is the triangle inequality for the case n= k+1.Here are some more exercises.Exercise: Describe geometrically, the set of all complex numbers z which satisfy the following conditionSolution:Since |z-1| >0, we know the set we want to describe does not contain the point z=1. By the triangle inequality, we havefor all z. So we want to exclude all points from the plane where the equalityholds. That is, we want to exclude any z whose distance from 1 is equal to 1 plus its distance to the origin. This just means we have to exclude the negative real axis and the origin. (Draw a picture.)We can also see this algebraically. Writing z= x+iy we haveandSetting these equal giveswhich reduces toThis can only hold if and y=0.So the set we want is the complex plane with the point z=1 and the segment deleted.Exercise: Let and be distinct complex numbers. Describe the set of pointsSolution:First consider the case when . Then the set in question isWriting for some r and we see that the above set is simply the line segment from the origin to .Now we can writeand we see that our set is the line segment from the origin to translated by . Checking the endpoints, we see that this is the line segment joining to .Exercise: Prove that the medians of a triangle with vertices , andintersect at the pointSolution:Using the previous exercise we can write the medians of the triangle asandThese segments intersect if and only if there are real numbersin the interval [0,1] such thatIt's clear that is the unique solution to this systemso the point of intersection is以下数学式⼦和图形仅供参考,以便⽤电脑输⼊上述类似数学式⼦时借⽤:{}0)Re(:24>=z z S)/s i n (1z π∑∞=+-+-+-+-=00202010)()()()(n nn n n z z b z z b z z b z z a z f )()(),()(321321321321z z z z z z z z z z z z =++=++ ()()01222=+dz z f d p02222=??+??ywx w D re z r iv r u z f w i ∈=?θ+θ==θ),,(),()()(Im ),(),(Re ),(iy x f y x v iy x f y x u +=+=2222242222222)()(2||)(y x y x i xy z z i z z z i z z i z i z i +-+===?=2/2)(θi e r z f = ?<<>πθπ22,0r-=-=-C i i z z dz 81612)2(4ππ ∑∞=-----φ+--φ++-φ''+-φ'+-φ=mn n m m m m z z n z z z m z z z z z z z z z z z f )(!)()!1/()()(!2/)()(!1/)()()()(00)(00)1(20010000),(),()(y x iv y x u z f +=∑∞=+----+-+-++-+=0010120201)()()()()(n nm n m m m m z z a z z b z z b z z b b z φ82)1(log Res22iz z iz +=+=π t z zt z zt i z i z πππcos 2sinh )exp(Res sinh )exp(Res -=+-== . )()!2()()()!1()(!)()()(!)()(200)2(00)1(0000)(??+-++-++-=-=++∞=∑z z m z f z z m z f m z f z z z z n z f z f m m mm mn nn+-=),()0,0(),(y x tdt sds y x v=≠-=.0 hen w0when ,/)1()(z z z e z g z 11||2(1,2,)2n n n M b M n πρρπρ-+≤==--+=RR R Rdx x f dx x f dx x f 00)()()(∞→∞∞--∞→+=22110lim lim R R R R xdx xdx xdx22110222lim 2lim R R R R x x ∞→-∞→+=2lim 2lim 222121R R R R ∞→∞→+-=;)(),(grad ),(grad z f v u h y x H '=。

平面图英文

平面图英文

平面图英文A plane is a two-dimensional surface that is infinitely large in both dimensions. In the context of mathematics, a plane is often used to represent a flat, geometric shape, such as a circle or a square. Planes are also used to graph equations and represent data. In this article, we will explore various types of planes and provide examples of how they are used in mathematics.Cartesian Plane:The Cartesian plane, also known as the coordinate plane or the rectangular coordinate system, is used to graph equations in two dimensions. It is named after the French mathematician René Descartes, who invented it in the 17th century. The Cartesian plane is made up of two perpendicular number lines, one vertical and one horizontal, that intersect at the origin (0,0). The vertical number line is called the y-axis, and the horizontal number line is called the x-axis. Any point on the Cartesian plane can be identified by its x-coordinate value and its y-coordinate value. These values are written in parentheses and separated by a comma, with the x-coordinate listed first. For example, the point (3,4) is located three units to the right of the y-axis and four units above the x-axis.Polar Plane:The polar plane, also known as the polar coordinate system, is used to graph equations in two dimensions based on their distance from a fixed point and their angle with respect to a fixed line. The fixed point is called the pole, and the fixed line is called the polar axis. To graph a point in the polar plane, its distance from the pole and its angle with respect to the polar axis are used.The distance is represented by the variable r, and the angle is represented by the Greek letter theta (θ). The angle is measured in radians, which are a unit of measurement for angles that is equivalent to the fraction of the circumference of a circle that subtends the angle. For example, the point (4,π/3) in the polar plane is located 4 units from the pole and at an angle of π/3 radians (which is equivalent to 60 degrees) with respect to the polar axis.3D Space:Three-dimensional space, also known as 3D space or simply space, is a mathematical concept that extends the idea of a plane into three dimensions. Space is used to graph equations and represent objects that have a length, a width, and a height. It is often represented by a set of three number lines that are perpendicular to each other, known as the x-axis, y-axis, and z-axis. Any point in space can be identified by its x-coordinate, y-coordinate, and z-coordinate values. These values are written in parentheses and separated by commas, with the x-coordinate listed first. For example, the point (1,2,3) is located 1 unit to the right of the yz-plane, 2 units in front of the xz-plane, and 3 units above the xy-plane.Other Types of Planes:There are many other types of planes that are used in mathematics, such as the hyperplane, which is a plane of n dimensions that divides an n-dimensional space into two parts, and the projective plane, which is a plane that is formed by adding a single point at infinity to each line in the Cartesian plane. These planes are used in advanced mathematics and areas such as geometry, topology, and algebraic geometry.In conclusion, planes are an essential tool for three-dimensional representation and processing of information. They are an essential concept in mathematics, physics, computer graphics, and many other fields. By studying different types of planes and their properties, we can better understand the world around us and solve problems more efficiently.。

precalculus知识点总结

precalculus知识点总结

precalculus知识点总结Precalculus is an essential branch of mathematics that serves as a bridge between algebra, geometry, and calculus. This subject is crucial for students preparing to undertake advanced courses in mathematics, physics, engineering, and other technical fields. In this precalculus knowledge summary, we will cover important topics such as functions, trigonometry, and analytic geometry.FunctionsOne of the fundamental concepts in precalculus is that of functions. A function is a relationship between two sets of numbers, where each input is associated with exactly one output. In other words, it assigns a unique value to each input. Functions can be represented in various forms, such as algebraic expressions, tables, graphs, and verbal descriptions.The most common types of functions encountered in precalculus include linear, quadratic, polynomial, rational, exponential, logarithmic, and trigonometric functions. Each type of function has its own unique characteristics and properties. For example, linear functions have a constant rate of change, while quadratic functions have a parabolic shape.Functions can be manipulated by performing operations such as addition, subtraction, multiplication, division, composition, and inversion. These operations can be used to create new functions from existing ones, or to analyze the behavior of functions under different conditions.TrigonometryTrigonometry is the study of the relationships between the angles and sides of triangles. It plays a crucial role in precalculus and is essential for understanding periodic phenomena such as oscillations, waves, and circular motion.The primary trigonometric functions are sine, cosine, and tangent, which are defined in terms of the sides of a right-angled triangle. These functions have various properties, such as periodicity, amplitude, and phase shift, which are important for modeling and analyzing periodic phenomena.Trigonometric functions can also be extended to the entire real line using their geometric definitions. They exhibit various symmetries and periodic behaviors, which can be visualized using the unit circle or trigonometric graphs. Additionally, trigonometric identities and equations are essential tools for simplifying expressions, solving equations, and proving theorems.Analytic GeometryAnalytic geometry is a branch of mathematics that combines algebra and geometry. It deals with the use of algebraic techniques to study geometric shapes and their properties. Inprecalculus, this subject is primarily concerned with the study of conic sections, such as circles, ellipses, parabolas, and hyperbolas.The equations of conic sections can be derived using geometric constructions, or by using algebraic methods such as completing the square, factoring, and manipulating equations. These equations can then be used to describe the geometric properties of conic sections, such as their shape, size, orientation, and position.Furthermore, analytic geometry also involves the study of vectors and matrices, which are important tools for representing and manipulating geometric objects in higher dimensions. Vectors can be used to represent points, lines, and planes in space, while matrices can be used to perform transformations such as rotations, reflections, and scaling.Other TopicsIn addition to the core topics mentioned above, precalculus also covers other important concepts such as complex numbers, polar coordinates, sequences and series, and mathematical induction. Complex numbers are used to extend the real number system to include solutions to equations that have no real roots. They have applications in various fields such as electrical engineering, quantum mechanics, and signal processing.Polar coordinates provide an alternative way of describing points in the plane using radial distance and angular direction. They are particularly useful for representing periodic and circular motion, as well as for simplifying certain types of calculations in calculus.Sequences and series are ordered lists of numbers that have a specific pattern or rule. They can be finite or infinite, and their sums can be used to represent various types of mathematical and physical phenomena. For example, arithmetic sequences are used to model linear growth or decline, while geometric series are used to model exponential growth or decay.Finally, mathematical induction is a powerful method for proving statements about positive integers. It is based on the principle that if a certain property holds for a base case, and if it can be shown that it also holds for the next case, then it holds for all subsequent cases as well. This method is widely used in various areas of mathematics, such as number theory, combinatorics, and discrete mathematics.ConclusionIn conclusion, precalculus is a diverse and rich subject that covers a wide range of mathematical concepts and techniques. It provides students with the necessary foundation to tackle more advanced topics in calculus and beyond. By mastering the core topics of precalculus, students will be well-equipped to understand and apply advanced mathematical methods in various technical fields. Whether it be functions, trigonometry, analytic geometry, or any other topic, a solid understanding of precalculus is essential for success in higher mathematics.。

代数英语

代数英语

(0,2) 插值||(0,2) interpolation0#||zero-sharp; 读作零井或零开。

0+||zero-dagger; 读作零正。

1-因子||1-factor3-流形||3-manifold; 又称“三维流形”。

AIC准则||AIC criterion, Akaike information criterionAp 权||Ap-weightA稳定性||A-stability, absolute stabilityA最优设计||A-optimal designBCH 码||BCH code, Bose-Chaudhuri-Hocquenghem codeBIC准则||BIC criterion, Bayesian modification of the AICBMOA函数||analytic function of bounded mean oscillation; 全称“有界平均振动解析函数”。

BMO鞅||BMO martingaleBSD猜想||Birch and Swinnerton-Dyer conjecture; 全称“伯奇与斯温纳顿-戴尔猜想”。

B样条||B-splineC*代数||C*-algebra; 读作“C星代数”。

C0 类函数||function of class C0; 又称“连续函数类”。

CA T准则||CAT criterion, criterion for autoregressiveCM域||CM fieldCN 群||CN-groupCW 复形的同调||homology of CW complexCW复形||CW complexCW复形的同伦群||homotopy group of CW complexesCW剖分||CW decompositionCn 类函数||function of class Cn; 又称“n次连续可微函数类”。

Cp统计量||Cp-statisticC。

griddata二维插值

griddata二维插值

griddata⼆维插值 对某些设备或测量仪器来说,采集的数据点的位置不是规则排列的⽹格结构(可参考),对于这种数据⽤散点图(每个采样点具有不同的值或权重)不能很好的展⽰其内部结构,因此需要对其进⾏插值,⽣成⼀个规则的栅格图像。

可采⽤griddata函数对已知的数据点进⾏插值,数据点(X, Y)不要求规则排列。

下图分别使⽤Nearest、Linear、Cubic三种插值⽅法对数据点进⾏插值。

插值算法都要⾸先⾯对⼀个共同的问题—— 邻近点的选择(利⽤靠近插值点附近的多个邻近点,构造插值函数计算插值点的函数值)。

应尽可能使所选择的邻近点均匀地分布在待估点周围。

因为使⽤适量的已知点对于插值的精度很重要,当已知点过多时,会使插值准确率下降,因为过多的信息量会掩盖有⽤的信息;被选择的邻近点构成的点分布不均匀,若某个⽅位上的数据点过多,或点集的某些数据点位置较集中,或者数据点过远,都会带来较⼤的插值误差。

griddata函数内部会先对已知的数据点进⾏Delaunay三⾓剖分,当完成三⾓剖分完成后,根据每个三⾓⽚顶点的数值,就可以对三⾓形区域内任意点进⾏线性插值(参考LinearNDInterpolator函数)。

对三⾓⽚内的插值点可采⽤质⼼插值,即是对插值点只考虑与该点最邻近周围点的影响,确定出插值点与最邻近的周围点之相互位置关系,求出与周围点的影响权重因⼦,以此建⽴线性插值公式(参考)。

"""Simple N-D interpolation.. versionadded:: 0.9"""## Copyright (C) Pauli Virtanen, 2010.## Distributed under the same BSD license as Scipy.### Note: this file should be run through the Mako template engine before# feeding it to Cython.## Run ``generate_qhull.py`` to regenerate the ``qhull.c`` file#cimport cythonfrom libc.float cimport DBL_EPSILONfrom libc.math cimport fabs, sqrtimport numpy as npimport scipy.spatial.qhull as qhullcimport scipy.spatial.qhull as qhullimport warnings#------------------------------------------------------------------------------# Numpy etc.#------------------------------------------------------------------------------cdef extern from"numpy/ndarrayobject.h":cdef enum:NPY_MAXDIMSctypedef fused double_or_complex:doubledouble complex#------------------------------------------------------------------------------# Interpolator base class#------------------------------------------------------------------------------class NDInterpolatorBase(object):"""Common routines for interpolators... versionadded:: 0.9"""def__init__(self, points, values, fill_value=np.nan, ndim=None,rescale=False, need_contiguous=True, need_values=True):"""Check shape of points and values arrays, and reshape values to(npoints, nvalues). Ensure the `points` and values arrays areC-contiguous, and of correct type."""if isinstance(points, qhull.Delaunay):# Precomputed triangulation was passed inif rescale:raise ValueError("Rescaling is not supported when passing ""a Delaunay triangulation as ``points``.")self.tri = pointspoints = points.pointselse:self.tri = Nonepoints = _ndim_coords_from_arrays(points)values = np.asarray(values)_check_init_shape(points, values, ndim=ndim)if need_contiguous:points = np.ascontiguousarray(points, dtype=np.double)if need_values:self.values_shape = values.shape[1:]if values.ndim == 1:self.values = values[:,None]elif values.ndim == 2:self.values = valueselse:self.values = values.reshape(values.shape[0],np.prod(values.shape[1:]))# Complex or real?self.is_complex = np.issubdtype(self.values.dtype, plexfloating) if self.is_complex:if need_contiguous:self.values = np.ascontiguousarray(self.values,dtype=plex128)self.fill_value = complex(fill_value)else:if need_contiguous:self.values = np.ascontiguousarray(self.values, dtype=np.double) self.fill_value = float(fill_value)if not rescale:self.scale = Noneself.points = pointselse:# scale to unit cube centered at 0self.offset = np.mean(points, axis=0)self.points = points - self.offsetself.scale = self.points.ptp(axis=0)self.scale[~(self.scale > 0)] = 1.0 # avoid division by 0self.points /= self.scaledef _check_call_shape(self, xi):xi = np.asanyarray(xi)if xi.shape[-1] != self.points.shape[1]:raise ValueError("number of dimensions in xi does not match x")return xidef _scale_x(self, xi):if self.scale is None:return xielse:return (xi - self.offset) / self.scaledef__call__(self, *args):"""interpolator(xi)Evaluate interpolator at given points.Parameters----------x1, x2, ... xn: array-like of floatPoints where to interpolate data at.x1, x2, ... xn can be array-like of float with broadcastable shape.or x1 can be array-like of float with shape ``(..., ndim)``"""xi = _ndim_coords_from_arrays(args, ndim=self.points.shape[1])xi = self._check_call_shape(xi)shape = xi.shapexi = xi.reshape(-1, shape[-1])xi = np.ascontiguousarray(xi, dtype=np.double)xi = self._scale_x(xi)if self.is_complex:r = self._evaluate_complex(xi)else:r = self._evaluate_double(xi)return np.asarray(r).reshape(shape[:-1] + self.values_shape)cpdef _ndim_coords_from_arrays(points, ndim=None):"""Convert a tuple of coordinate arrays to a (..., ndim)-shaped array."""cdef ssize_t j, nif isinstance(points, tuple) and len(points) == 1:# handle argument tuplepoints = points[0]if isinstance(points, tuple):p = np.broadcast_arrays(*points)n = len(p)for j in range(1, n):if p[j].shape != p[0].shape:raise ValueError("coordinate arrays do not have the same shape") points = np.empty(p[0].shape + (len(points),), dtype=float) for j, item in enumerate(p):points[...,j] = itemelse:points = np.asanyarray(points)if points.ndim == 1:if ndim is None:points = points.reshape(-1, 1)else:points = points.reshape(-1, ndim)return pointscdef _check_init_shape(points, values, ndim=None):"""Check shape of points and values arrays"""if values.shape[0] != points.shape[0]:raise ValueError("different number of values and points")if points.ndim != 2:raise ValueError("invalid shape for input data points")if points.shape[1] < 2:raise ValueError("input data must be at least 2-D")if ndim is not None and points.shape[1] != ndim:raise ValueError("this mode of interpolation available only for ""%d-D data" % ndim)#------------------------------------------------------------------------------# Linear interpolation in N-D#------------------------------------------------------------------------------class LinearNDInterpolator(NDInterpolatorBase):"""LinearNDInterpolator(points, values, fill_value=np.nan, rescale=False)Piecewise linear interpolant in N dimensions... versionadded:: 0.9Methods-------__call__Parameters----------points : ndarray of floats, shape (npoints, ndims); or DelaunayData point coordinates, or a precomputed Delaunay triangulation.values : ndarray of float or complex, shape (npoints, ...)Data values.fill_value : float, optionalValue used to fill in for requested points outside of theconvex hull of the input points. If not provided, thenthe default is ``nan``.rescale : bool, optionalRescale points to unit cube before performing interpolation.This is useful if some of the input dimensions haveincommensurable units and differ by many orders of magnitude.Notes-----The interpolant is constructed by triangulating the input datawith Qhull [1]_, and on each triangle performing linearbarycentric interpolation.Examples--------We can interpolate values on a 2D plane:>>> from scipy.interpolate import LinearNDInterpolator>>> import matplotlib.pyplot as plt>>> np.random.seed(0)>>> x = np.random.random(10) - 0.5>>> y = np.random.random(10) - 0.5>>> z = np.hypot(x, y)>>> X = np.linspace(min(x), max(x))>>> Y = np.linspace(min(y), max(y))>>> X, Y = np.meshgrid(X, Y) # 2D grid for interpolation>>> interp = LinearNDInterpolator(list(zip(x, y)), z)>>> Z = interp(X, Y)>>> plt.pcolormesh(X, Y, Z, shading='auto')>>> plt.plot(x, y, "ok", label="input point")>>> plt.legend()>>> plt.colorbar()>>> plt.axis("equal")>>> plt.show()See also--------griddata :Interpolate unstructured D-D data.NearestNDInterpolator :Nearest-neighbor interpolation in N dimensions.CloughTocher2DInterpolator :Piecewise cubic, C1 smooth, curvature-minimizing interpolant in 2D. References----------.. [1] /"""def__init__(self, points, values, fill_value=np.nan, rescale=False):NDInterpolatorBase.__init__(self, points, values, fill_value=fill_value, rescale=rescale)if self.tri is None:self.tri = qhull.Delaunay(self.points)def _evaluate_double(self, xi):return self._do_evaluate(xi, 1.0)def _evaluate_complex(self, xi):return self._do_evaluate(xi, 1.0j)@cython.boundscheck(False)@cython.wraparound(False)def _do_evaluate(self, const double[:,::1] xi, double_or_complex dummy): cdef const double_or_complex[:,::1] values = self.valuescdef double_or_complex[:,::1] outcdef const double[:,::1] points = self.pointscdef const int[:,::1] simplices = self.tri.simplicescdef double c[NPY_MAXDIMS]cdef double_or_complex fill_valuecdef int i, j, k, m, ndim, isimplex, inside, start, nvaluescdef qhull.DelaunayInfo_t infocdef double eps, eps_broadndim = xi.shape[1]start = 0fill_value = self.fill_valueqhull._get_delaunay_info(&info, self.tri, 1, 0, 0)out = np.empty((xi.shape[0], self.values.shape[1]),dtype=self.values.dtype)nvalues = out.shape[1]eps = 100 * DBL_EPSILONeps_broad = sqrt(DBL_EPSILON)with nogil:for i in range(xi.shape[0]):# 1) Find the simplexisimplex = qhull._find_simplex(&info, c,&xi[0,0] + i*ndim,&start, eps, eps_broad)# 2) Linear barycentric interpolationif isimplex == -1:# don't extrapolatefor k in range(nvalues):out[i,k] = fill_valuecontinuefor k in range(nvalues):out[i,k] = 0for j in range(ndim+1):for k in range(nvalues):m = simplices[isimplex,j]out[i,k] = out[i,k] + c[j] * values[m,k]return out#------------------------------------------------------------------------------# Gradient estimation in 2D#------------------------------------------------------------------------------class GradientEstimationWarning(Warning):pass@cython.cdivision(True)cdef int _estimate_gradients_2d_global(qhull.DelaunayInfo_t *d, double *data, int maxiter, double tol,double *y) nogil:"""Estimate gradients of a function at the vertices of a 2d triangulation.Parameters----------info : inputTriangulation in 2Ddata : inputFunction values at the verticesmaxiter : inputMaximum number of Gauss-Seidel iterationstol : inputAbsolute / relative stop tolerancey : output, shape (npoints, 2)Derivatives [F_x, F_y] at the verticesReturns-------num_iterationsNumber of iterations if converged, 0 if maxiter reachedwithout convergenceNotes-----This routine uses a re-implementation of the global approximatecurvature minimization algorithm described in [Nielson83] and [Renka84]. References----------.. [Nielson83] G. Nielson,''A method for interpolating scattered data based upon a minimum norm network''.Math. Comp., 40, 253 (1983)... [Renka84] R. J. Renka and A. K. Cline.''A Triangle-based C1 interpolation method.'',Rocky Mountain J. Math., 14, 223 (1984)."""cdef double Q[2*2]cdef double s[2]cdef double r[2]cdef int ipoint, iiter, k, ipoint2, jpoint2cdef double f1, f2, df2, ex, ey, L, L3, det, err, change# initializefor ipoint in range(2*d.npoints):y[ipoint] = 0## Main point:## Z = sum_T sum_{E in T} int_E |W''|^2 = min!## where W'' is the second derivative of the Clough-Tocher# interpolant to the direction of the edge E in triangle T.## The minimization is done iteratively: for each vertex V,# the sum## Z_V = sum_{E connected to V} int_E |W''|^2## is minimized separately, using existing values at other V.## Since the interpolant can be written as## W(x) = f(x) + w(x)^T y## where y = [ F_x(V); F_y(V) ], it is clear that the solution to# the local problem is is given as a solution of the 2x2 matrix# equation.## Here, we use the Clough-Tocher interpolant, which restricted to# a single edge is## w(x) = (1 - x)**3 * f1# + x*(1 - x)**2 * (df1 + 3*f1)# + x**2*(1 - x) * (df2 + 3*f2)# + x**3 * f2## where f1, f2 are values at the vertices, and df1 and df2 are# derivatives along the edge (away from the vertices).## As a consequence, one finds## L^3 int_{E} |W''|^2 = y^T A y + 2 B y + C## with## A = [4, -2; -2, 4]# B = [6*(f1 - f2), 6*(f2 - f1)]# y = [df1, df2]# L = length of edge E## and C is not needed for minimization. Since df1 = dF1.E, df2 = -dF2.E, # with dF1 = [F_x(V_1), F_y(V_1)], and the edge vector E = V2 - V1,# we have## Z_V = dF1^T Q dF1 + 2 s.dF1 + const.## which is minimized by## dF1 = -Q^{-1} s## where## Q = sum_E [A_11 E E^T]/L_E^3 = 4 sum_E [E E^T]/L_E^3# s = sum_E [ B_1 + A_21 df2] E /L_E^3# = sum_E [ 6*(f1 - f2) + 2*(E.dF2)] E / L_E^3## Gauss-Seidelfor iiter in range(maxiter):err = 0for ipoint in range(d.npoints):for k in range(2*2):Q[k] = 0for k in range(2):s[k] = 0# walk over neighbours of given pointfor jpoint2 in range(d.vertex_neighbors_indptr[ipoint],d.vertex_neighbors_indptr[ipoint+1]):ipoint2 = d.vertex_neighbors_indices[jpoint2]# edgeex = d.points[2*ipoint2 + 0] - d.points[2*ipoint + 0]ey = d.points[2*ipoint2 + 1] - d.points[2*ipoint + 1]L = sqrt(ex**2 + ey**2)L3 = L*L*L# data at verticesf1 = data[ipoint]f2 = data[ipoint2]# scaled gradient projections on the edgedf2 = -ex*y[2*ipoint2 + 0] - ey*y[2*ipoint2 + 1]# edge sumQ[0] += 4*ex*ex / L3Q[1] += 4*ex*ey / L3Q[3] += 4*ey*ey / L3s[0] += (6*(f1 - f2) - 2*df2) * ex / L3s[1] += (6*(f1 - f2) - 2*df2) * ey / L3Q[2] = Q[1]# solvedet = Q[0]*Q[3] - Q[1]*Q[2]r[0] = ( Q[3]*s[0] - Q[1]*s[1])/detr[1] = (-Q[2]*s[0] + Q[0]*s[1])/detchange = max(fabs(y[2*ipoint + 0] + r[0]),fabs(y[2*ipoint + 1] + r[1]))y[2*ipoint + 0] = -r[0]y[2*ipoint + 1] = -r[1]# relative/absolute errorchange /= max(1.0, max(fabs(r[0]), fabs(r[1])))err = max(err, change)if err < tol:return iiter + 1# Didn't converge before maxiterreturn 0@cython.boundscheck(False)@cython.wraparound(False)cpdef estimate_gradients_2d_global(tri, y, int maxiter=400, double tol=1e-6): cdef const double[:,::1] datacdef double[:,:,::1] gradcdef qhull.DelaunayInfo_t infocdef int k, ret, nvaluesy = np.asanyarray(y)if y.shape[0] != tri.npoints:raise ValueError("'y' has a wrong number of items")if np.issubdtype(y.dtype, plexfloating):rg = estimate_gradients_2d_global(tri, y.real, maxiter=maxiter, tol=tol) ig = estimate_gradients_2d_global(tri, y.imag, maxiter=maxiter, tol=tol) r = np.zeros(rg.shape, dtype=complex)r.real = rgr.imag = igreturn ry_shape = y.shapeif y.ndim == 1:y = y[:,None]y = y.reshape(tri.npoints, -1).Ty = np.ascontiguousarray(y, dtype=np.double)yi = np.empty((y.shape[0], y.shape[1], 2))data = ygrad = yiqhull._get_delaunay_info(&info, tri, 0, 0, 1)nvalues = data.shape[0]for k in range(nvalues):with nogil:ret = _estimate_gradients_2d_global(&info,&data[k,0],maxiter,tol,&grad[k,0,0])if ret == 0:warnings.warn("Gradient estimation did not converge, ""the results may be inaccurate",GradientEstimationWarning)return yi.transpose(1, 0, 2).reshape(y_shape + (2,))#------------------------------------------------------------------------------# Cubic interpolation in 2D#------------------------------------------------------------------------------@cython.cdivision(True)cdef double_or_complex _clough_tocher_2d_single(qhull.DelaunayInfo_t *d, int isimplex,double *b,double_or_complex *f,double_or_complex *df) nogil:"""Evaluate Clough-Tocher interpolant on a 2D triangle.Parameters----------d :Delaunay infoisimplex : intTriangle to evaluate onb : shape (3,)Barycentric coordinates of the point on the trianglef : shape (3,)Function values at verticesdf : shape (3, 2)Gradient values at verticesReturns-------w :Value of the interpolant at the given pointReferences----------.. [CT] See, for example,P. Alfeld,''A trivariate Clough-Tocher scheme for tetrahedral data''.Computer Aided Geometric Design, 1, 169 (1984);G. Farin,''Triangular Bernstein-Bezier patches''.Computer Aided Geometric Design, 3, 83 (1986)."""cdef double_or_complex \c3000, c0300, c0030, c0003, \c2100, c2010, c2001, c0210, c0201, c0021, \c1200, c1020, c1002, c0120, c0102, c0012, \c1101, c1011, c0111cdef double_or_complex \f1, f2, f3, df12, df13, df21, df23, df31, df32cdef double g[3]cdef double \e12x, e12y, e23x, e23y, e31x, e31y, \e14x, e14y, e24x, e24y, e34x, e34ycdef double_or_complex wcdef double minvalcdef double b1, b2, b3, b4cdef int k, itricdef double c[3]cdef double y[2]# XXX: optimize + refactor this!e12x = (+ d.points[0 + 2*d.simplices[3*isimplex + 1]]- d.points[0 + 2*d.simplices[3*isimplex + 0]])e12y = (+ d.points[1 + 2*d.simplices[3*isimplex + 1]]- d.points[1 + 2*d.simplices[3*isimplex + 0]])e23x = (+ d.points[0 + 2*d.simplices[3*isimplex + 2]]- d.points[0 + 2*d.simplices[3*isimplex + 1]])e23y = (+ d.points[1 + 2*d.simplices[3*isimplex + 2]]- d.points[1 + 2*d.simplices[3*isimplex + 1]])e31x = (+ d.points[0 + 2*d.simplices[3*isimplex + 0]]- d.points[0 + 2*d.simplices[3*isimplex + 2]])e31y = (+ d.points[1 + 2*d.simplices[3*isimplex + 0]]- d.points[1 + 2*d.simplices[3*isimplex + 2]])e14x = (e12x - e31x)/3e14y = (e12y - e31y)/3e24x = (-e12x + e23x)/3e24y = (-e12y + e23y)/3e34x = (e31x - e23x)/3e34y = (e31y - e23y)/3f1 = f[0]f2 = f[1]f3 = f[2]df12 = +(df[2*0+0]*e12x + df[2*0+1]*e12y)df21 = -(df[2*1+0]*e12x + df[2*1+1]*e12y)df23 = +(df[2*1+0]*e23x + df[2*1+1]*e23y)df32 = -(df[2*2+0]*e23x + df[2*2+1]*e23y)df31 = +(df[2*2+0]*e31x + df[2*2+1]*e31y)df13 = -(df[2*0+0]*e31x + df[2*0+1]*e31y)c3000 = f1c2100 = (df12 + 3*c3000)/3c2010 = (df13 + 3*c3000)/3c0300 = f2c1200 = (df21 + 3*c0300)/3c0210 = (df23 + 3*c0300)/3c0030 = f3c1020 = (df31 + 3*c0030)/3c0120 = (df32 + 3*c0030)/3c2001 = (c2100 + c2010 + c3000)/3c0201 = (c1200 + c0300 + c0210)/3c0021 = (c1020 + c0120 + c0030)/3## Now, we need to impose the condition that the gradient of the spline # to some direction `w` is a linear function along the edge.## As long as two neighbouring triangles agree on the choice of the# direction `w`, this ensures global C1 differentiability.# Otherwise, the choice of the direction is arbitrary (except that# it should not point along the edge, of course).## In [CT]_, it is suggested to pick `w` as the normal of the edge.# This choice is given by the formulas## w_12 = E_24 + g[0] * E_23# w_23 = E_34 + g[1] * E_31# w_31 = E_14 + g[2] * E_12## g[0] = -(e24x*e23x + e24y*e23y) / (e23x**2 + e23y**2)# g[1] = -(e34x*e31x + e34y*e31y) / (e31x**2 + e31y**2)# g[2] = -(e14x*e12x + e14y*e12y) / (e12x**2 + e12y**2)## However, this choice gives an interpolant that is *not*# invariant under affine transforms. This has some bad# consequences: for a very narrow triangle, the spline can# develops huge oscillations. For instance, with the input data## [(0, 0), (0, 1), (eps, eps)], eps = 0.01# F = [0, 0, 1]# dF = [(0,0), (0,0), (0,0)]## one observes that as eps -> 0, the absolute maximum value of the # interpolant approaches infinity.## So below, we aim to pick affine invariant `g[k]`.# We choose## w = V_4' - V_4## where V_4 is the centroid of the current triangle, and V_4' the# centroid of the neighbour. Since this quantity transforms similarly # as the gradient under affine transforms, the resulting interpolant# is affine-invariant. Moreover, two neighbouring triangles clearly# always agree on the choice of `w` (sign is unimportant), and so# this choice also makes the interpolant C1.## The drawback here is a performance penalty, since we need to# peek into neighbouring triangles.#for k in range(3):itri = d.neighbors[3*isimplex + k]if itri == -1:# No neighbour.# Compute derivative to the centroid direction (e_12 + e_13)/2. g[k] = -1./2continue# Centroid of the neighbour, in our local barycentric coordinates y[0] = (+ d.points[0 + 2*d.simplices[3*itri + 0]]+ d.points[0 + 2*d.simplices[3*itri + 1]]+ d.points[0 + 2*d.simplices[3*itri + 2]]) / 3y[1] = (+ d.points[1 + 2*d.simplices[3*itri + 0]]+ d.points[1 + 2*d.simplices[3*itri + 1]]+ d.points[1 + 2*d.simplices[3*itri + 2]]) / 3qhull._barycentric_coordinates(2, d.transform + isimplex*2*3, y, c) # Rewrite V_4'-V_4 = const*[(V_4-V_2) + g_i*(V_3 - V_2)]# Now, observe that the results can be written *in terms of# barycentric coordinates*. Barycentric coordinates stay# invariant under affine transformations, so we can directly# conclude that the choice below is affine-invariant.if k == 0:g[k] = (2*c[2] + c[1] - 1) / (2 - 3*c[2] - 3*c[1])elif k == 1:g[k] = (2*c[0] + c[2] - 1) / (2 - 3*c[0] - 3*c[2])elif k == 2:g[k] = (2*c[1] + c[0] - 1) / (2 - 3*c[1] - 3*c[0])c0111 = (g[0]*(-c0300 + 3*c0210 - 3*c0120 + c0030)+ (-c0300 + 2*c0210 - c0120 + c0021 + c0201))/2c1011 = (g[1]*(-c0030 + 3*c1020 - 3*c2010 + c3000)+ (-c0030 + 2*c1020 - c2010 + c2001 + c0021))/2c1101 = (g[2]*(-c3000 + 3*c2100 - 3*c1200 + c0300)+ (-c3000 + 2*c2100 - c1200 + c2001 + c0201))/2c1002 = (c1101 + c1011 + c2001)/3c0102 = (c1101 + c0111 + c0201)/3c0012 = (c1011 + c0111 + c0021)/3c0003 = (c1002 + c0102 + c0012)/3# extended barycentric coordinatesminval = b[0]for k in range(3):if b[k] < minval:minval = b[k]b1 = b[0] - minvalb2 = b[1] - minvalb3 = b[2] - minvalb4 = 3*minval# evaluate the polynomial -- the stupid and ugly way to do it,# one of the 4 coordinates is in fact zerow = (b1**3*c3000 + 3*b1**2*b2*c2100 + 3*b1**2*b3*c2010 +3*b1**2*b4*c2001 + 3*b1*b2**2*c1200 +6*b1*b2*b4*c1101 + 3*b1*b3**2*c1020 + 6*b1*b3*b4*c1011 +3*b1*b4**2*c1002 + b2**3*c0300 + 3*b2**2*b3*c0210 +3*b2**2*b4*c0201 + 3*b2*b3**2*c0120 + 6*b2*b3*b4*c0111 +3*b2*b4**2*c0102 + b3**3*c0030 + 3*b3**2*b4*c0021 +3*b3*b4**2*c0012 + b4**3*c0003)return wclass CloughTocher2DInterpolator(NDInterpolatorBase):"""CloughTocher2DInterpolator(points, values, tol=1e-6)Piecewise cubic, C1 smooth, curvature-minimizing interpolant in 2D. .. versionadded:: 0.9Methods-------__call__Parameters----------points : ndarray of floats, shape (npoints, ndims); or DelaunayData point coordinates, or a precomputed Delaunay triangulation. values : ndarray of float or complex, shape (npoints, ...)Data values.fill_value : float, optionalValue used to fill in for requested points outside of theconvex hull of the input points. If not provided, thenthe default is ``nan``.tol : float, optionalAbsolute/relative tolerance for gradient estimation.maxiter : int, optionalMaximum number of iterations in gradient estimation.rescale : bool, optionalRescale points to unit cube before performing interpolation.This is useful if some of the input dimensions haveincommensurable units and differ by many orders of magnitude.Notes-----The interpolant is constructed by triangulating the input datawith Qhull [1]_, and constructing a piecewise cubicinterpolating Bezier polynomial on each triangle, using aClough-Tocher scheme [CT]_. The interpolant is guaranteed to becontinuously differentiable.The gradients of the interpolant are chosen so that the curvatureof the interpolating surface is approximatively minimized. Thegradients necessary for this are estimated using the globalalgorithm described in [Nielson83]_ and [Renka84]_.Examples--------We can interpolate values on a 2D plane:>>> from scipy.interpolate import CloughTocher2DInterpolator>>> import matplotlib.pyplot as plt>>> np.random.seed(0)>>> x = np.random.random(10) - 0.5>>> y = np.random.random(10) - 0.5>>> z = np.hypot(x, y)>>> X = np.linspace(min(x), max(x))>>> Y = np.linspace(min(y), max(y))>>> X, Y = np.meshgrid(X, Y) # 2D grid for interpolation>>> interp = CloughTocher2DInterpolator(list(zip(x, y)), z)>>> Z = interp(X, Y)>>> plt.pcolormesh(X, Y, Z, shading='auto')>>> plt.plot(x, y, "ok", label="input point")>>> plt.legend()>>> plt.colorbar()>>> plt.axis("equal")>>> plt.show()See also--------griddata :Interpolate unstructured D-D data.LinearNDInterpolator :Piecewise linear interpolant in N dimensions.NearestNDInterpolator :Nearest-neighbor interpolation in N dimensions.References----------.. [1] /.. [CT] See, for example,P. Alfeld,''A trivariate Clough-Tocher scheme for tetrahedral data''.Computer Aided Geometric Design, 1, 169 (1984);G. Farin,''Triangular Bernstein-Bezier patches''.Computer Aided Geometric Design, 3, 83 (1986)... [Nielson83] G. Nielson,''A method for interpolating scattered data based upon a minimum norm network''.Math. Comp., 40, 253 (1983)... [Renka84] R. J. Renka and A. K. Cline.''A Triangle-based C1 interpolation method.'',Rocky Mountain J. Math., 14, 223 (1984)."""def__init__(self, points, values, fill_value=np.nan,tol=1e-6, maxiter=400, rescale=False):NDInterpolatorBase.__init__(self, points, values, ndim=2,fill_value=fill_value, rescale=rescale)if self.tri is None:self.tri = qhull.Delaunay(self.points)self.grad = estimate_gradients_2d_global(self.tri, self.values,tol=tol, maxiter=maxiter)def _evaluate_double(self, xi):return self._do_evaluate(xi, 1.0)def _evaluate_complex(self, xi):return self._do_evaluate(xi, 1.0j)@cython.boundscheck(False)@cython.wraparound(False)def _do_evaluate(self, const double[:,::1] xi, double_or_complex dummy):cdef const double_or_complex[:,::1] values = self.valuescdef const double_or_complex[:,:,:] grad = self.gradcdef double_or_complex[:,::1] outcdef const double[:,::1] points = self.pointscdef const int[:,::1] simplices = self.tri.simplicescdef double c[NPY_MAXDIMS]cdef double_or_complex f[NPY_MAXDIMS+1]cdef double_or_complex df[2*NPY_MAXDIMS+2]cdef double_or_complex wcdef double_or_complex fill_valuecdef int i, j, k, m, ndim, isimplex, inside, start, nvaluescdef qhull.DelaunayInfo_t infocdef double eps, eps_broadndim = xi.shape[1]start = 0fill_value = self.fill_valueqhull._get_delaunay_info(&info, self.tri, 1, 1, 0)out = np.zeros((xi.shape[0], self.values.shape[1]),dtype=self.values.dtype)nvalues = out.shape[1]eps = 100 * DBL_EPSILONeps_broad = sqrt(eps)with nogil:for i in range(xi.shape[0]):# 1) Find the simplexisimplex = qhull._find_simplex(&info, c,&xi[i,0],&start, eps, eps_broad)# 2) Clough-Tocher interpolationif isimplex == -1:# outside triangulationfor k in range(nvalues):out[i,k] = fill_valuecontinuefor k in range(nvalues):for j in range(ndim+1):f[j] = values[simplices[isimplex,j],k]df[2*j] = grad[simplices[isimplex,j],k,0]df[2*j+1] = grad[simplices[isimplex,j],k,1]w = _clough_tocher_2d_single(&info, isimplex, c, f, df)out[i,k] = wreturn outView Code 下图中红⾊的是已知采样点,蓝⾊是待插值的栅格点,三⾓形内部栅格点的数值可通过线性插值或其它插值⽅法计算出,三⾓形外部的点可在函数中指定⼀个数值(默认为NaN)。

Polar_NonPolar极性共价键

Polar_NonPolar极性共价键

Asymmetry- has different sides
If the electrons are not distributed equally, the molecule is said to be polar.
The molecule has a negative end and a positive end.
That means that the most electronegative elements are in the upper-right corner of the table.
Every element has an electronegativity value
Francium has the lowest electronegativity 0.7 Fluorine has the highest 4.0
The ELECTRONEGATIVITY of an element electronegativity helps us understand the difference between ionic and covalent bonding
• Electronegativity is the measure of the ability of an atom in a bond to attract electrons.
+
and partial positive charges (+) of other polar bonds.
Water is a molecule that consists of two polar covalent O-H bonds.
The electrons are not distributed evenly so the water molecule is polar. The negative end of the molecule is the oxygen end. O is more electronegative than H and pulls the negative electrons toward itself. Also, there are two lone pairs around oxygen.

量子力学索引英汉对照

量子力学索引英汉对照

21-centimeter line, 21厘米线AAbsorption, 吸收Addition of angular momenta, 角动量叠加Adiabatic approximation, 绝热近似Adiabatic process, 绝热过程Adjoint, 自伴的Agnostic position, 不可知论立场Aharonov-Bohm effect, 阿哈罗诺夫-玻姆效应Airy equation, 艾里方程;Airy function, 艾里函数Allowed energy, 允许能量Allowed transition, 允许跃迁Alpha decay, 衰变;Alpha particle, 粒子Angular equation, 角向方程Angular momentum, 角动量Anomalous magnetic moment, 反常磁矩Antibonding, 反键Anti-hermitian operator, 反厄米算符Associated Laguerre polynomial, 连带拉盖尔多项式Associated Legendre function, 连带勒让德多项式Atoms, 原子Average value, 平均值Azimuthal angle, 方位角Azimuthal quantum number, 角量子数BBalmer series, 巴尔末线系Band structure, 能带结构Baryon, 重子Berry's phase, 贝利相位Bessel functions, 贝塞尔函数Binding energy, 束缚能Binomial coefficient, 二项式系数Biot-Savart law, 毕奥-沙法尔定律Blackbody spectrum, 黑体谱Bloch's theorem, 布洛赫定理Bohr energies, 玻尔能量;Bohr magneton, 玻尔磁子;Bohr radius, 玻尔半径Boltzmann constant, 玻尔兹曼常数Bond, 化学键Born approximation, 玻恩近似Born's statistical interpretation, 玻恩统计诠释Bose condensation, 玻色凝聚Bose-Einstein distribution, 玻色-爱因斯坦分布Boson, 玻色子Bound state, 束缚态Boundary conditions, 边界条件Bra, 左矢Bulk modulus, 体积模量CCanonical commutation relations, 正则对易关系Canonical momentum, 正则动量Cauchy's integral formula, 柯西积分公式Centrifugal term, 离心项Chandrasekhar limit, 钱德拉赛卡极限Chemical potential, 化学势Classical electron radius, 经典电子半径Clebsch-Gordan coefficients, 克-高系数Coherent States, 相干态Collapse of wave function, 波函数塌缩Commutator, 对易子Compatible observables, 对易的可观测量Complete inner product space, 完备内积空间Completeness, 完备性Conductor, 导体Configuration, 位形Connection formulas, 连接公式Conservation, 守恒Conservative systems, 保守系Continuity equation, 连续性方程Continuous spectrum, 连续谱Continuous variables, 连续变量Contour integral, 围道积分Copenhagen interpretation, 哥本哈根诠释Coulomb barrier, 库仑势垒Coulomb potential, 库仑势Covalent bond, 共价键Critical temperature, 临界温度Cross-section, 截面Crystal, 晶体Cubic symmetry, 立方对称性Cyclotron motion, 螺旋运动DDarwin term, 达尔文项de Broglie formula, 德布罗意公式de Broglie wavelength, 德布罗意波长Decay mode, 衰变模式Degeneracy, 简并度Degeneracy pressure, 简并压Degenerate perturbation theory, 简并微扰论Degenerate states, 简并态Degrees of freedom, 自由度Delta-function barrier, 势垒Delta-function well, 势阱Derivative operator, 求导算符Determinant, 行列式Determinate state, 确定的态Deuterium, 氘Deuteron, 氘核Diagonal matrix, 对角矩阵Diagonalizable matrix, 对角化Differential cross-section, 微分截面Dipole moment, 偶极矩Dirac delta function, 狄拉克函数Dirac equation, 狄拉克方程Dirac notation, 狄拉克记号Dirac orthonormality, 狄拉克正交归一性Direct integral, 直接积分Discrete spectrum, 分立谱Discrete variable, 离散变量Dispersion relation, 色散关系Displacement operator, 位移算符Distinguishable particles, 可分辨粒子Distribution, 分布Doping, 掺杂Double well, 双势阱Dual space, 对偶空间Dynamic phase, 动力学相位EEffective nuclear charge, 有效核电荷Effective potential, 有效势Ehrenfest's theorem, 厄伦费斯特定理Eigenfunction, 本征函数Eigenvalue, 本征值Eigenvector, 本征矢Einstein's A and B coefficients, 爱因斯坦A,B系数;Einstein's mass-energy formula, 爱因斯坦质能公式Electric dipole, 电偶极Electric dipole moment, 电偶极矩Electric dipole radiation, 电偶极辐射Electric dipole transition, 电偶极跃迁Electric quadrupole transition, 电四极跃迁Electric field, 电场Electromagnetic wave, 电磁波Electron, 电子Emission, 发射Energy, 能量Energy-time uncertainty principle, 能量-时间不确定性关系Ensemble, 系综Equilibrium, 平衡Equipartition theorem, 配分函数Euler's formula, 欧拉公式Even function, 偶函数Exchange force, 交换力Exchange integral, 交换积分Exchange operator, 交换算符Excited state, 激发态Exclusion principle, 不相容原理Expectation value, 期待值FFermi-Dirac distribution, 费米-狄拉克分布Fermi energy, 费米能Fermi surface, 费米面Fermi temperature, 费米温度Fermi's golden rule, 费米黄金规则Fermion, 费米子Feynman diagram, 费曼图Feynman-Hellman theorem, 费曼-海尔曼定理Fine structure, 精细结构Fine structure constant, 精细结构常数Finite square well, 有限深方势阱First-order correction, 一级修正Flux quantization, 磁通量子化Forbidden transition, 禁戒跃迁Foucault pendulum, 傅科摆Fourier series, 傅里叶级数Fourier transform, 傅里叶变换Free electron, 自由电子Free electron density, 自由电子密度Free electron gas, 自由电子气Free particle, 自由粒子Function space, 函数空间Fusion, 聚变Gg-factor, g-因子Gamma function, 函数Gap, 能隙Gauge invariance, 规范不变性Gauge transformation, 规范变换Gaussian wave packet, 高斯波包Generalized function, 广义函数Generating function, 生成函数Generator, 生成元Geometric phase, 几何相位Geometric series, 几何级数Golden rule, 黄金规则"Good" quantum number, "好"量子数"Good" states, "好"的态Gradient, 梯度Gram-Schmidt orthogonalization, 格莱姆-施密特正交化法Graphical solution, 图解法Green's function, 格林函数Ground state, 基态Group theory, 群论Group velocity, 群速Gyromagnetic railo, 回转磁比值HHalf-integer angular momentum, 半整数角动量Half-life, 半衰期Hamiltonian, 哈密顿量Hankel functions, 汉克尔函数Hannay's angle, 哈内角Hard-sphere scattering, 硬球散射Harmonic oscillator, 谐振子Heisenberg picture, 海森堡绘景Heisenberg uncertainty principle, 海森堡不确定性关系Helium, 氦Helmholtz equation, 亥姆霍兹方程Hermite polynomials, 厄米多项式Hermitian conjugate, 厄米共轭Hermitian matrix, 厄米矩阵Hidden variables, 隐变量Hilbert space, 希尔伯特空间Hole, 空穴Hooke's law, 胡克定律Hund's rules, 洪特规则Hydrogen atom, 氢原子Hydrogen ion, 氢离子Hydrogen molecule, 氢分子Hydrogen molecule ion, 氢分子离子Hydrogenic atom, 类氢原子Hyperfine splitting, 超精细分裂IIdea gas, 理想气体Idempotent operaror, 幂等算符Identical particles, 全同粒子Identity operator, 恒等算符Impact parameter, 碰撞参数Impulse approximation, 脉冲近似Incident wave, 入射波Incoherent perturbation, 非相干微扰Incompatible observables, 不对易的可观测量Incompleteness, 不完备性Indeterminacy, 非确定性Indistinguishable particles, 不可分辨粒子Infinite spherical well, 无限深球势阱Infinite square well, 无限深方势阱Inner product, 内积Insulator, 绝缘体Integration by parts, 分部积分Intrinsic angular momentum, 内禀角动量Inverse beta decay, 逆衰变Inverse Fourier transform, 傅里叶逆变换KKet, 右矢Kinetic energy, 动能Kramers' relation, 克莱默斯关系Kronecker delta, 克劳尼克LLCAO technique, 原子轨道线性组合法Ladder operators, 阶梯算符Lagrange multiplier, 拉格朗日乘子Laguerre polynomial, 拉盖尔多项式Lamb shift, 兰姆移动Lande g-factor, 朗德g-因子Laplacian, 拉普拉斯的Larmor formula, 拉摩公式Larmor frequency, 拉摩频率Larmor precession, 拉摩进动Laser, 激光Legendre polynomial, 勒让德多项式Levi-Civita symbol, 列维-西维塔符号Lifetime, 寿命Linear algebra, 线性代数Linear combination, 线性组合Linear combination of atomic orbitals, 原子轨道的线性组合Linear operator, 线性算符Linear transformation, 线性变换Lorentz force law, 洛伦兹力定律Lowering operator, 下降算符Luminoscity, 照度Lyman series, 赖曼线系MMagnetic dipole, 磁偶极Magnetic dipole moment, 磁偶极矩Magnetic dipole transition, 磁偶极跃迁Magnetic field, 磁场Magnetic flux, 磁通量Magnetic quantum number, 磁量子数Magnetic resonance, 磁共振Many worlds interpretation, 多世界诠释Matrix, 矩阵;Matrix element, 矩阵元Maxwell-Boltzmann distribution, 麦克斯韦-玻尔兹曼分布Maxwell's equations, 麦克斯韦方程Mean value, 平均值Measurement, 测量Median value, 中位值Meson, 介子Metastable state, 亚稳态Minimum-uncertainty wave packet, 最小不确定度波包Molecule, 分子Momentum, 动量Momentum operator, 动量算符Momentum space wave function, 动量空间波函数Momentum transfer, 动量转移Most probable value, 最可几值Muon, 子Muon-catalysed fusion, 子催化的聚变Muonic hydrogen, 原子Muonium, 子素NNeumann function, 纽曼函数Neutrino oscillations, 中微子振荡Neutron star, 中子星Node, 节点Nomenclature, 术语Nondegenerate perturbationtheory, 非简并微扰论Non-normalizable function, 不可归一化的函数Normalization, 归一化Nuclear lifetime, 核寿命Nuclear magnetic resonance, 核磁共振Null vector, 零矢量OObservable, 可观测量Observer, 观测者Occupation number, 占有数Odd function, 奇函数Operator, 算符Optical theorem, 光学定理Orbital, 轨道的Orbital angular momentum, 轨道角动量Orthodox position, 正统立场Orthogonality, 正交性Orthogonalization, 正交化Orthohelium, 正氦Orthonormality, 正交归一性Orthorhombic symmetry, 斜方对称Overlap integral, 交叠积分PParahelium, 仲氦Partial wave amplitude, 分波幅Partial wave analysis, 分波法Paschen series, 帕邢线系Pauli exclusion principle, 泡利不相容原理Pauli spin matrices, 泡利自旋矩阵Periodic table, 周期表Perturbation theory, 微扰论Phase, 相位Phase shift, 相移Phase velocity, 相速Photon, 光子Planck's blackbody formula, 普朗克黑体辐射公式Planck's constant, 普朗克常数Polar angle, 极角Polarization, 极化Population inversion, 粒子数反转Position, 位置;Position operator, 位置算符Position-momentum uncertainty principles, 位置-动量不确定性关系Position space wave function, 坐标空间波函数Positronium, 电子偶素Potential energy, 势能Potential well, 势阱Power law potential, 幂律势Power series expansion, 幂级数展开Principal quantum number, 主量子数Probability, 几率Probability current, 几率流Probability density, 几率密度Projection operator, 投影算符Propagator, 传播子Proton, 质子QQuantum dynamics, 量子动力学Quantum electrodynamics, 量子电动力学Quantum number, 量子数Quantum statics, 量子统计Quantum statistical mechanics, 量子统计力学Quark, 夸克RRabi flopping frequency, 拉比翻转频率Radial equation, 径向方程Radial wave function, 径向波函数Radiation, 辐射Radius, 半径Raising operator, 上升算符Rayleigh's formula, 瑞利公式Realist position, 实在论立场Recursion formula, 递推公式Reduced mass, 约化质量Reflected wave, 反射波Reflection coefficient, 反射系数Relativistic correction, 相对论修正Rigid rotor, 刚性转子Rodrigues formula, 罗德里格斯公式Rotating wave approximation, 旋转波近似Rutherford scattering, 卢瑟福散射Rydberg constant, 里德堡常数Rydberg formula, 里德堡公式SScalar potential, 标势Scattering, 散射Scattering amplitude, 散射幅Scattering angle, 散射角Scattering matrix, 散射矩阵Scattering state, 散射态Schrodinger equation, 薛定谔方程Schrodinger picture, 薛定谔绘景Schwarz inequality, 施瓦兹不等式Screening, 屏蔽Second-order correction, 二级修正Selection rules, 选择定则Semiconductor, 半导体Separable solutions, 分离变量解Separation of variables, 变量分离Shell, 壳Simple harmonic oscillator, 简谐振子Simultaneous diagonalization, 同时对角化Singlet state, 单态Slater determinant, 斯拉特行列式Soft-sphere scattering, 软球散射Solenoid, 螺线管Solids, 固体Spectral decomposition, 谱分解Spectrum, 谱Spherical Bessel functions, 球贝塞尔函数Spherical coordinates, 球坐标Spherical Hankel functions, 球汉克尔函数Spherical harmonics, 球谐函数Spherical Neumann functions, 球纽曼函数Spin, 自旋Spin matrices, 自旋矩阵Spin-orbit coupling, 自旋-轨道耦合Spin-orbit interaction, 自旋-轨道相互作用Spinor, 旋量Spin-spin coupling, 自旋-自旋耦合Spontaneous emission, 自发辐射Square-integrable function, 平方可积函数Square well, 方势阱Standard deviation, 标准偏差Stark effect, 斯塔克效应Stationary state, 定态Statistical interpretation, 统计诠释Statistical mechanics, 统计力学Stefan-Boltzmann law, 斯特番-玻尔兹曼定律Step function, 阶跃函数Stem-Gerlach experiment, 斯特恩-盖拉赫实验Stimulated emission, 受激辐射Stirling's approximation, 斯特林近似Superconductor, 超导体Symmetrization, 对称化Symmetry, 对称TTaylor series, 泰勒级数Temperature, 温度Tetragonal symmetry, 正方对称Thermal equilibrium, 热平衡Thomas precession, 托马斯进动Time-dependent perturbation theory, 含时微扰论Time-dependent Schrodinger equation, 含时薛定谔方程Time-independent perturbation theory, 定态微扰论Time-independent Schrodinger equation, 定态薛定谔方程Total cross-section, 总截面Transfer matrix, 转移矩阵Transformation, 变换Transition, 跃迁;Transition probability, 跃迁几率Transition rate, 跃迁速率Translation,平移Transmission coefficient, 透射系数Transmitted wave, 透射波Trial wave function, 试探波函数Triplet state, 三重态Tunneling, 隧穿Turning points, 回转点Two-fold degeneracy , 二重简并Two-level systems, 二能级体系UUncertainty principle, 不确定性关系Unstable particles, 不稳定粒子VValence electron, 价电子Van der Waals interaction, 范德瓦尔斯相互作用Variables, 变量Variance, 方差Variational principle, 变分原理Vector, 矢量Vector potential, 矢势Velocity, 速度Vertex factor, 顶角因子Virial theorem, 维里定理WWave function, 波函数Wavelength, 波长Wave number, 波数Wave packet, 波包Wave vector, 波矢White dwarf, 白矮星Wien's displacement law, 维恩位移定律YYukawa potential, 汤川势ZZeeman effect, 塞曼效应。

Obstacle_avoidance

Obstacle_avoidance

the layout in Figure 1(a) could be mapped into the graph of Figure 1(b), in which the exact “shape” of each “track” is abstracted away, and we add extra nodes to mark the start and end of the path. Then finding the shortest route for a particular vehicle can be done by a standard graph-search algorithm, such as Dijkstra’s or A*. In fact for practical vehicle systems, other considerations come into play such as: the problems of accurately estimating traversal times; dealing properly with junctions and turns; scheduling in a multi-vehicle environment; and dealing with the unexpected. It is beyond the scope of this article to consider these aspects in detail, and they form areas of active research.
Basic Vehicle Path Planning
Any autonomous vehicle must be able to move around without colliding with obstacles in its environment, and most existing installations use some variation on what I call the railway track algorithm. In this the vehicle (or, more frequently, vehicles) are constrained by design to follow particular paths or roadways – the railway tracks. Given well-designed tracks, obstacle avoidance is ensured by allowing only one vehicle on to a piece of track at any one time, and path planning is achieved by translating the map of the tracks into a graph structure, in which the graph edges represent tracks and the graph nodes represent the joins between tracks (at points). As a simple example,

摩乐斯电子技术有限公司 HDM 后平面模块极性引脚选项销售集装箱 型号:73644说明书

摩乐斯电子技术有限公司 HDM 后平面模块极性引脚选项销售集装箱 型号:73644说明书

DOCUMENT STATUS P1RELEASE DATE 2022/09/1308:27:36 FUNCTIONAL SYMBOLS =0=0=0DIVISIONAL SYMBOLS THIS DRAWING CONTAINS INFORMATION THAT IS PROPRIETARY TO MOLEX ELECTRONIC TECHNOLOGIES, LLC AND SHOULD NOT BE USED WITHOUT WRITTEN PERMISSIONCURRENT REV DESC: SEE REVISION TABLE ON SHEET 3EC NO:719835DRWN:KRISHH12022/05/16CHK'D:JKACHLIC 2022/09/13APPR:JKACHLIC 2022/09/13INITIAL REVISION:DRWN:RFC_PLMIMP 2018/11/28APPR:SREED 2003/05/15THIRD ANGLE PROJECTION DRAWING SERIES B-SIZE 73644SALES ASSEMBLY , HDM BACKPLANE MODULE POLAR/GUIDE OPTION PRODUCT CUSTOMER DRAWING DOCUMENT NUMBER DOC TYPE DOC PART REVISION SDA-73644-XXXX PSD 001L MATERIAL NUMBER CUSTOMER SHEET NUMBER SEE TABLE GENERAL MARKET 1 OF 3DIMENSION UNITS SCALE mm 2:1GENERAL TOLERANCES (UNLESS SPECIFIED)ANGULAR TOL ±0.5 °4 PLACES ±3 PLACES ±2 PLACES ±0.051 PLACE ±0.10 PLACES ±DRAFT WHERE APPLICABLE MUST REMAINWITHIN DIMENSIONS NOTES:1. MATERIALS : HOUSING - LIQUID CRYSTAL POLYMER (LCP) GLASS-FILLED, UL 94V-0, COLOR: BLACK TERMINAL - PHOSPHOR BRONZE.2. FINISH: SELECTIVE GOLD (Au) IN CONTACT AREA, 0.76 MCROMETERS MINIMUM THICKNESS, NICKEL (Ni) AND GOLD FLASH OVERALL. SELECTIVE GOLD (Au) IN CONTACT AREA, 0.76 MICROMETERS MINIMUM THICKNESS, SELECTIVE MATTE TIN (Sn) ON IN TAIL AREA. NICKEL (Ni) OVERALL.3. THIS PART CONFORMS TO MOLEX PRODUCT SPECIFICATION PS-73670-9999.4. FOR MIXED CONTACT MATING LENGTHS, CONSULT FACTORY FOR AVAILABILITY.5. DIMENSIONS ARE IN MILLIMETERS.6. FOR SPECIFIC MATERIAL NUMBERS, SEE SHEET 2.7. PACKAGE PER PK-70873-0818.8. PARTS MARKED WITH PART NUMBER AND DATE CODE ON EITHER SIDE, APPROXIMATLELY WHERE SHOWN.9. POSITION TOLERANCE ARE INSPECTED BY GAUGE 737270006 OR 737270011.B2.00A ±0.1510.005X 2.0014.35±0.207.05±0.25 6.502.0M ±0.25�15.7��0.43�0.40±0.03�0.40�11.7+0.15-0.20�3.5��3.45� PCB LAYOUT : COMPONENT SIDE RECOMMENDED PCB THICKNESS: 2.50 MIN.10.005X 2.002.00BHOUSING OUTLINE0.80/0.65⌀0.838 DRILL PLATED THRU HOLE ⌀1.20 COPPER PAD⌖⌀0.15ⓂY Z⌀SEE NOTE 9ZY 73644-XXXX WW/YY SEE NOTE 8⌖⌀0.30ⓂY ZSEE NOTE 9⌖⌀0.15ⓂX Y ZSEE NOTE 9⌖⌀0.20X Y Z SEE NOTE 9ZY 1A A2BB 3CC 4DD 5E E6F F78910XFORMAT: master-tb-prod-BREVISION: J1DATE: 2021/05/01DOCUMENT STATUSP1RELEASE DATE 2022/09/1308:27:36 FUNCTIONAL SYMBOLS =0=0=0DIVISIONAL SYMBOLS DIMENSION UNITS SCALE mm 2:1GENERAL TOLERANCES (UNLESS SPECIFIED)ANGULAR TOL ±0.5 °4 PLACES ±3 PLACES ±2 PLACES ±0.051 PLACE ±0.10 PLACES ±DRAFT WHERE APPLICABLE MUST REMAIN WITHIN DIMENSIONS CURRENT REV DESC: SEE REVISION TABLE ON SHEET 3EC NO:719835DRWN:KRISHH12022/05/16CHK'D:JKACHLIC 2022/09/13APPR:JKACHLIC 2022/09/13INITIAL REVISION:DRWN:RFC_PLMIMP 2018/11/28APPR:SREED 2003/05/15THIRD ANGLE PROJECTION DRAWING SERIES B-SIZE 73644THIS DRAWING CONTAINS INFORMATION THAT IS PROPRIETARY TO MOLEX ELECTRONIC TECHNOLOGIES, LLC AND SHOULD NOT BE USED WITHOUT WRITTEN PERMISSION SALES ASSEMBLY , HDM BACKPLANE MODULE POLAR/GUIDE OPTION PRODUCT CUSTOMER DRAWING DOCUMENT NUMBER DOC TYPE DOC PART REVISION SDA-73644-XXXX PSD 001L MATERIAL NUMBER CUSTOMER SHEET NUMBER SEE TABLE GENERAL MARKET 2 OF 3MATERIALNUMBER NUMBER OF SIGNAL CONTACTS PLATING DIM "A"DIM "B"DIM "M"73644-00**72SELECTIVE GOLD/GOLD FLASH 31.6022.00 5.0073644-01**72SELECTIVE GOLD/GOLD FLASH 31.6022.00 5.5073644-02**72SELECTIVE GOLD/GOLD FLASH 31.6022.00 6.0073644-10**144SELECTIVE GOLD/GOLD FLASH 55.6046.00 5.0073644-11**144SELECTIVE GOLD/GOLD FLASH 55.6046.00 5.5073644-12**144SELECTIVE GOLD/GOLD FLASH 55.6046.00 6.0073644-20**72 SELECTIVE GOLD/SELECTIVE MATTE TIN 31.6022.00 5.0073644-21**72 SELECTIVE GOLD/SELECTIVE MATTE TIN 31.6022.00 5.5073644-22**72 SELECTIVE GOLD/SELECTIVE MATTE TIN 31.6022.00 6.0073644-30**144 SELECTIVE GOLD/SELECTIVE MATTE TIN 55.6046.00 5.0073644-31**144 SELECTIVE GOLD/SELECTIVE MATTE TIN 55.6046.00 5.5073644-32**144 SELECTIVE GOLD/SELECTIVE MATTE TIN 55.6046.00 6.001A A2BB 3C C 4D D5EE 6F F78910LOCATION ALOCATION B ROW F ROW ALOCATION BLOCATION AREF. MATERIAL NUMBER 73644-0008GUIDE PIN IS IN LOCATION BPOLARIZING KEY IS IN LOCATION A, POSITION E REF. MATERIAL NUMBER 73644-0009GUIDE PIN IS IN LOCATION A POLARIZING KEY IS IN LOCATION B, POSITION E MATERIAL NUMBER ASSIGNMENT 73644 - * * * *CIRCUIT SIZE0= 72 CIRCUIT, GOLD/GOLD FLASH1= 144 CIRCUIT, GOLD/GOLD FLASH2= 72 CIRCUIT, GOLD/MATTE TIN3= 144 CIRCUIT, GOLD/MATTE TIN* FORMERLY TIN/ LEAD 0= 5.00 MATING LENGTH1= 5.50 MATING LENGTH2= 6.00 MATING LENGTHNUMBER GUIDE POST LOCATION POLAR KEY POSITION 00B A 01A 02B B 03A 04B C 05A 06B D 07A 08B E 09A 10B F 11A 12B G 13A 14B H 15A 16B N/A17A 18N/ADOCUMENT STATUS P1RELEASE DATE 2022/09/1308:27:3632FUNCTIONAL SYMBOLS =0=0=0A 5B FDIVISIONAL SYMBOLS 91CDIMENSION UNITS SCALE mm 1:1GENERAL TOLERANCES (UNLESS SPECIFIED)ANGULAR TOL ±0.5 °4 PLACES ±3 PLACES ±2 PLACES ±0.051 PLACE ±0.10 PLACES ±DRAFT WHERE APPLICABLE MUST REMAINWITHIN DIMENSIONS SALES ASSEMBLY , HDM BACKPLANE MODULE POLAR/GUIDE OPTION PRODUCT CUSTOMER DRAWING DOCUMENT NUMBER DOC TYPE DOC PART REVISION SDA-73644-XXXX PSD 001L MATERIAL NUMBER CUSTOMER SHEET NUMBER SEE TABLE GENERAL MARKET 3 OF 3CURRENT REV DESC: SEE REVISION TABLE ON SHEET 3EC NO:719835DRWN:KRISHH12022/05/16CHK'D:JKACHLIC 2022/09/13APPR:JKACHLIC 2022/09/13INITIAL REVISION:DRWN:RFC_PLMIMP 2018/11/28APPR:SREED 2003/05/15THIRD ANGLE PROJECTION DRAWING SERIES B-SIZE 73644THIS DRAWING CONTAINS INFORMATION THAT IS PROPRIETARY TO MOLEX ELECTRONIC TECHNOLOGIES, LLC AND SHOULD NOT BE USED WITHOUT WRITTEN PERMISSIONE 6E 710F BA8D D C 4DATE REV DESCRIPTION05/19/2022L1. DRAWING MIGRATION.2. SHEET 1: E07:DIMENSION 0.92 REMOVED BECAUSE DIM 'A' AND DIM 'B' ARE WITH SAME CENTER LINE, SO DIM 0.92 IS NOT REQUIRED.3. SHEET 1: C09: DIM 0.42 CHANGED TO 0.40±0.03MM, TO FOLLOW ACTUAL COMPONENT DIMENSION.0.394. SHEET 1: D06: TOLERANCE OF DIM 'A' CHANGED TO ±0.15 FROM ±0.10, TO FOLLOW ACTUAL COMPONENT DIMENSION.5. SHEET 1: C10: DIM 2.00 CHANGED TO DIM 2.0 TO INCREASE THE TOLERANCE TO FOLLOW ACTUAL COMPONENT DIMENSION.6. SHEET 1: E04: ADDED NOTE 9 FOR POSITION TOLERANCE MEASUREMENT.7. SHEET 1: E08, E07, C04, B03 : REMOVED BRACKET FROM THE BASIC DIMENSION 10.00, B , 10.00, B RESPECTIVELY FOR TP CONTROL8. SHEET 1: B08, C10, C06, C01 : ADDED "SEE NOTE 8" FOR TP CONTROL9. SHEET 1: C08: DIM 7.05±0.20 AND 14.35±0.15 CHANGED TO 7.05±0.25 AND 14.35±0.15 RESPECTIVELY.。

Delta

Delta

Let be an abstract simplicial complex on vertex set n] := f1; 2; 3; : : : ; ng, i.e. is a collection of subsets F n] called faces which is closed under inclusion. The dimension dim(F ) of the face F is jF j? 1, and dim( ) is the maximum dimension of its faces. We say that is pure if all maximal faces of have the same dimension, equal to dim( ). There is a well-known construction (see St, Chapter 2]) of the Stanley-Reisner ring k ] associated to : one forms a certain square-free monomial ideal I in the polynomial ring A := k x1; : : : ; xn ], and then k ] is the quotient ring A=I . The ideal I is generated by the monomials xG as G runs over the inclusion-minimal Q subsets of n] which are not faces in , where xG := i2G xi . Every square-free monomial ideal I in A is of the form I for some simplicial complex , and plays a role in understanding the homological properties of I . Conversely, the rings k ] have played a role in understanding combinatorial properties of simplicial complexes, and in particular the enumeration of their faces of various dimensions (see St]). One homological property of interest for k ] are the Betti numbers

量子力学学习课件第三章英文版

量子力学学习课件第三章英文版
(1) hermitian? In this case: As is the usual polar coordinate:
On the interval
(2) The eigenvalue equation, The general solution is By using periodic boundary condition
Therefore, the set of all square-integrable functions, on a specified interval,
constitutes a (much smaller) vector space.
Mathematicians call it L2(a,b), while physicists call it Hilbert space.
the addition and the inner product
The inner product of two vectors, which generalizes the dot product in three dimensions, is defined by
2. Linear transformations
In an N-dimensional space, the vector is represented by a N-number of its components, with respect to a specified orthonormal basis:
We can define operations on vectors:
Some important concepts
On state
we measure an observable Q.

关于经纬度的英文原文及翻译

关于经纬度的英文原文及翻译

Latitude and LongitudeAny location on Earth is described by two numbers--its latitude and its longitude. If a pilot or a ship's captain wants to specify position on a map, these are the "coordinates" they would use.Actually, these are two angles, measured in degrees, "minutes of arc" and "seconds of arc." These are denoted by the symbols ( °, ', " ) e.g. 35° 43' 9" means an angle of 35 degrees, 43 minutes and 9 seconds (do not confuse this with the notation (', ") for feet and inches!). A degree contains 60 minutes of arc and a minute contains 60 seconds of arc--and you may omit the words "of arc" where the context makes it absolutely clear that these are not units of time.Calculations often represent angles by small letters of the Greek alphabet, and that way latitude will be represented by λ(lambda, Greek L), and longitude by φ (phi, Greek F). Here is how they are defined.PLEASE NOTE: Charts used in ocean navigation often use the OPPOSITE notation--λfor LONGITUDE and φfor LATITUDE. The convention followed here resembles the one used by mathematicians in 3 dimensions for spherical polar coordinates.Imagine the Earth was a transparent sphere(actually the shape is slightly oval; because of the Earth's rotation, its equator bulges out a little). Through the transparent Earth (drawing) we can see its equatorial plane, and its middle the point is O, the center of the Earth.To specify the latitude of some point P on the surface, draw the radius OP to that point. Then the elevation angle of that point above the equator is its latitude λ--northern latitude if north of the equator, southern (or negative) latitude if south of it.LatitudeIn geography, latitude (φ) is a geographic coordinate that specifies the north–south position of a point on the Earth's surface. Latitude is an angle (defined below) which ranges from 0° at the Equator to 90° (North or South) at the poles. Lines of constan t latitude, or parallels, run east–west as circles parallel to the equator. Latitude is used together with longitude to specify the precise location of features on the surface of the Earth. Two levels of abstraction are employed in the definition of these coordinates. In the first step the physical surface is modelled by the geoid, a surface which approximates the mean sea level over the oceans and its continuation under the land masses. The second step is to approximate the geoid by a mathematically simpler reference surface. The simplest choice for the reference surface is a sphere, but the geoid is more accurately modelled by an ellipsoid.The definitions of latitude and longitude on such reference surfaces are detailed in the following sections. Lines of constant latitude and longitude together constitute a graticule on the reference surface. The latitude of a point on the actual surface is that of the corresponding point on the reference surface, the correspondence being along the normal to the reference surface which passes through the point on the physical surface. Latitude and longitude together with some specification of height constitute a geographic coordinate system as defined in the specification of the ISO 19111 standard.Since there are many different reference ellipsoids the latitude of a feature on the surface is not unique: this is stressed in the ISO standard which states that "without the full specification of the coordinate reference system, coordinates (that is latitude and longitude) are ambiguous at best and meaningless at worst". This is of great importance in accurate applications, such as GPS, but in common usage, where high accuracy is not required, the reference ellipsoid is not usually stated.In English texts the latitude angle, defined below, is usually denoted by the Greek lower-case letter phi (φ or ɸ). It is measured in degrees, minutes and seconds or decimal degrees, north or south of the equator.Measurement of latitude requires an understanding of the gravitational field of the Earth, either for setting up theodolites or for determination of GPS satellite orbits. The study of the figure of the Earth together with its gravitational field is the science of geodesy. These topics are not discussed in this article. (See for example the textbooks by Torge and Hofmann-Wellenhof and Moritz.)This article relates to coordinate systems for the Earth: it may be extended to cover the Moon, planets and other celestial objects by a simple change of nomenclature.Imagine the Earth was a transparent sphere(actually the shape is slightly oval; because of the Earth's rotation, its equator bulges out a little). Through the transparent Earth (drawing) we can see its equatorial plane, and its middle the point is O, the center of the Earth. To specify the latitude of some point P on the surface, draw the radius OP to that point. Then the elevation angle of that point above the equator is its latitude λ--northern latitude if north of the equator, southern (or negative) latitude if south of it.[How can one define the angle between a line and a plane, you may well ask? After all, angles are usually measured between twolines!Good question. We must use the angle which completes it to 90 degrees, the one between the given line and one perpendicular to the plane. Herethat would be the angle (90°-λ) between OP and the Earth's axis, known as the co-latitude of P.]On a globe of the Earth, lines of latitude are circles of different size. The longest is the equator, whose latitude is zero, while at the poles--at latitudes 90° north and 90° south (or -90°) the circles shrink to a point.LongitudeLongitude is a geographic coordinate that specifies the east-west position of a point on the Earth's surface. It is an angular measurement, usually expressed in degrees and denoted by the Greek letter lambda (λ). Meridians (lines running from the North Pole to the South Pole) connect points with the same longitude. By convention, one of these, the Prime Meridian, which passes through the Royal Observatory, Greenwich, England, was allocated the position of zero degrees longitude. The longitude of other places is measured as the angle east or west from the Prime Meridian, ranging from 0° at the Prime Meridian to +180° eastward and −180° westward. Specifically, it is the angle between a plane containing the Prime Meridian and a plane containing the North Pole, South Pole and the location in question. (This forms a right-handed coordinate system with the z axis (right hand thumb) pointing from the Earth's center toward the North Pole and the x axis (right hand index finger) extending from Earth's center through the equator at the Prime Meridian.)A location's north–south position along a meridian is given by its latitude, which is approximately the angle between the local vertical and the plane of the Equator.If the Earth were perfectly spherical and homogeneous, then the longitude at a point would be equal to the angle between a vertical north–south plane through that point and the plane of the Greenwich meridian. Everywhere on Earth the vertical north–south plane would contain the Earth's axis. But the Earth is not homogeneous, and has mountains—which have gravity and so can shift the vertical plane away from the Earth's axis. The vertical north–south plane still intersects the plane of the Greenwich meridian at some angle; that angle is the astronomical longitude, calculated from star observations. The longitude shown on maps and GPS devices is the angle between the Greenwich plane and a not-quite-vertical plane through the point; the not-quite-vertical plane is perpendicular to the surface of the spheroid chosen to approximate the Earth's sea-level surface, rather than perpendicular to the sea-level surface itself.On the globe, lines of constant longitude ("meridians") extend from pole to pole, like the segment boundaries on a peeled orange.Every meridian must cross the equator. Since the equator is a circle, we can divide it--like any circle--into 360 degrees, andthe longitude φ of a point is then the marked value of that division where its meridian meets the equator.What that value is depends of course on where we begin to count--on where zero longitude is. For historical reasons, the meridian passing the old Royal Astronomical Observatory in Greenwich, England, is the one chosen as zero longitude. Located at the eastern edge of London, the British capital, the observatory is now a public museum and a brass band stretching across its yard marks the "prime meridian." Tourists often get photographed as they straddle it--one foot in the eastern hemisphere of the Earth, the other in the western hemisphere.A lines of longitude is also called a meridian, derived from the Latin, from meri, a variation of "medius" which denotes "middle", and diem, meaning "day." The word once meant "noon", and times of the day before noon were known as "ante meridian", while times after it were "post meridian." Today's abbreviations a.m. and p.m. come from these terms, and the Sun at noon was said to be "passing meridian". All points on the same line of longitude experienced noon (and any other hour) at the same time and were therefore said to be on the same "meridian line", which became "meridian" for short.About time--Local and UniversalTwo important concepts, related to latitude and (especially) longitude are Local time (LT) and Universal time (UT)Local time is actually a measure of the position of the Sun relative to a locality. At 12 noon local time the Sun passes to the south and is furthest from the horizon (northern hemisphere). Somewhere around 6 am it rises, and around 6 pm it sets. Local time is what you and I use to regulate our lives locally, our work times, meals and sleep-times.But suppose we wanted to time an astronomical event--e.g. the time when the 1987 supernova was first detected. For that we need a single agreed-on clock, marking time world-wide, not tied to our locality. That is universal time (UT), which can be defined (with some slight imprecision, no concern here) as the local time in Greenwich, England, at the zero meridian.Local Time (LT) and Time ZonesLongitudes are measured from zero to 180° east and 180° west (or -180°), and both 180-degree longitudes share the same line, in the middle of the Pacific Ocean.As the Earth rotates around its axis, at any moment one line of longitude--"the noon meridian"--faces the Sun, and at that moment, it will be noon everywhere on it. After 24 hours the Earth has undergone a full rotation with respect to the Sun, and the same meridian again faces noon. Thus each hour the Earth rotates by 360/24 = 15 degrees.When at your location the time is 12 noon, 15° to the east the time is 1 p.m., for that is the meridian which faced the Sun an hour ago. On the other hand, 15° to the west the time is 11 a.m., for in an hour's time, that meridian will face the Sun and experience noon.In the middle of the 19th century, each community across the US defined in this manner its own local time, by which the Sun, on the average, reached the farthest point from the horizon (for that day) at 12 oclock. However, travelers crossing the US by train had to re-adjust their watches at every city, and long distance telegraph operators had to coordinate their times. This confusion led railroad companies to adopt time zones, broad strips (about 15° wide) which observed the same local time, differing by 1 hour from neighboring zones, and the system was adopted by the nation as a whole.The continental US has 4 main time zones--eastern, central, mountain and western, plus several more for Alaska, the Aleut islands and Hawaii. Canadian provinces east of Maine observe Atlantic time; you may find those zones outlined in your telephone book, on the map giving area codes. Other countries of the world have their own time zones; only Saudi Arabia uses local times, because of religious considerations.In addition, the clock is generally shifted one hour forward between April and October. This "daylight saving time" allows people to take advantage of earlier sunrises, without shifting their working hours. By rising earlier and retiring sooner, you make better use of the sunlight of the early morning, and you can enjoy sunlight one hour longer in late afternoon.The Date Line and Universal Time (UT) Suppose it is noon where you are and you proceed west--and suppose you could travel instantly to wherever you wanted.Fifteen degrees to the west the time is 11 a.m., 30 degrees to the west, 10 a.m., 45 degrees--9 a.m. and so on. Keeping this up, 180 degrees away one should reach midnight, and still further west, it is the previous day. This way, by the time we have covered 360 degrees and have come back to where we are, the time should be noon again--yesterday noon.Hey--wait a minute! You cannot travel from today to the same time yesterday!We got into trouble because longitude determines only the hour of the day--not the date, which is determined separately. To avoid the sort of problem encountered above, the international date line has been established--most of it following the 180th meridian--where by common agreement, whenever we cross it the date advances one day (going west) or goes back one day (going east).That line passes the Bering Strait between Alaska and Siberia, which thus have different dates, but for most of its course it runs in mid-ocean and does not inconvenience any local time keeping.Astronomers, astronauts and people dealing with satellite data may need a time schedule which is the same everywhere, not tied to a locality or time zone. The Greenwich mean time, the astronomical time at Greenwich (averaged over the year) is generally used here. It is sometimes called Universal Time (UT).Right Ascension and DeclinationThe globe of the heavens resembles the globe of the Earth, and positions on it are marked in a similar way, by a network ofmeridians stretching from pole to pole and of lines of latitudeperpendicular to them, circling the sky. To study some particular galaxy, an astronomer directs the telescope to its coordinates.On Earth, the equator is divided into 360 degrees, with the zero meridian passing Greenwich and with the longitude angle φmeasured east or west of Greenwich, depending on where the corresponding meridian meets the equator.In the sky, the equator is also divided into 360 degrees, but the count begins at one of the two points where the equator cuts theecliptic--the one which the Sun reaches around March 21. It is called the vernal equinox ("vernal" means related to spring) or sometimes the first point in Aries, because in ancient times, when first observedby the Greeks, it was in the zodiac constellation of Aries, the ram. It has since then moved, as is discussed in the later section on precession.The celestial globe, however, uses terms and notations which differ somewhat from those of the globe of the Earth. Meridians are marked by the angle α (alpha, Greek A), called right ascension, not longitude. It is measured from the vernal equinox, but only eastward, and instead of going from 0 to 360 degrees, it is specified in hours and other divisions of time, each hour equal to 15 degrees.Similarly, where on Earth latitude goes from 90° north to 90° south (or -90°), astronomers prefer the co-latitude, the angle from the polar axis,equal to 0° at the north pole, 90° on the equator, and 180° at the south pole. It is called declination and is denoted by the letter δ (delta, Greek small D). The two angles (α, δ), used in specifying (for instance) the position of a star are jointly called its celestial coordinates.纬度和经度地球上的任何位置都是由两个数字来描述的--纬度和经度。

坐标系的种类及应用

坐标系的种类及应用

坐标系的种类及应用As a fundamental tool in mathematics and science, coordinate systems play a crucial role in representing and analyzing spatial relationships. There are several types of coordinate systems, each with its unique characteristics and applications. One of the most commonly used coordinate systems is the Cartesian coordinate system, also known as the rectangular coordinate system.作为数学和科学中的基本工具,坐标系在表示和分析空间关系中发挥着至关重要的作用。

坐标系有几种类型,每种都具有独特的特征和应用。

最常用的坐标系之一是笛卡尔坐标系,也称为直角坐标系。

这种坐标系统通过在一个平面上用两个互相垂直的轴来描述一个点的位置,其中x轴和y轴分别代表水平和垂直方向。

In the Cartesian system, each point is represented by an ordered pair of numbers, known as coordinates. The x-coordinate gives the position of the point along the horizontal axis, while the y-coordinate gives the position along the vertical axis. By using this system, geometric shapes, equations, and functions can be graphed and analyzed with precision.在笛卡尔系统中,每个点由一对数字表示,称为坐标。

Roots of complex numbers

Roots of complex numbers

Lecture 4•Roots of complex numbers •Characterization of a polynomial by its roots •Techniques for solving polynomial equationsnROOTS OF COMPLEX NUMBERSDef.:A number u is said to be an n -th root of complex number z if u n = z , and we write u = z 1/n .Th.:• Every complex number has exactly n distinct n -th roots.Let z = r (cos θ + i sin θ); u = ρ(cos α + i sin α). Then r (cos θ + i sin θ) = ρn (cos α + i sin α)n = ρn (cos nα + i sin nα)⇒ ρ = r , nα = θ + 2πk(k integer)Thus ρ = r1/n, α = θ/n + 2πk/n .n distinct values for k from 0 to n − 1. (z /= 0)So u = z 1/n = r 1/nΣ . θ cos n Σ 2πk + n .θ + i s in n ΣΣ 2πk + n,k = 0, 1, . . . , n −1•Example 1 : nth roots of unity :Note. f (z ) = z1/nis a “multi -valued” function.x n = 1 (i .e . x n-1 = 0)⇒ x = 11/n1 = e2 m ν ı⇒ 11/ n = e2 m ν ı / n=cos(2m ν)+ısin(2m ν)n n11/5= cos( 2m ν ) + ı sin( 2m ν) (m = 0,1,2,3,4).5 5k=0k=144121212Example 2. Find all cubic roots of z = −1 + i:u = (−1 + i)1/3u = (√2)1/3Σ.3π 1cos4 3Σ2πk+3.3π 1+ i s in4 3ΣΣ2πk+3, k = 0, 1, 2 that is,k = 0 : 21/6..cos πΣ+ i sin πΣk = 1 : 21/6k = 2 : 21/6cos 11π.cos 19π+ i sin 11π12 Σ+ i sin 19π•Equivalently:u = (−1 + i)1/3 = e(1/3) ln(−1+i) = e(1/3)[ln√2+i(3π/4+2kπ)]= (√2)1/3e i(π/4+2kπ/3)k=2Roots of polynomialsP(z) ≡a z n+a z n-1+ +a .n n-1 0) = 0 ⇒z i is a rootP(z = zi[Gauss, 1799]•proof of fundamental theorem of algebra is given in the course “Functions of a complex variable”, Short Option S1Roots of polynomialsan-1 =-∴z an ;j aan= (-1)n⇓zjP(z) ≡ a z n+a z n-1 + +a .n n-1 0P(z = zi) = 0 ⇒z i is a rootCharacterising a polynomial by its rootsa z n+a z n-1+ +a =a (z -z )(z -z ) (z -z )n n-1 0 n 1 2 nn n=an(z n-z n-1∴zj =1 + + (-1)n⇓j =1zj).Comparing coefficients of z n−1and z0j2 1 0ae.g. quadratic equations :(-a 1a x 2 + a x + aRoots:x 1,2 =2a 2If complex, roots come in complex conjugate pairsSum of rootsa 1 = -(x+x )Product of rootsa 0= x .x121 222for roots• General solutions not available for higher order polynomials (quartics and above)^Can find solutions in special cases….a5 5 5 5 Example 1:z 5+ 32 = 0• The solutions of the given equation are the fifth roots of −32:(−32)1/5 = 321/5that is,Σ . π cos5 Σ 2πk + 5 . π + i s in 5 ΣΣ 2πk + 5, k = 0, 1, 2, 3, 4k = 0 : 2 . cos π Σ + i sin πk=1 Im z. 55 Σ k=0k = 1 : 2 cos 3π + i sin 3πk=2k = 2 : −2Rek = 3 : 2 . cos 7π + i sin 7π Σ 5 k = 4 : 2 . cos 9π + i sin 9π Σ 5k=3k=414 14(z +ı)7+ (z -ı)7=0( z +ı)7 =-1 = e( 2 m+1)νız -ı⇒z +ız -ı= e( 2 m+1)νı /7⇒z(1 - e( 2m+1)νı/7 ) =-ı(1 + e( 2m+1)νı/7 ) e( 2m+1)νı/7 + 1⇒z =ıe( 2m+1)νı/7 - 1e( 2m+1)νı/14 + e-( 2 m+1)νı/14 ıe( 2m+1)νı/14 - e-( 2 m+1)νı/142cos( 2m+1ν )=ı142ıs in( 2m+1ν )= cot( 2m+1ν )m=0,1,2,3,4,5,6Example2:Roots of polynomials =14 Example2:an alternative form(z +ı)7+ (z -ı)7=0We will often need the coefficient of x r y n-r in(x + y)nThese are conveniently obtained from Pascal’s triangle:Solution:z = cot( 2m+1ν)7th row of Pascal’s triangle is 1 7 21 35 35 21 7 1 so(z +ı)7+ (z -ı)7= 0 ⇒z7- 21z5+ 35z3- 7z = 0 (x +y)01(x+y)111(x+y)2121(x+y)31331(x+y)414641(x+y)51510105114Example 2 : yet another formThe original equation(z + ı)7 + (z - ı)7 = 0 ⇒ z 7 - 21z 5 + 35z 3 - 7z = 0can be written in another form :z 7 - 21z 5 + 35z 3 - 7z = 0z m , m = 3⇒ z 6 - 21z 4 + 35z 2 - 7 = 0 or z =⇒ w 3 - 21w 2 + 35w - 7 = 0 (w ≡ z 2 )Sum of roots⇒ 2m =0cot 2 ( 2m +1ν ) = 21 ∴. ΣPascal’s triangle = table of binomial coefficients nrn!r!(n−r)!i.e., coefficients of x r y n−r in (x + y)n[Pascal, 1654]11 11 2 11 3 3 11 4 6 4 11 5 10 10 5 11 6 15 20 15 6 11 7 21 35 35 21 7 1•r-th element of n-th row given by sum of two elements above it in (n −1)-th row:. Σ.n=rΣ.n −1+r − 1Σn −1r[u s e(x+y)n=(x+y)n−1x+(x+y)n−1y]=Historical note♦binomial coefficients already known in the middle ages:•“Pascal’s triangle”first discovered by Chinese mathematicians of 13th century to find coefficients of (x + y)n. Σn•r =n!in Hebrew writings of 14th century is r!(n−r)!Pascal (1654) rediscovers triangle and most importantly unites algebraic and combinatorial viewpoints−→ theory of probability; proof by induction♦2z = -cot 2 (m ν/8)m = 1, 2, 3Ex 3Another example where the underlying equation is not obvious :( + y )01 (x + y )1 119th row of Pascal’s triangle is(x + y )2121(x + y )31 3 3 1(x + y )41 4 6 4 1 (x + y )515 10 10 511 8 28 56 70 56 28 8 1so1 [(z +1)8 - (z -1)8 ] = 8z 7 + 56z 5 + 56z 3 + 8z= 8z [w 3 + 7w 2 + 7w +1] (w ≡ z 2 ).Now(z +1) 8 - (z , -1)8= 0e m ν ı. / 4 + 1whenz +1 z -1= e2m ν i /8i.e. when z = e m ν ı / 4- 1= -ı cot(m ν / 8) (m = 1,2,…,7),so the roots of the given equation arez 3 + 7z 2+ 7z +1 = 0.ExampleQuestion from 2008 PaperFind all the solutions of the equation.z + iΣnand solvez −i= −1 ,z4−10z2 + 5 = 0 .∫z+i,n z − i =−1=e i(π+2Nπ),N i n t e g e r⇒z + iz −i=ei(π/n+2Nπ/n),N=0,1,...,n−1Then : z = i e i(π/n+2Nπ/n) + 1e i(π/n+2Nπ/n) − 1 = icos[π(1 + 2N )/(2n)]= cotgi sin[π(1 + 2N )/(2n)]π(1 + 2N ).2nFor n = 5 : (z+i)5 = −(z−i)5⇒z(z4−10z2+5) = 0 . Then the 4 roots of z4−10z2+5 = 0 arcotgπ10, cotg 3π10, cotg 7π10, cotg 9π .10rEx 4 Show thatz 2m- a 2mz 2 - a 2= (z 2 - 2az cos ν+ a m2 )(z 2 - 2az cos 2ν m+ a 2 ) (z 2 - 2az cos (m -1)ν m + a 2 ).i.e. Show that P (z ) = Q (z ) whereP (z ) ≡ z 2m - a 2m(Roots :z = a e r ν i / m)Q (z ) ≡ (z 2- a 2)(z 2- 2az cos ν + a 2 )(z 2 - 2az cos 2ν + a 2 ) (z 2- 2az cos (m -1)ν + a 2 ).m m mRootsz = a cosr ν ± rm=a (cosr ν±m)=a e ±ır ν / m (r =0,1,…,m ).Leading coefficienta 2m = 1P(z) and Q(z) identical•This concludes part A of the course.A. Complex numbers1 Introduction to complex numbers2 Fundamental operations with complex numbers3 Elementary functions of complex variable4 De Moivre’s theorem and applications5 Curves in the complex plane6 Roots of complex numbers and polynomials。

英语高级视听 听力原文 Unit 5 The global warning

英语高级视听 听力原文 Unit 5 The global warning

Unit 5 The global warningThe North Pole has been frozen for 100,000 years. But according to scientists, that won't be true by the end of this century. The top of the world is melting.There's been a debate burning for years about the causes of global warming. But the scientists you're about to meet say the debate is over. New evidence shows man is contributing to the warming of the planet, pumping out greenhouse gases that trap solar heat.Much of this new evidence was compiled by American scientist Bob Corell, who led a study called the "Arctic Climate Impact Assessment." It's an awkward name — but consider the findings: the seas are rising, hurricanes will be more powerful, like Katrina, and polar bears may be headed toward extinction.What does the melting arctic look like? Correspondent Scott Pelley went north to see what Bob Corell calls a "global warning."Towers of ice the height of 10-story buildings rise on the coast of Greenland. It's the biggest ice sheet in the Northern Hemisphere, measuring some 700,000 square miles. But temperatures in the arctic are rising twice as fast as the rest of the world, so a lot of Greenland's ice is running to the sea."Right now the entire planet is out of balance," says Bob Corell, who is among the world's top authorities on climate change. He led 300 scientists from eight nations in the "Arctic Climate Impact Assessment."Corell believes he has seen the future. "This is a bellwether, a barometer. Some people call it the canary in the mine. The warning that things are coming," he says. "In 10 years here in the arctic, we see what the rest of the planet will see in 25 or 35 years from now."Over the last few decades, the North Pole has been dramatically reduced in size and Corell says the glaciers there have been receding for the last 50 years.Back in 1987, President Reagan asked Corell to look into climate change. He's been at it ever since.In Iceland, he showed 60 Minutes glaciers that were growing until the 1990sand are now melting. In fact, 98 percent of the world's mountain glaciers are melting.Corell says all that water will push sea levels three feet higher all around the world in 100 years."You and I sit here, another foot. Your children, another foot. Your grandchildren, another foot. And it won't take long for sea level to inundate," says Corell."Sea level will be inundating the low lands of virtually every country of the world, ours included," Corell predicts.To find the sights and sounds of the arctic melting, there are few places better than a fjord in Greenland, with a glacier just a short distance away.Pelley stood on a huge block of ice that had split off from the glacier and had dropped into the sea — a big iceberg."This part of Greenland is melting faster than just about any other. To get a sense of the enormity of what's happening, consider this: The ice that is melting here is the equivalent of all the ice in the Alps," Pelley explained, standing atop the iceberg.That's more than 105 million acres of melted ice in 15 years. Just four minutes after Pelley cleared off this berg, part of the ice caved in.60 Minutes got a bird's-eye view of how unstable the ice is becoming on a flight with glaciologist Carl Boggild.Boggild anchored 10 research stations to the ice. But every time he comes to visit, the ice and his stations have moved.Flying over the ice, Pelley noticed lots of fissures and crevices breaking through the ice.Asked what causes this, Boggild explained, "This is actually the ice flow, where you have so much tension in the ice that it cannot stick together. And it breaks and opens a crevice which goes about 150, 200 feet down."The ice is also melting on the sides, Boggild says.High overhead, Pelley remarked that one could hear the water running."It's like a small river," Boggild said.A leading theory says those little rivers lubricate the bottom of the ice sheet, helping it move off the bedrock and out to sea.And there may be no stopping it. Arctic warming is accelerating. It's a chain reaction. As snow and ice melt they reveal dark land and water that absorb solar heat. That melts more snow and ice, and around it goes.There's long been a debate about how much of this is earth's naturally changing climate and how much is man's doing. Paul Mayewski, at the University of Maine, says the answer to that question is frozen inside an ice core from Greenland.With funding from the National Science Foundation, Mayewski has led 35 expeditions collecting deep ice cores from glaciers. The ice captures everything in the air, laying down a record covering half a million years."We can go to any section of the ice core, to tell, basically, what the greenhouse gas levels were; we can tell whether or not it was stormy, what the temperatures were like," Mayewski explains.60 Minutes brought Mayewski back to Greenland, where he says his research has proven that the ice and the atmosphere have man's fingerprints all over them.Mayewski says we haven't seen a temperature rise to this level going back at least 2,000 years, and arguably several thousand years.As for carbon dioxide (CO2) levels, Mayewski says, "we haven't seen CO2 levels like this in hundreds of thousands of years, if not millions of years." What does that tell him?"It all points to something that has changed and something that has impacted the system which wasn't doing it more than 100 years ago. And we know exactly what it is. It's human activity," he says.It's activity like burning fossil fuels, releasing carbon dioxide and other greenhouse gases. The U.S. is by far the largest polluter. Corell says there's so much greenhouse gas in the air already that more temperature rise is inevitable.Even if we stopped using every car, truck, and power plant — stopping all greenhouse gas emissions — Mayewski says the planet would continue towarm anyway. "Would continue to warm for another, about another degree," he says.That's enough to melt the Arctic — and if greenhouse gases continue to increase, the temperature will rise even more. The ice that's melting already is changing the weather by disrupting ocean currents.Corell points to floods in the U.S., heat waves in Europe; and 60 Minutes wanted to know about the catastrophic 2005 hurricane season."The one thing I think we can say with a fairly high degree of confidence is the severity of the storms, how strong the storms, these cyclonic events like hurricanes and cyclones in the Pacific, are going to get — they're gonna be more severe. Now one thing that is in doubt is whether there'll be more of them," Corell explains."The oceans of the Northern Hemisphere are the warmest they've been on record. When they get up in that temperature, they spin off hurricanes. Well, if it goes up another degree, it's gonna spawn these with more intensity," Corell says.The name "arctic" comes from ancient Greek meaning "Land of the Great Bear."But the warming climate is threatening this icon of the arctic, the polar bear. Flying above the sub-arctic region of Hudson Bay, Canadian scientist Nick Lunn is hunting polar bears in a 30-year study that tracks their health. It's the job of his assistant Evan Richardson to take them down with a tranquilizer dart.Once tranquilized, Lunn carefully checks the bear with a pole, without getting too close.The polar bear is the largest predator on land. Native people in the region say he'll even hunt humans, but not on the day Pelley joined Lunn: with the tranquilizer, the bear was awake but immobile.The scientists knew this bear by his tattoo. His history is written chapter and verse in the "bear bible.""This is the record book of all the bears that have been handled by us or Manitoba Conservation," Lunn explains.The study began at the Wapusk National Park, because the bear population was thought to be the healthiest in the world.Lunn's annual checkup records changes in fat, dimensions and an inventory of weapons. The polar bear uses its teeth to hunt primarily one thing — seals. That's where arctic warming comes in.Polar bears can only hunt on the ice. Lunn says the ice is breaking up three weeks earlier than it did 30 years go. He's now finding female bears 55 pounds lighter — weaker mothers with fewer cubs.Asked how the bear population has changed since he started his research, Lunn says, "When we first started doing this research, we've done inventories in the mid-80s, in the mid-90s. Both times we came out with an estimate of approximately 1,200 animals for what is known as the western Hudson Bay population. The numbers now suggest that the population has declined to below 1,000."The bears are unlikely to survive as a species if there's a complete loss of ice in summer, which the arctic study projects will happen by the end of this century.There are skeptics who question climate change projections like that, saying they're no more reliable than your local weatherman. But Mayewski says arctic projections done decades ago are proving accurate."That said, the skeptics have brought up some very, very interesting issues over the last few years. And they've forced us to think more and more about the data that we collect. We can owe the skeptics a vote of thanks for making our science as precise as it is today," says Mayewski.One big supporter of climate science research is the Bush administration, spending $5 billion a year. But Mr. Bush refuses to sign a treaty forcing cuts in greenhouse gases.The White House also declined 60 Minutes' request for an interview. Corell, who first studied the issue for President Reagan, believes the climate change facts are in, even if President Bush does not."When you look at the American government, which is saying essentially, 'Wait a minute. We need to study this some more. We can't flip our energy use overnight. It would hurt the economy.' When you hear that, what do you think?" Pelley asked."Well, what I do then is, I try to tell them exactly what we know scientifically. The science is, I believe, unassailable," says Corell. "I'm not arguing their policy, that's their business, how they deal with policy. But my job is to say,scientifically, shorten that time scale so that if you don't push out the effects of climate change into the long, long distant future. Because even under the best of circumstances, this natural system of a climate will continue to warm the planet for literally hundreds of years, no matter what we do."。

GeoGebra指南:教学AS和A级进阶数学说明书

GeoGebra指南:教学AS和A级进阶数学说明书

A guide to use GeoGebra when teaching AS and A level Further Mathematics Below you can find links to GeoGebra files designed to help you teach the content of AS and Alevel Further Mathematics qualifications with the aid of GeoGebra.These can be used for teaching, or as students’ aided or independent learning materials with Pearson Textbooks.Core Pure MathematicsCore Pure AS/Year 1 Core Pure Year 2OptionsFurther Pure 1 Further Pure 2 Further Statistics 1 Further Statistics 2 Further Mechanics 1 Further Mechanics 2 Decision 1 Decision 2AS/Year 1 Further Mathematics – Core PureArgand diagrams (Chapter 2)Explore adding and subtracting complex numbers on an Argand diagram. (Page 19)•GeoGebra interactiveExplore multiplying and dividing complex numbers on an Argand diagram. (Page 25)•GeoGebra interactiveExplore the locus of z, when |z − z1| = r (Page 28)•GeoGebra interactiveExplore the locus of z, when |z − z2| = |z − z2| (Page 29)•GeoGebra interactiveExplore the locus of z, when arg (z − z1) = θ(Page 32)•GeoGebra interactiveExplore shaded region on an Argand diagram. (page 37)•GeoGebra interactiveVolumes of revolution (Chapter 5)•Page 76: Explore volumes of revolution around the x- and y-axes using GeoGebra.•GeoGebra interactiveCP1 CP2 FP1 FP2 FS1 FS2 FM1 FM2 D1 D2AS/Year 1 Further Mathematics – Core Pure (Cont’d)Linear transformations (Chapter 7)Explore rotations of the unit square. (Page 134)•GeoGebra interactiveExplore enlargements and stretches of triangle T. (Page 137)•GeoGebra interactiveExplore rotations about the coordinate axes. (Page 145)•GeoGebra interactiveVectors (Chapter 9)Explore the vector equation of a line. (Page 168)•GeoGebra interactiveExplore the vector and Cartesian equations of a plane. (Page 176)•GeoGebra interactiveUse GeoGebra to consider the scalar product as the component of one vector in the directionof another. (Page 179)•GeoGebra interactiveExplore the angle between a line and a plane. (Page 186)•GeoGebra interactiveVisualise the angle between two planes. (Page 187)•GeoGebra interactiveExplore the perpendicular distance between two lines. (Page 194)•GeoGebra interactiveExplore reflections in a plane using GeoGebra. (Page 198)•GeoGebra interactiveYear 2/ A level Further Mathematics – Core PureComplex numbers (Chapter 1)Explore nth roots of complex numbers in an Argand diagram. (Page 25)•GeoGebra interactiveSeries (Chapter 2)Explore graphs of the successive Maclaurin polynomials. (Page 42)•GeoGebra interactiveCP1 CP2 FP1 FP2 FS1 FS2 FM1 FM2 D1 D2Year 2/ A level Further Mathematics – Core Pure (Cont’d)Methods in calculus (Chapter 3)Explore the integral. (Page 54)•GeoGebra interactiveVolumes of revolution (Chapter 4)Explore volumes of revolution around the x-axis. (Page 78)•GeoGebra interactiveExplore volumes of revolution around the y-axis. (Page 81)•GeoGebra interactivePolar coordinates (Chapter 5)Explore curves given in polar form using GeoGebra. (Page 106)•GeoGebra interactiveExplore the area enclosed by a loop of the polar curve with the form r = a sin θ. (Page 110)•GeoGebra interactiveHyperbolic functions (Chapter 6)Explore graphs of hyperbolic functions. (Page 122)•GeoGebra interactiveMethods in differential equations (Chapter 7)Explore families of solution curves. (Page 148)•GeoGebra interactiveModelling with differential equations (Chapter 8)Explore simple harmonic motion. (Page 175)•GeoGebra interactiveExplore damped harmonic motion. (Page 181)•GeoGebra interactiveExplore forced harmonic motion. (Page 183)•GeoGebra interactiveCP1 CP2 FP1 FP2 FS1 FS2 FM1 FM2 D1 D2A level Further Mathematics – Further Pure 1Vectors (Chapter 1)Explore the cross product of two vectors. (Page 2)•GeoGebra interactiveExplore the area of a parallelogram using vector notation. (Page 8)•GeoGebra interactiveExplore the scalar triple product. (Page 11)•GeoGebra interactiveExplore the vector equation of a line, written using a cross product. (Page 16)•GeoGebra interactiveConic sections 1 (Chapter 2)Explore the focus-directrix properties of a parabola. (Page 35)•GeoGebra interactiveExplore the locus of M. (Page 56 Question 7)•GeoGebra interactiveConic sections 2 (Chapter 3)Explore conic sections. (Page 63)•GeoGebra interactiveExplore the foci and directrices of an ellipse. (Page 69)•GeoGebra interactiveExplore the foci and directrices of a hyperbola. (Page 72)•GeoGebra interactiveExplore the locus of the midpoint of AB. (Page 84)•GeoGebra interactiveInequalities (Chapter 4)Explore the solution to the inequality. (Page 97)•GeoGebra interactiveExplore the solution to the inequality. (Page 101)•GeoGebra interactiveCP1 CP2 FP1 FP2 FS1 FS2 FM1 FM2 D1 D2A level Further Mathematics – Further Pure 1 (Cont’d)Taylor series (Chapter 6)Explore the Taylor series expansion of f(x) = tan x (Page 133)•GeoGebra interactiveMethods in calculus (Chapter 7)Explore the graph of the function t = e ln t (Page 154)•GeoGebra interactiveNumerical methods (Chapter 8)Explore tangent fields. (Page 162)•GeoGebra interactivePage 174: Explore the use of Simpson's rule to estimate the integral. (Page 174)•GeoGebra interactiveA level Further Mathematics – Further Pure 2Number theory (Chapter 1)Implement the Euclidean algorithm. (Page 6)• GeoGebra interactiveCarry out divisibility tests. (Page 17)•GeoGebra interactiveExplore permutations. (Page 31)•GeoGebra interactiveExplore combinations. (Page 33)•GeoGebra interactiveGroups (Chapter 2)Explore groups of symmetries. (Page 57)•GeoGebra interactiveExplore generators. (Page 69)•GeoGebra interactiveCP1 CP2 FP1 FP2 FS1 FS2 FM1 FM2 D1 D2A level Further Mathematics – Further Pure 2 (Cont’d)Complex numbers (Chapter 3)Explore the locus of z when |z - a| = k|z - b| (Page 88)•GeoGebra interactiveExplore the locus of z when arg((z - a)/(z - b)) = θ (Page 91)•GeoGebra interactiveExplore this region. (Page 98)•GeoGebra interactiveExplore these transformations. (Page 101)•GeoGebra interactiveRecurrence relations Chapter 4)Play the Tower of Hanoi. (Page 130)•GeoGebra interactiveMatrix algebra (Chapter 5)Explore eigenvalues and eigenvectors. (Page 155)•GeoGebra interactiveIntegration techniques (Chapter 6)Explore the use of integration to find the arc length between two points. (Page 199) •GeoGebra interactiveExplore the use of integration to find the length of an arc on a curve with a polar equation.(Page 201)•GeoGebra interactiveExplore the use of integration to find the area of a surface of revolution. (Page 208) •GeoGebra interactiveA level Further Mathematics – Further Statistics 1Discrete random variables (Chapter 1)Explore probability distributions of a discrete random variable and compare the theoreticaldistribution with observed results generated from that discrete random variable. (Page 5)•GeoGebra interactiveCP1 CP2 FP1 FP2 FS1 FS2 FM1 FM2 D1 D2A level Further Mathematics – Further Statistics 1 (Cont’d)Poisson distributions (Chapter 2)Explore the Poisson distribution. (Page 21)•GeoGebra interactiveGeometric and negative binomial distributions (Chapter 3)Explore the geometric distribution. (Page 44)•GeoGebra interactiveExplore the cumulative geometric distribution. (Page 45)•GeoGebra interactiveExplore the negative binominal distribution. (Page 50)•GeoGebra interactiveHypothesis testing (Chapter 4)Explore critical regions for a Poisson distribution. (Page 63)•GeoGebra interactiveExplore critical regions for a geometric distribution. (Page 70)•GeoGebra interactiveChi-squared tests (Chapter 6)Explore the chi-squared distribution to determine critical values for goodness of fit. (Page 98)•GeoGebra interactiveQuality of tests (Chapter 8)Explore probabilities of Type I and Type II errors in a normal distribution. (Page 154)•GeoGebra interactiveA level Further Mathematics – Further Statistics 2Linear regression (Chapter 1)Explore the calculation of a least squares regression line. (Page 3)•GeoGebra interactiveExplore residuals of data points and reasonableness of fit. (Page 11)•GeoGebra interactiveCP1 CP2 FP1 FP2 FS1 FS2 FM1 FM2 D1 D2A level Further Mathematics – Further Statistics 2 (Cont’d)Correlation (Chapter 2)Explore linear correlation between two variables, measured by the PMCC. (Page 22)•GeoGebra interactiveExplore Spearman’s rank correlation coefficient. (Page 27)•GeoGebra interactiveContinuous distributions (Chapter 3)Explore probability density functions. (Page 46)•GeoGebra interactiveExplore cumulative distribution functions. (Page 51)•GeoGebra interactiveEstimation, confidence intervals and tests using a normal distribution (Chapter 5) Explore biased and unbiased estimators. (Page 114)•GeoGebra interactiveFurther hypothesis tests (Chapter 6)Explore the F-distribution using GeoGebra and use it to determine critical values of the samplevariances. (Page 151)•GeoGebra interactiveConfidence intervals and tests using the t-distribution (Chapter 7)Explore the t-distribution using GeoGebra and use it to determine critical values of the samplevariances. (Page 165)•GeoGebra interactiveCP1 CP2 FP1 FP2 FS1 FS2 FM1 FM2 D1 D2A level Further Mathematics – Further Mechanics 1Momentum and impulse (Chapter 1)Explore particle collisions. (Page 4)•GeoGebra interactiveExplore collisions with two moving particles. (Page 5)•GeoGebra interactiveExplore particle collisions with known impulse. (Page 7)•GeoGebra interactiveExplore particle collisions in two dimensions. (Page 10)•GeoGebra interactiveElastic strings and springs (Chapter 3)Explore Hooke's law in equilibrium problems involving two elastic springs. (Page 41) •GeoGebra interactiveExplore Hooke's law in equilibrium problems involving one elastic spring. (Page 42) •GeoGebra interactiveExplore Hooke's law in dynamics problems. (Page 47)•GeoGebra interactiveElastic collisions in one dimension (Chapter 4)Explore direct impact. (Page 72)•GeoGebra interactiveExplore direct impact with a known impulse. (Page 73)•GeoGebra interactiveExplore the direct collision of a falling particle with a smooth plane. (Page 77)•GeoGebra interactiveExplore the loss of kinetic energy in a collision. (Page 80)•GeoGebra interactiveExplore successive collisions. (Page 85)•GeoGebra interactiveExplore successive impacts of a falling particle. (Page 88)•GeoGebra interactiveCP1 CP2 FP1 FP2 FS1 FS2 FM1 FM2 D1 D2A level Further Mathematics – Further Mechanics 1 (Cont’d)Elastic collisions in two dimensions (Chapter 5)Explore oblique impact with a fixed surface. (Page 97)•GeoGebra interactiveExplore successive oblique impacts with a fixed surface. (Page 105)•GeoGebra interactiveExplore oblique impacts of smooth spheres. (Page 111)• GeoGebra interactiveA level Further Mathematics – Further Mechanics 2Circular motion (Chapter 1)Explore circular motion of a particle attached to a light inextensible string. (Page 6) •GeoGebra interactiveExplore circular motion in three dimensions. (Page 11)•GeoGebra interactiveExplore vertical circular motion. (Page 20)•GeoGebra interactiveExplore motion of a particle not constrained on a circular path. (Page 26)•GeoGebra interactiveCentres of mass of plane figures (Chapter 2)Explore the centre of mass of systems of particles. (Page 37)•GeoGebra interactiveExplore the centre of mass of particles arranged in a plane. (Page 39)•GeoGebra interactiveExplore centres of mass of standard uniform plane laminas. (Page 45)•GeoGebra interactiveExplore centres of mass of a framework. (Page 55)•GeoGebra interactiveCP1 CP2 FP1 FP2 FS1 FS2 FM1 FM2 D1 D2A level Further Mathematics – Further Mechanics 2 (Cont’d)Further centres of mass (Chapter 3)Explore the centre of mass of a solid of revolution. (Page 90)• GeoGebra interactiveExplore toppling and sliding. (Page 110)•GeoGebra interactiveKinematics (Chapter 4)Explore terminal or limiting velocity. (Page 162)•GeoGebra interactiveDynamics (Chapter 5)Explore simple harmonic motion. (Page 184)•GeoGebra interactiveExplore calculations for simple harmonic motion with a reference circle. (Page 191) •GeoGebra interactiveExplore the simple harmonic motion of a vertical spring. (Page 201)•GeoGebra interactiveA level Further Mathematics – Decision 1Algorithms (Chapter 1)See the operation of the first-fit algorithm using GeoGebra. (Page 17)•GeoGebra interactiveSee the operation of the first-fit decreasing algorithm using GeoGebra. (Page 18)•GeoGebra interactiveCP1 CP2 FP1 FP2 FS1 FS2 FM1 FM2 D1 D2A level Further Mathematics – Decision 1 (Cont’d)Algorithms on graphs (Chapter 3)Example 1: Explore Kruskal's algorithm using GeoGebra. (Page 53)•GeoGebra interactiveExample 2: Explore Kruskal's algorithm using GeoGebra. (Page 54)• GeoGebra interactiveExplore Prim's algorithm using GeoGebra. (Page 57)•GeoGebra interactiveExplore Dijkstra's algorithm using GeoGebra. (Page 66)•GeoGebra interactiveExplore Floyd's algorithm using GeoGebra. (Page 76)• GeoGebra interactiveLinear programming (Chapter 7)Explore graphical solutions to linear programming problems. (Page 147)•GeoGebra interactiveExplore how the optimal solution can be found using the objective line method. (Page 150) •GeoGebra interactiveExplore how the optimal solution can be found using the objective line method. (Page 153) • GeoGebra interactiveExplore how the optimal solution can be found using the objective line method. (Page 154) •GeoGebra interactiveExplore how the optimal solution can be found using the objective line method. (Page 155) •GeoGebra interactiveExplore how the optimal solution can be found using vertex testing. (Page 157)•GeoGebra interactiveExplore how the optimal solution can be found using vertex testing. (Page 158)•GeoGebra interactiveExplore how the optimal solution can be found using the objective line method. (Page 163)•GeoGebra interactiveCP1 CP2 FP1 FP2 FS1 FS2 FM1 FM2 D1 D2A level Further Mathematics – Decision 1 (Cont’d)Critical path analysis (Chapter 8)Explore event times in activity networks. (Page 230)•GeoGebra interactiveExplore critical paths. (Page 233)•GeoGebra interactiveA level Further Mathematics – Decision 2Transportation problems (Chapter 1)Explore how the north-west corner method can be used to find an initial solution. (Page 4) •GeoGebra interactiveExplore how to calculate shadow costs. (Page 13)•GeoGebra interactiveExplore how to calculate improvement indices. (Page 16)•GeoGebra interactiveExplore how to obtain an improved solution with the stepping stone method. (Page 16) •GeoGebra interactiveFlows in networks 1 (Chapter 3)Explore cuts and their capacities in this network. (Page 81)•GeoGebra interactiveFind feasible flows on this network. (Page 89)•GeoGebra interactiveFlows in networks 2 (Chapter 4)Explore feasible flows through a directed network with lower and upper capacities. (Page 113) •GeoGebra interactiveExplore cuts in a directed network with upper and lower capacities. (Page 116)•GeoGebra interactiveGame theory (Chapter 6)Find stable solutions for zero-sum games. (Page 186)•GeoGebra interactiveExplore the optimal solution to two-player zero-sum game with no stable solution. (Page 199) •GeoGebra interactiveCP1 CP2 FP1 FP2 FS1 FS2 FM1 FM2 D1 D2A level Further Mathematics – Decision 2 (Cont’d)Recurrence relations (Chapter 7)Play the Tower of Hanoi. (Page 224)•GeoGebra interactiveDecision analysis (Chapter 8)Explore EMV. (Page 246)•GeoGebra interactive。

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Polar Complex Numbers in n Dimensions
arXiv:math/0008124v1 [math.CV] 16 Aug 2000
Silviu Olariu
∗பைடு நூலகம்
Institute of Physics and Nuclear Engineering, Department of Fundamental Experimental Physics 76900 Magurele, P.O. Box MG-6, Bucharest, Romania 4 August 2000
1 n ∞ k +pn /(k p=0 y
n−1 l=0 exp {y cos (2πl/n)}cos {y sin (2πl/n)
Abstract Polar commutative n-complex numbers of the form u = x0 + h1 x1 + h2 x2 + · · · + hn−1 xn−1 are introduced in n dimensions, the variables x0 , ..., xn−1 being real numbers. The polar n-complex number can be represented, in an even number of dimensions, by the modulus d, by the amplitude ρ, by 2 polar angles θ+ , θ− , by n/2 − 2 planar angles ψk−1 , and by n/2 − 1 azimuthal angles φk . In an odd number of dimensions, the polar n-complex number can be represented by d, ρ, by 1 polar angle θ+ , by (n − 3)/2 planar angles ψk−1 , and by (n − 1)/2 azimuthal angles φk . The exponential function of a polar n-complex number can be expanded in terms of the polar n-dimensional cosexponential functions gnk (y ), k = 0, 1, ..., n − 1. Expressions are given for these cosexponential functions. The polar n-complex numbers can be written in exponential and trigonometric forms with the aid of the modulus, amplitude and the angular variables. The polar n-complex functions defined by series of powers are analytic, and the partial derivatives of the components of the polar n-complex functions are closely related. The integrals of polar n-complex functions are independent of path in regions where the functions are regular. The fact that the exponential form of
1
Introduction
A regular, two-dimensional complex number x + iy can be represented geometrically by the modulus ρ = (x2 + y 2 )1/2 and by the polar angle θ = arctan(y/x). The modulus ρ is multiplicative and the polar angle θ is additive upon the multiplication of ordinary complex numbers. The quaternions of Hamilton are a system of hypercomplex numbers defined in four dimensions, the multiplication being a noncommutative operation, [1] and many other hypercomplex systems are possible, [2]-[4] but these hypercomplex systems do not have all the required properties of regular, two-dimensional complex numbers which rendered possible the development of the theory of functions of a complex variable. A system of complex numbers in n dimensions is described in this work, for which the multiplication is both associative and commutative, and which is rich enough in properties so that an exponential form exists and the concepts of analytic n-complex function, contour integration and residue can be defined. The n-complex numbers introduced in this work have the form u = x0 + h1 x1 + h2 x2 + · · · + hn−1 xn−1 , the variables x0 , ..., xn−1 being real numbers. The multiplication rules for the complex units h1 , ..., hn−1 are hj hk = hj +k if 0 ≤ j + k ≤ n − 1, and hj hk = hj +k−n if n ≤ j + k ≤ 2n − 2. The product of two n-complex numbers is equal to zero if both numbers are equal to zero, or if the numbers belong to certain n-dimensional hyperplanes described further in this work. If the n-complex number u = x0 + h1 x1 + h2 x2 + · · · + hn−1 xn−1 is represented by the point A of coordinates x0 , x1 , ..., xn−1 , the position of the point A can be described, in an

e-mail: olariu@ifin.nipne.ro
1
a polar n-complex numbers depends on the cyclic variables φk leads to the concept of pole and residue for integrals on closed paths. The polynomials of polar n-complex variables can be written as products of linear or quadratic factors, although the factorization may not be unique.
1/2 , by n/2 − 1 2 2 even number of dimensions, by the modulus d = (x2 0 + x1 + · · · + xn−1 )
azimuthal angles φk , by n/2 − 2 planar angles ψk−1 , and by 2 polar angles θ+ , θ− . In an odd number of dimensions, the position of the point A is described by d, by (n − 1)/2 azimuthal
where v+ = x0 + · · · + xn−1 , v− = x0 − x1 + · · · + xn−2 − xn−1 , and ρk are radii in orthogonal two-dimensional planes defined further in this work. The amplitude ρ, the variables v+ , v− , √ √ the radii ρk , the variables (1/ 2) tan θ+ , (1/ 2) tan θ− , tan ψk−1 are multiplicative, and the azimuthal angles φk are additive upon the multiplication of n-complex numbers. Because of the role of the axis v+ and, in an even number of dimensions, of the axis v− , in the description of the position of the point A with the aid of the polar angle θ+ and, in an even number of dimensions, of the polar angle θ− , the hypercomplex numbers studied in this work will be called polar n-complex number, to distinguish them from the planar n-complex numbers, which exist in an even number of dimensions. [5] The exponential function of an n-complex number can be expanded in terms of the polar n-dimensional cosexponential functions gnk (y ) = 1. It is shown that gnk (y ) =
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