Maths Investigation Ideas for A-Level, IB and Gifted GCSE Students
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Engineering Curves – I
Engineering Curves – I 1. Classification 2. Conic sections - explanation 3. Common Definition 4. Ellipse – ( six methods of construction) 5. Parabola – ( Three methods of construction) 6. Hyperbola – ( Three methods of construction ) 7. Methods of drawing Tangents & Normals ( four cases) Engineering Curves – II 1. Classification 2. Definitions 3. Involutes - (five cases) 4. Cycloid 5. Trochoids – (Superior and Inferior) 6. Epic cycloid and Hypo - cycloid 7. Spiral (Two cases) 8. Helix – on cylinder & on cone 9. Methods of drawing Tangents and Normals (Three cases) ENGINEERING CURVES Part- I {Conic Sections} ELLIPSE PARABOLA HYPERBOLA 1.Concentric Circle Method 1.Rectangle Method 1.Rectangular Hyperbola (coordinates given) 2.Rectangle Method 2 Method of Tangents ( Triangle Method) 2 Rectangular Hyperbola 3.Oblong Method (P-V diagram - Equation given) 3.Basic Locus Method 4.Arcs of Circle Method (Directrix – focus) 3.Basic Locus Method (Directrix – focus) 5.Rhombus Metho 6.Basic Locus Method Methods of Drawing (Directrix – focus) Tangents & Normals To These Curves. CONIC SECTIONS ELLIPSE, PARABOLA AND HYPERBOLA ARE CALLED CONIC SECTIONS BECAUSE THESE CURVES APPEAR ON THE SURFACE OF A CONE WHEN IT IS CUT BY SOME TYPICAL CUTTING PLANES. OBSERVE ILLUSTRATIONS GIVEN BELOW.. Ellipse Section Plane Section Plane Hyperbola Through Generators Parallel to Axis. Section Plane Parallel to end generator. COMMON DEFINATION OF ELLIPSE, PARABOLA & HYPERBOLA: These are the loci of points moving in a plane such that the ratio of it’s distances from a fixed point And a fixed line always remains constant. The Ratio is called ECCENTRICITY. (E) A) For Ellipse E<1 B) For Parabola E=1 C) For Hyperbola E>1 Refer Problem nos. -
Art and Engineering Inspired by Swarm Robotics
RICE UNIVERSITY Art and Engineering Inspired by Swarm Robotics by Yu Zhou A Thesis Submitted in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy Approved, Thesis Committee: Ronald Goldman, Chair Professor of Computer Science Joe Warren Professor of Computer Science Marcia O'Malley Professor of Mechanical Engineering Houston, Texas April, 2017 ABSTRACT Art and Engineering Inspired by Swarm Robotics by Yu Zhou Swarm robotics has the potential to combine the power of the hive with the sen- sibility of the individual to solve non-traditional problems in mechanical, industrial, and architectural engineering and to develop exquisite art beyond the ken of most contemporary painters, sculptors, and architects. The goal of this thesis is to apply swarm robotics to the sublime and the quotidian to achieve this synergy between art and engineering. The potential applications of collective behaviors, manipulation, and self-assembly are quite extensive. We will concentrate our research on three topics: fractals, stabil- ity analysis, and building an enhanced multi-robot simulator. Self-assembly of swarm robots into fractal shapes can be used both for artistic purposes (fractal sculptures) and in engineering applications (fractal antennas). Stability analysis studies whether distributed swarm algorithms are stable and robust either to sensing or to numerical errors, and tries to provide solutions to avoid unstable robot configurations. Our enhanced multi-robot simulator supports this research by providing real-time simula- tions with customized parameters, and can become as well a platform for educating a new generation of artists and engineers. The goal of this thesis is to use techniques inspired by swarm robotics to develop a computational framework accessible to and suitable for both artists and engineers. -
The Birthday Problem (2.7)
Combinatorics (2.6) The Birthday Problem (2.7) Prof. Tesler Math 186 Winter 2020 Prof. Tesler Combinatorics & Birthday Problem Math 186 / Winter 2020 1 / 29 Multiplication rule Combinatorics is a branch of Mathematics that deals with systematic methods of counting things. Example How many outcomes (x, y, z) are possible, where x = roll of a 6-sided die; y = value of a coin flip; z = card drawn from a 52 card deck? (6 choices of x) × (2 choices of y) × (52 choices of z) = 624 Multiplication rule The number of sequences (x1, x2,..., xk) where there are n1 choices of x1, n2 choices of x2,..., nk choices of xk is n1 · n2 ··· nk. This assumes the number of choices of xi is a constant ni that doesn’t depend on the other choices. Prof. Tesler Combinatorics & Birthday Problem Math 186 / Winter 2020 2 / 29 Addition rule Months and days How many pairs (m, d) are there where m = month 1,..., 12; d = day of the month? Assume it’s not a leap year. 12 choices of m, but the number of choices of d depends on m (and if it’s a leap year), so the total is not “12 × ” Split dates into Am = f (m, d): d is a valid day in month m g: A = A1 [···[ A12 = whole year jAj = jA1j + ··· + jA12j = 31 + 28 + ··· + 31 = 365 Addition rule If A ,..., A are mutually exclusive, then 1 n n n [ Ai = jAij i=1 i=1 X Prof. Tesler Combinatorics & Birthday Problem Math 186 / Winter 2020 3 / 29 Permutations of distinct objects Here are all the permutations of A, B, C: ABC ACB BAC BCA CAB CBA There are 3 items: A, B, C. -
A Note on the Exponentiation Approximation of the Birthday Paradox
A note on the exponentiation approximation of the birthday paradox Kaiji Motegi∗ and Sejun Wooy Kobe University Kobe University July 3, 2021 Abstract In this note, we shed new light on the exponentiation approximation of the probability that all K individuals have distinct birthdays across N calendar days. The exponen- tiation approximation imposes a pairwise independence assumption, which does not hold in general. We sidestep it by deriving the conditional probability for each pair of individuals to have distinct birthdays given that previous pairs do. An interesting im- plication is that the conditional probability decreases in a step-function form |not in a strictly monotonical form| as the more pairs are restricted to have distinct birthdays. The source of the step-function structure is identified and illustrated. The equivalence between the proposed pairwise approach and the existing permutation approach is also established. MSC 2010 Classification Codes: 97K50, 60-01, 65C50. Keywords: birthday problem, pairwise approach, permutation approach, probability. ∗Corresponding author. Graduate School of Economics, Kobe University. 2-1 Rokkodai-cho, Nada, Kobe, Hyogo 657-8501 Japan. E-mail: [email protected] yDepartment of Economics, Kobe University. 2-1 Rokkodai-cho, Nada, Kobe, Hyogo 657-8501 Japan. E-mail: [email protected] 1 1 Introduction Suppose that each of K individuals has his/her birthday on one of N calendar days consid- ered. Let K¯ = f1;:::;Kg be the set of individuals, and let N¯ = f1;:::;Ng be the set of ¯ calendar days, where 2 ≤ K ≤ N. Let Ak;n be an event that individual k 2 K has his/her birthday on day n 2 N¯ . -
Probability Theory
Probability Theory Course Notes — Harvard University — 2011 C. McMullen March 29, 2021 Contents I TheSampleSpace ........................ 2 II Elements of Combinatorial Analysis . 5 III RandomWalks .......................... 15 IV CombinationsofEvents . 24 V ConditionalProbability . 29 VI The Binomial and Poisson Distributions . 37 VII NormalApproximation. 44 VIII Unlimited Sequences of Bernoulli Trials . 55 IX Random Variables and Expectation . 60 X LawofLargeNumbers...................... 68 XI Integral–Valued Variables. Generating Functions . 70 XIV RandomWalkandRuinProblems . 70 I The Exponential and the Uniform Density . 75 II Special Densities. Randomization . 94 These course notes accompany Feller, An Introduction to Probability Theory and Its Applications, Wiley, 1950. I The Sample Space Some sources and uses of randomness, and philosophical conundrums. 1. Flipped coin. 2. The interrupted game of chance (Fermat). 3. The last roll of the game in backgammon (splitting the stakes at Monte Carlo). 4. Large numbers: elections, gases, lottery. 5. True randomness? Quantum theory. 6. Randomness as a model (in reality only one thing happens). Paradox: what if a coin keeps coming up heads? 7. Statistics: testing a drug. When is an event good evidence rather than a random artifact? 8. Significance: among 1000 coins, if one comes up heads 10 times in a row, is it likely to be a 2-headed coin? Applications to economics, investment and hiring. 9. Randomness as a tool: graph theory; scheduling; internet routing. We begin with some previews. Coin flips. What are the chances of 10 heads in a row? The probability is 1/1024, less than 0.1%. Implicit assumptions: no biases and independence. 10 What are the chance of heads 5 out of ten times? ( 5 = 252, so 252/1024 = 25%). -
A Non-Uniform Birthday Problem with Applications to Discrete Logarithms
A NON-UNIFORM BIRTHDAY PROBLEM WITH APPLICATIONS TO DISCRETE LOGARITHMS STEVEN D. GALBRAITH AND MARK HOLMES Abstract. We consider a generalisation of the birthday problem that arises in the analysis of algorithms for certain variants of the discrete logarithm problem in groups. More precisely, we consider sampling coloured balls and placing them in urns, such that the distribution of assigning balls to urns depends on the colour of the ball. We determine the expected number of trials until two balls of different colours are placed in the same urn. As an aside we present an amusing “paradox” about birthdays. Keywords: birthday paradox, discrete logarithm problem (DLP), probabilistic analysis of randomised algorithms 1. Introduction In the classical birthday problem one samples uniformly with replacement from a set of size N until the same value is sampled twice. It is known that the expected time at which this match first occurs grows as πN/2. The word “birthday” arises from a common application of this result: the expected value of the minimum number of people in a room before two of them have the same birthday is approximately 23.94 (assuming birthdays are uniformly distributed over the year). The birthday problem can be generalised in a number of ways. For example, the assumption that births are uniformly distributed over the days in the year is often false. Hence, researchers have studied the expected time until a match occurs for general distributions. One can also generalise the problem to multi-collisions (e.g., 3 people having the same birthday) or coincidences among individuals of different “types” (e.g., in a room with equal numbers of boys and girls, when can one expect a boy and girl to share the same birthday). -
The Matching, Birthday and the Strong Birthday Problem
Journal of Statistical Planning and Inference 130 (2005) 377–389 www.elsevier.com/locate/jspi The matching, birthday and the strong birthday problem: a contemporary review Anirban DasGupta Department of Statistics, Purdue University, 1399 Mathematical Science Building, West Lafayette, IN 47907, USA Received 7 October 2003; received in revised form 1 November 2003; accepted 10 November 2003 Dedicated to Herman Chernoff with appreciation and affection on his 80th birthday Available online 29 July 2004 Abstract This article provides a contemporary exposition at a moderately quantitative level of the distribution theory associated with the matching and the birthday problems. A large number of examples, many not well known, are provided to help a reader have a feeling for these questions at an intuitive level. © 2004 Elsevier B.V. All rights reserved. Keywords: Birthday problem; Coincidences; Matching problem; Poisson; Random permutation; Strong birthday problem 1. Introduction My first exposure to Professor Chernoff’s work was in an asymptotic theory class at the ISI. Later I had the opportunity to read and teach a spectrum of his work on design of experiments, goodness of fit, multivariate analysis and variance inequalities. My own modest work on look-alikes in Section 2.8 here was largely influenced by the now famous Chernoff faces. It is a distinct pleasure to write this article for the special issue in his honor. This article provides an exposition of some of the major questions related to the matching and the birthday problems. The article is partially historical, and partially forward looking. For example, we address a new problem that we call the strong birthday problem. -
Approximations to the Birthday Problem with Unequal Occurrence Probabilities and Their Application to the Surname Problem in Japan*
Ann. Inst. Statist. Math. Vol. 44, No. 3, 479-499 (1992) APPROXIMATIONS TO THE BIRTHDAY PROBLEM WITH UNEQUAL OCCURRENCE PROBABILITIES AND THEIR APPLICATION TO THE SURNAME PROBLEM IN JAPAN* SHIGERU MASE Faculty of Integrated Arts and Sciences, Hiroshima University, Naka-ku, Hiroshima 730, Japan (Received July 4, 1990; revised September 2, 1991) Abstract. Let X1,X2,...,X~ be iid random variables with a discrete dis- tribution {Pi}~=l. We will discuss the coincidence probability Rn, i.e., the probability that there are members of {Xi} having the same value. If m = 365 and p~ ~ 1/365, this is the famous birthday problem. Also we will give two kinds of approximation to this probability. Finally we will give two applica- tions. The first is the estimation of the coincidence probability of surnames in Japan. For this purpose, we will fit a generalized zeta distribution to a fre- quency data of surnames in Japan. The second is the true birthday problem, that is, we will evaluate the birthday probability in Japan using the actual (non-uniform) distribution of birthdays in Japan. Key words and phrases: Birthday problem, coincidence probability, non-uni- formness, Bell polynomial, approximation, surname. I. Introduction It is frequently observed that, even within a small group, there are people with the same surname. It may not be considered so curious to find persons with the same surname in a group compared to those with the same birthday. But if we consider the variety of surnames and the relatively small portions of each surname in some countries, this fact becomes less trivial than is first seen. -
A Dynamic Rod Model to Simulate Mechanics of Cables and Dna
A DYNAMIC ROD MODEL TO SIMULATE MECHANICS OF CABLES AND DNA by Sachin Goyal A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy (Mechanical Engineering and Scientific Computing) in The University of Michigan 2006 Doctoral Committee: Professor Noel Perkins, Chair Associate Professor Edgar Meyhofer, Co-Chair Assistant Professor Ioan Andricioaei Assistant Professor Krishnakumar Garikipati Assistant Professor Jens-Christian Meiners Dedication To my parents ii Acknowledgements I am extremely grateful to my advisor Prof. Noel Perkins for his highly conducive and motivating research guidance and overall mentoring. I sincerely appreciate many engaging discussions of DNA mechanics and supercoiling with Prof. E. Meyhofer and Prof. K. Garikipati in Mechanical Engineering Program, Prof. C. Meiners in the Biophysics Program, Prof. I. Andricioaei in the Biochemistry/ Biophysics Program and and Dr. S. Blumberg in Medical school at the University of Michigan and Dr. C. L. Lee at the Lawrence Livermore National Laboratory, California. I also appreciate Professor Jason D. Kahn (Department of Chemistry and Biochemistry, University of Maryland) for providing the sequences used in his experiments, and Professor Wilma K. Olson (Department of Chemistry and Chemical Biology, Rutgers University) for suggesting alternative binding topologies in DNA-protein complexes. I also acknowledge recent graduate students T. Lillian in Mechanical Engineering Program, D. Wilson in Physics Program and J. Wereszczynski in Chemistry Program to extend my work and collaborate on ongoing studies beyond the scope of dissertation. I also gratefully acknowledge the research support provided by the U. S. Office of Naval Research, Lawrence Livermore National Laboratories, and the National Science Foundation. -
A FIRST COURSE in PROBABILITY This Page Intentionally Left Blank a FIRST COURSE in PROBABILITY
A FIRST COURSE IN PROBABILITY This page intentionally left blank A FIRST COURSE IN PROBABILITY Eighth Edition Sheldon Ross University of Southern California Upper Saddle River, New Jersey 07458 Library of Congress Cataloging-in-Publication Data Ross, Sheldon M. A first course in probability / Sheldon Ross. — 8th ed. p. cm. Includes bibliographical references and index. ISBN-13: 978-0-13-603313-4 ISBN-10: 0-13-603313-X 1. Probabilities—Textbooks. I. Title. QA273.R83 2010 519.2—dc22 2008033720 Editor in Chief, Mathematics and Statistics: Deirdre Lynch Senior Project Editor: Rachel S. Reeve Assistant Editor: Christina Lepre Editorial Assistant: Dana Jones Project Manager: Robert S. Merenoff Associate Managing Editor: Bayani Mendoza de Leon Senior Managing Editor: Linda Mihatov Behrens Senior Operations Supervisor: Diane Peirano Marketing Assistant: Kathleen DeChavez Creative Director: Jayne Conte Art Director/Designer: Bruce Kenselaar AV Project Manager: Thomas Benfatti Compositor: Integra Software Services Pvt. Ltd, Pondicherry, India Cover Image Credit: Getty Images, Inc. © 2010, 2006, 2002, 1998, 1994, 1988, 1984, 1976 by Pearson Education, Inc., Pearson Prentice Hall Pearson Education, Inc. Upper Saddle River, NJ 07458 All rights reserved. No part of this book may be reproduced, in any form or by any means, without permission in writing from the publisher. Pearson Prentice Hall™ is a trademark of Pearson Education, Inc. Printed in the United States of America 10987654321 ISBN-13: 978-0-13-603313-4 ISBN-10: 0-13-603313-X Pearson Education, Ltd., London Pearson Education Australia PTY. Limited, Sydney Pearson Education Singapore, Pte. Ltd Pearson Education North Asia Ltd, Hong Kong Pearson Education Canada, Ltd., Toronto Pearson Educacion´ de Mexico, S.A. -
A (Probably) Exact Solution to the Birthday Problem
A (Probably) Exact Solution to the Birthday Problem David Brink July 2011 Abstract. Given a year with n ≥ 1 days, the Birthday Problem asks for the minimal number X (n) such that in a class of X (n) students, the probability of finding two students with the same birthday is at least 50 percent. We derive heuristically an exact formula for X (n) and argue that the probability that a counter-example to this formula exists is less than one in 45 billion. We then give a new derivation of the asymptotic expansion of Ramanujan's Q-function and note its curious resemblance to the formula for X (n). 1 Introduction It is a surprising fact, apparently first noticed by Davenport around 1927, that in a class of 23 students, the probability of finding two students with the same birthday is more than 50 percent. This observation, and the multitude of related problems that it raises, have been discussed by many distinguished authors, including von Mises [19], Gamow [9, pp. 204{206], Littlewood [17, p. 18], Halmos [11, pp. 103{104], and Davenport [6, pp. 174{175]. The interested reader is referred to [22] for a historical account and to [12] to experience the problem experimentally. Given a year with n ≥ 1 days, the variant of the Birthday Problem studied here asks for the min- imal number X (n) such that in a class of X (n) students, the probability of a birthday coincidence is at least 50 percent. In other words, X (n) is the minimal integer x such that 1 2 x − 1 P (x) := 1 − 1 − ··· 1 − (1) n n n 1 is less than or equal to 2 . -
Physical Geometry
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by ScholarWorks@UMass Amherst University of Massachusetts Amherst ScholarWorks@UMass Amherst Doctoral Dissertations Dissertations and Theses July 2016 Physical Geometry James P. Binkoski University of Massachusetts Amherst Follow this and additional works at: https://scholarworks.umass.edu/dissertations_2 Part of the Metaphysics Commons, and the Philosophy of Science Commons Recommended Citation Binkoski, James P., "Physical Geometry" (2016). Doctoral Dissertations. 625. https://scholarworks.umass.edu/dissertations_2/625 This Open Access Dissertation is brought to you for free and open access by the Dissertations and Theses at ScholarWorks@UMass Amherst. It has been accepted for inclusion in Doctoral Dissertations by an authorized administrator of ScholarWorks@UMass Amherst. For more information, please contact [email protected]. PHYSICAL GEOMETRY A Dissertation Presented by JAMES BINKOSKI Submitted to the Graduate School of the University of Massachusetts Amherst in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY May 2016 Philosophy c Copyright by James Binkoski 2016 All Rights Reserved PHYSICAL GEOMETRY A Dissertation Presented by JAMES BINKOSKI Approved as to style and content by: Bradford Skow, Chair Phillip Bricker, Member Christopher Meacham, Member Lorenzo Sorbo, Member Joseph Levine, Department Chair Philosophy DEDICATION To Jen. ACKNOWLEDGMENTS Most of what I know about how to do philosophy well, I learned from Phil Bricker, Chris Meacham, and Brad Skow. I thank them for their guidance and support over a period of many years. I consider myself incredibly fortunate to have studied with such top-notch philosophers. I am especially grateful to Brad Skow for agreeing to work with me after his move from UMass to MIT in 2007.