Title Run Time (in mir Ratings (audiend Release Date Creed III 116 95% 03/03/2023 Scream VI 123 91% 03/10/2023 John Wick: Chap 169 94% 03/24/2023 The Super Maric 92 95% 04/05/2023 The Pope's Exor 103 81% 04/14/2023 Guardians of the 150 94% 05/05/2023 The Little Merma 135 94% 05/26/2023 Barbie 114 83% 07/21/2023 Meg 2: The Tren 115 73% 08/04/2023 Paw Patrol: The 92 94% 09/23/2023 The Hunger Gar 157 89% 11/17/2023

Title Run Time (in mir Ratings (audiend Release Date Creed III 116 95% 03/03/2023 Scream VI 123 91% 03/10/2023 John Wick: Chap 169 94% 03/24/2023 The Super Maric 92 95% 04/05/2023 The Pope's Exor 103 81% 04/14/2023 Guardians of the 150 94% 05/05/2023 The Little Merma 135 94% 05/26/2023 Barbie 114 83% 07/21/2023 Meg 2: The Tren 115 73% 08/04/2023 Paw Patrol: The 92 94% 09/23/2023 The Hunger Gar 157 89% 11/17/2023

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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The following equation can be used to compute values of y as a function of x: y=be¯ªsin(bx) (0.0012x¹ −0.15x³ +0.075x² +2.5x) where a and b are parameters. (a) Write Python code to compute a vector y for a = 2, b = 5, and x as a vector with values from 0 to π/2 in increments Ax= π/40. (b) Compute the vector z = y² with element-by-element-wise operations. (c) Combine x, y, and zinto a 3-column matrix W.(Hint use np. hstack function) (d) Generate a plot of y and z versus x. Include a title, axis labels, and a legend. For the y versus x curve, format a solid blue line. For the z versus x curve, choose a magenta dashed line. Include grid lines.
Can you use Python programming language to to this question? Thanks
Follow the instructions in the screenshots provided carefully to implement the code below in python 3.10 or later, please. def pi_monte_carlo(n) : """Computes and returns an estimation of pi using Monte Carlo simulation. Keyword arguments: n - The number of samples. """ pass def pi_parallel_monte_carlo(n, p=4) : """Computes and returns an estimation of pi using a parallel Monte Carlo simulation. Keyword arguments: n - The total number of samples. p - The number of processes to use. """ # You can distribute the work across p # processes by having each process # call the sequential version, where # those calls divide the n samples across # the p calls. # Once those calls return, simply average # the p partial results and return that average. pass def generate_table() : """This function should generate and print a table of results to demonstrate that both versions compute increasingly accurate estimations of pi as…