This document contains C++ code examples using numerical methods like Newton-Raphson, successive approximation, and secant methods to find the real roots of various equations. It includes 6 questions/problems with sample code to find roots of equations like x^4 - 11x = 8, e^x - 3x^2 = 0, x^3 - 2x - 3 = 0, and sin(x) + 3cos(x) = 2. For each problem, the code provided the numerical method used, input/output examples, and calculations to iteratively approximate the root to within a set tolerance.
Numerical Methods with Computer ProgrammingUtsav Patel
This report includes computer programming of some of the basic numerical methods. The programming language used is C++. Outputs of the programs are attached in the form of a screenshot. It can be helpful in the assignments on programming.
Numerical Methods with Computer ProgrammingUtsav Patel
This report includes computer programming of some of the basic numerical methods. The programming language used is C++. Outputs of the programs are attached in the form of a screenshot. It can be helpful in the assignments on programming.
Polynomial Function and Synthetic DivisionAleczQ1414
This file is about Polynomial Function and Synthetic Division. A project passed to Mrs. Marissa De Ocampo. Submitted by Group 6 of Grade 10-Galilei of Caloocan National Science and Technology High School '15-'16
A functional relationship between y and x can be made explicit. Even if the relation is not functional, however, we can assume the relation usually defines y as a function.
How do you calculate the particular integral of linear differential equations?
Learn this and much more by watching this video. Here, we learn how the inverse differential operator is used to find the particular integral of trigonometric, exponential, polynomial and inverse hyperbolic functions. Problems are explained with the relevant formulae.
This is useful for graduate students and engineering students learning Mathematics. For more videos, visit my page
https://www.mathmadeeasy.co/about-4
Subscribe to my channel for more videos.
Polynomial Function and Synthetic DivisionAleczQ1414
This file is about Polynomial Function and Synthetic Division. A project passed to Mrs. Marissa De Ocampo. Submitted by Group 6 of Grade 10-Galilei of Caloocan National Science and Technology High School '15-'16
A functional relationship between y and x can be made explicit. Even if the relation is not functional, however, we can assume the relation usually defines y as a function.
How do you calculate the particular integral of linear differential equations?
Learn this and much more by watching this video. Here, we learn how the inverse differential operator is used to find the particular integral of trigonometric, exponential, polynomial and inverse hyperbolic functions. Problems are explained with the relevant formulae.
This is useful for graduate students and engineering students learning Mathematics. For more videos, visit my page
https://www.mathmadeeasy.co/about-4
Subscribe to my channel for more videos.
Numerical solution of ordinary differential equations by using Runge-Kutta Method of Order Two and Runge-Kutta Method of Order Four
How to write the C++ codes?
C++ is a middle-level programming language developed by Bjarne Stroustrup starting in 1979 at Bell Labs. C++ runs on a variety of platforms, such as Windows, Mac OS, and the various versions of UNIX.
This reference will take you through simple and practical approach while learning C++ Programming language.
Not so long ago Microsoft announced a new language trageting on front-end developers. Everybody's reaction was like: Why?!! Is it just Microsoft darting back to Google?!
So, why a new language? JavaScript has its bad parts. Mostly you can avoid them or workaraund. You can emulate class-based OOP style, modules, scoping and even run-time typing. But that is doomed to be clumsy. That's not in the language design. Google has pointed out these flaws, provided a new language and failed. Will the story of TypeScript be any different?
Observation of Io’s Resurfacing via Plume Deposition Using Ground-based Adapt...Sérgio Sacani
Since volcanic activity was first discovered on Io from Voyager images in 1979, changes
on Io’s surface have been monitored from both spacecraft and ground-based telescopes.
Here, we present the highest spatial resolution images of Io ever obtained from a groundbased telescope. These images, acquired by the SHARK-VIS instrument on the Large
Binocular Telescope, show evidence of a major resurfacing event on Io’s trailing hemisphere. When compared to the most recent spacecraft images, the SHARK-VIS images
show that a plume deposit from a powerful eruption at Pillan Patera has covered part
of the long-lived Pele plume deposit. Although this type of resurfacing event may be common on Io, few have been detected due to the rarity of spacecraft visits and the previously low spatial resolution available from Earth-based telescopes. The SHARK-VIS instrument ushers in a new era of high resolution imaging of Io’s surface using adaptive
optics at visible wavelengths.
Toxic effects of heavy metals : Lead and Arsenicsanjana502982
Heavy metals are naturally occuring metallic chemical elements that have relatively high density, and are toxic at even low concentrations. All toxic metals are termed as heavy metals irrespective of their atomic mass and density, eg. arsenic, lead, mercury, cadmium, thallium, chromium, etc.
DERIVATION OF MODIFIED BERNOULLI EQUATION WITH VISCOUS EFFECTS AND TERMINAL V...Wasswaderrick3
In this book, we use conservation of energy techniques on a fluid element to derive the Modified Bernoulli equation of flow with viscous or friction effects. We derive the general equation of flow/ velocity and then from this we derive the Pouiselle flow equation, the transition flow equation and the turbulent flow equation. In the situations where there are no viscous effects , the equation reduces to the Bernoulli equation. From experimental results, we are able to include other terms in the Bernoulli equation. We also look at cases where pressure gradients exist. We use the Modified Bernoulli equation to derive equations of flow rate for pipes of different cross sectional areas connected together. We also extend our techniques of energy conservation to a sphere falling in a viscous medium under the effect of gravity. We demonstrate Stokes equation of terminal velocity and turbulent flow equation. We look at a way of calculating the time taken for a body to fall in a viscous medium. We also look at the general equation of terminal velocity.
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
Earliest Galaxies in the JADES Origins Field: Luminosity Function and Cosmic ...Sérgio Sacani
We characterize the earliest galaxy population in the JADES Origins Field (JOF), the deepest
imaging field observed with JWST. We make use of the ancillary Hubble optical images (5 filters
spanning 0.4−0.9µm) and novel JWST images with 14 filters spanning 0.8−5µm, including 7 mediumband filters, and reaching total exposure times of up to 46 hours per filter. We combine all our data
at > 2.3µm to construct an ultradeep image, reaching as deep as ≈ 31.4 AB mag in the stack and
30.3-31.0 AB mag (5σ, r = 0.1” circular aperture) in individual filters. We measure photometric
redshifts and use robust selection criteria to identify a sample of eight galaxy candidates at redshifts
z = 11.5 − 15. These objects show compact half-light radii of R1/2 ∼ 50 − 200pc, stellar masses of
M⋆ ∼ 107−108M⊙, and star-formation rates of SFR ∼ 0.1−1 M⊙ yr−1
. Our search finds no candidates
at 15 < z < 20, placing upper limits at these redshifts. We develop a forward modeling approach to
infer the properties of the evolving luminosity function without binning in redshift or luminosity that
marginalizes over the photometric redshift uncertainty of our candidate galaxies and incorporates the
impact of non-detections. We find a z = 12 luminosity function in good agreement with prior results,
and that the luminosity function normalization and UV luminosity density decline by a factor of ∼ 2.5
from z = 12 to z = 14. We discuss the possible implications of our results in the context of theoretical
models for evolution of the dark matter halo mass function.
Deep Behavioral Phenotyping in Systems Neuroscience for Functional Atlasing a...Ana Luísa Pinho
Functional Magnetic Resonance Imaging (fMRI) provides means to characterize brain activations in response to behavior. However, cognitive neuroscience has been limited to group-level effects referring to the performance of specific tasks. To obtain the functional profile of elementary cognitive mechanisms, the combination of brain responses to many tasks is required. Yet, to date, both structural atlases and parcellation-based activations do not fully account for cognitive function and still present several limitations. Further, they do not adapt overall to individual characteristics. In this talk, I will give an account of deep-behavioral phenotyping strategies, namely data-driven methods in large task-fMRI datasets, to optimize functional brain-data collection and improve inference of effects-of-interest related to mental processes. Key to this approach is the employment of fast multi-functional paradigms rich on features that can be well parametrized and, consequently, facilitate the creation of psycho-physiological constructs to be modelled with imaging data. Particular emphasis will be given to music stimuli when studying high-order cognitive mechanisms, due to their ecological nature and quality to enable complex behavior compounded by discrete entities. I will also discuss how deep-behavioral phenotyping and individualized models applied to neuroimaging data can better account for the subject-specific organization of domain-general cognitive systems in the human brain. Finally, the accumulation of functional brain signatures brings the possibility to clarify relationships among tasks and create a univocal link between brain systems and mental functions through: (1) the development of ontologies proposing an organization of cognitive processes; and (2) brain-network taxonomies describing functional specialization. To this end, tools to improve commensurability in cognitive science are necessary, such as public repositories, ontology-based platforms and automated meta-analysis tools. I will thus discuss some brain-atlasing resources currently under development, and their applicability in cognitive as well as clinical neuroscience.
Seminar of U.V. Spectroscopy by SAMIR PANDASAMIR PANDA
Spectroscopy is a branch of science dealing the study of interaction of electromagnetic radiation with matter.
Ultraviolet-visible spectroscopy refers to absorption spectroscopy or reflect spectroscopy in the UV-VIS spectral region.
Ultraviolet-visible spectroscopy is an analytical method that can measure the amount of light received by the analyte.
Remote Sensing and Computational, Evolutionary, Supercomputing, and Intellige...University of Maribor
Slides from talk:
Aleš Zamuda: Remote Sensing and Computational, Evolutionary, Supercomputing, and Intelligent Systems.
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Inter-Society Networking Panel GRSS/MTT-S/CIS Panel Session: Promoting Connection and Cooperation
https://www.etran.rs/2024/en/home-english/
BLOOD AND BLOOD COMPONENT- introduction to blood physiology
C++ TUTORIAL 7
1. TUTORIAL 7 SJEM2231: STRUCTURED PROGRAMMING (C++)
QUESTION 1
Write a C++ program by using Newton-Raphson method to find the
real root near 2 of the equation x4 – 11x= 8
USING DO-WHILE LOOP(FIRST WAY)
#include <iostream>
#include <cmath>
using namespace std;
#define f(x) pow(x,4) - 11*x - 8
#define ff(x) 4*pow(x,3) - 11
void main ()
{
double guess, x1, fguess,ffguess;
cout<<"Enter an initial guess of the solution: ";
cin>> guess;
do
{
fguess= f(guess);
ffguess =ff(guess);
x1= guess - (fguess/ ffguess);
cout << "n" <<guess << "tt" << x1<< "tt" << fguess <<"tt" << ffguess
<< "nn";
guess=x1;
//new approx. becomes previous and it is approximation for the next iteration
4. cout << "nThe root is " << a << endl;
return 0;
}
double func(double x) {return (double)pow(x,4)-11*x-8;}
double diff(double x) {return (double)4*pow(x,3)-11;}
double newton(double x) {return (double)x-(func(x)/diff(x));}
//Output:
NEWTON-RAPHSON METHOD
x^4-11x-8 [near 2]
i x f(x) f'(x)
1 2.00000 -14.00000 21.00000
2 2.66667 13.23457 64.85185
3 2.46259 1.68798 48.73623
4 2.42796 0.04324 46.25104
The root is 2.42702
5. TUTORIAL 7 SJEM2231: STRUCTURED PROGRAMMING (C++)
ALTERNATIVE METHOD FOR QUESTION 1 USING FOR LOOP
#include <iostream>
#include <cmath>
using namespace std;
#define f(x) pow(x,4) - 11*x - 8
#define ff(x) 4*pow(x,3) - 11
void main ()
{
double guess, x1, fguess,ffguess;
cout<<"Enter an initial guess of the solution: ";
cin>> guess;
for(int i=0 ; i<=10; i++)
{
fguess=f(guess);
ffguess =ff(guess);
x1= guess - (fguess/ ffguess);
if (fabs(x1- guess)/guess < 0.00001)
break;
else
guess=x1;
}
cout<<"The root is "<< x1<<endl;
}
6. //Output:
Enter an initial guess of the solution: 24.0911
The root is 2.42704
QUESTION 2
Write a C++ function program to find the root of the equation
f(x)=ex – 3x2 to an accuracy of 5 digits.
USING DO-WHILE LOOP(FIRST WAY)
#include <iostream>
#include <cmath>
using namespace std;
double f( double x)
{
double fx;
fx=exp(x) - 3*pow(x,2);
return fx;
}
double ff(double x)
{
double ffx;
ffx=exp(x) - 6*x;
return ffx;
}
void main ()
{
double guess, x1, fguess,ffguess;
7. TUTORIAL 7 SJEM2231: STRUCTURED PROGRAMMING (C++)
cout<<"Enter an initial guess of the solution: ";
cin>> guess;
do
{
fguess= f(guess);
ffguess =ff(guess);
x1= guess - (fguess/ ffguess);
cout << "n" <<guess << "t" << x1<< "t" << fguess <<"t" << ffguess <<
"nn";
guess=x1;
//new approx becomes previous and it is approximation for the next iteration
} while ( fabs(fguess) > 0.000001);
cout<<"The root is "<< x1<<endl;
}
//Output:
Enter an initial guess of the solution: 1
1 0.914155 -0.281718 -3.28172
0.914155 0.910018 -0.0123726 -2.99026
0.910018 0.910008 -3.00348e-005 -2.97574
0.910008 0.910008 -1.79075e-010 -2.9757
The root is 0.910008
8. USING DO-WHILE LOOP(SECOND WAY)
#include <cmath>
#include <iostream>
#include <iomanip>
using namespace std;
double func(double x);
double diff(double x);
double newton(double x);
int main ()
{
double a=1,b,r;
int iter=1;
cout << "NEWTON-RAPHSON METHOD" << endl;
cout << "e^x-3x^2n" << endl;
cout << "itxttf(x)ttf'(x)" <<endl;
do
{
r=newton(a);
cout << iter << "t"<< setprecision(5) << fixed << a << "tt" << func(a) << "t"
<< diff(a) << endl;
b=a;
a=r;
iter++;
} while (fabs((a-b)/b) > 0.001);
9. TUTORIAL 7 SJEM2231: STRUCTURED PROGRAMMING (C++)
cout << "nThe root is " << a << endl;
return 0;
}
double func(double x) {return (double)exp(x)-3*pow(x,2);}
double diff(double x) {return (double)exp(x)-6*x;}
double newton(double x) {return (double)x-(func(x)/diff(x));}
//Output:
NEWTON-RAPHSON METHOD
e^x-3x^2
i x f(x) f'(x)
1 1.00000 -0.28172 -3.28172
2 0.91416 -0.01237 -2.99026
3 0.91002 -0.00003 -2.97574
The root is 0.91001
QUESTION 3
Write a C++ Function program to find the smallest positive root of
the equation x3– 2x – 3 =0. using Successive Approximation Method
#include <cmath>
#include <iostream>
#include <iomanip>