The document discusses various types of integrals and rules for finding antiderivatives. It defines definite and indefinite integrals. It then lists and explains the main antiderivative rules for powers, chain rule, product rule, quotient rule, scalar multiples, sums and differences, trigonometric functions, and inverse trigonometric functions. Examples are provided to illustrate each rule.
First principle, power rule, derivative of constant term, product rule, quotient rule, chain rule, derivatives of trigonometric functions and their inverses, derivatives of exponential functions and natural logarithmic functions, implicit differentiation, parametric differentiation, L'Hopital's rule
First principle, power rule, derivative of constant term, product rule, quotient rule, chain rule, derivatives of trigonometric functions and their inverses, derivatives of exponential functions and natural logarithmic functions, implicit differentiation, parametric differentiation, L'Hopital's rule
Integration is a part of Calculus.
This is just a short presentation on Integration.
It may help you out to complete your academic presentation.
Thank You
In this video we talk about the LIATE rule, this is the secret for choosing u and v' correctly. Then we learn another useful trick for integration by parts.
Watch video: http://www.youtube.com/edit?ns=1&video_id=1SEUGvHTcQ0
For more lessons: http://www.intuitive-calculus.com/integration-by-parts.html
An application of the hyperfunction theory to numerical integrationHidenoriOgata
The slide of a speech in the conference "ECMI2016" (The 19th European Conference on Mathematics for Industry) held at Santiago de Compostela, Spain in June 2016.
Integral Calculus. - Differential Calculus - Integration as an Inverse Process of Differentiation - Methods of Integration - Integration using trigonometric identities - Integrals of Some Particular Functions - rational function - partial fraction - Integration by partial fractions - standard integrals - First and second fundamental theorem of integral calculus
The increased availability of biomedical data, particularly in the public domain, offers the opportunity to better understand human health and to develop effective therapeutics for a wide range of unmet medical needs. However, data scientists remain stymied by the fact that data remain hard to find and to productively reuse because data and their metadata i) are wholly inaccessible, ii) are in non-standard or incompatible representations, iii) do not conform to community standards, and iv) have unclear or highly restricted terms and conditions that preclude legitimate reuse. These limitations require a rethink on data can be made machine and AI-ready - the key motivation behind the FAIR Guiding Principles. Concurrently, while recent efforts have explored the use of deep learning to fuse disparate data into predictive models for a wide range of biomedical applications, these models often fail even when the correct answer is already known, and fail to explain individual predictions in terms that data scientists can appreciate. These limitations suggest that new methods to produce practical artificial intelligence are still needed.
In this talk, I will discuss our work in (1) building an integrative knowledge infrastructure to prepare FAIR and "AI-ready" data and services along with (2) neurosymbolic AI methods to improve the quality of predictions and to generate plausible explanations. Attention is given to standards, platforms, and methods to wrangle knowledge into simple, but effective semantic and latent representations, and to make these available into standards-compliant and discoverable interfaces that can be used in model building, validation, and explanation. Our work, and those of others in the field, creates a baseline for building trustworthy and easy to deploy AI models in biomedicine.
Bio
Dr. Michel Dumontier is the Distinguished Professor of Data Science at Maastricht University, founder and executive director of the Institute of Data Science, and co-founder of the FAIR (Findable, Accessible, Interoperable and Reusable) data principles. His research explores socio-technological approaches for responsible discovery science, which includes collaborative multi-modal knowledge graphs, privacy-preserving distributed data mining, and AI methods for drug discovery and personalized medicine. His work is supported through the Dutch National Research Agenda, the Netherlands Organisation for Scientific Research, Horizon Europe, the European Open Science Cloud, the US National Institutes of Health, and a Marie-Curie Innovative Training Network. He is the editor-in-chief for the journal Data Science and is internationally recognized for his contributions in bioinformatics, biomedical informatics, and semantic technologies including ontologies and linked data.
Multi-source connectivity as the driver of solar wind variability in the heli...Sérgio Sacani
The ambient solar wind that flls the heliosphere originates from multiple
sources in the solar corona and is highly structured. It is often described
as high-speed, relatively homogeneous, plasma streams from coronal
holes and slow-speed, highly variable, streams whose source regions are
under debate. A key goal of ESA/NASA’s Solar Orbiter mission is to identify
solar wind sources and understand what drives the complexity seen in the
heliosphere. By combining magnetic feld modelling and spectroscopic
techniques with high-resolution observations and measurements, we show
that the solar wind variability detected in situ by Solar Orbiter in March
2022 is driven by spatio-temporal changes in the magnetic connectivity to
multiple sources in the solar atmosphere. The magnetic feld footpoints
connected to the spacecraft moved from the boundaries of a coronal hole
to one active region (12961) and then across to another region (12957). This
is refected in the in situ measurements, which show the transition from fast
to highly Alfvénic then to slow solar wind that is disrupted by the arrival of
a coronal mass ejection. Our results describe solar wind variability at 0.5 au
but are applicable to near-Earth observatories.
Integration is a part of Calculus.
This is just a short presentation on Integration.
It may help you out to complete your academic presentation.
Thank You
In this video we talk about the LIATE rule, this is the secret for choosing u and v' correctly. Then we learn another useful trick for integration by parts.
Watch video: http://www.youtube.com/edit?ns=1&video_id=1SEUGvHTcQ0
For more lessons: http://www.intuitive-calculus.com/integration-by-parts.html
An application of the hyperfunction theory to numerical integrationHidenoriOgata
The slide of a speech in the conference "ECMI2016" (The 19th European Conference on Mathematics for Industry) held at Santiago de Compostela, Spain in June 2016.
Integral Calculus. - Differential Calculus - Integration as an Inverse Process of Differentiation - Methods of Integration - Integration using trigonometric identities - Integrals of Some Particular Functions - rational function - partial fraction - Integration by partial fractions - standard integrals - First and second fundamental theorem of integral calculus
The increased availability of biomedical data, particularly in the public domain, offers the opportunity to better understand human health and to develop effective therapeutics for a wide range of unmet medical needs. However, data scientists remain stymied by the fact that data remain hard to find and to productively reuse because data and their metadata i) are wholly inaccessible, ii) are in non-standard or incompatible representations, iii) do not conform to community standards, and iv) have unclear or highly restricted terms and conditions that preclude legitimate reuse. These limitations require a rethink on data can be made machine and AI-ready - the key motivation behind the FAIR Guiding Principles. Concurrently, while recent efforts have explored the use of deep learning to fuse disparate data into predictive models for a wide range of biomedical applications, these models often fail even when the correct answer is already known, and fail to explain individual predictions in terms that data scientists can appreciate. These limitations suggest that new methods to produce practical artificial intelligence are still needed.
In this talk, I will discuss our work in (1) building an integrative knowledge infrastructure to prepare FAIR and "AI-ready" data and services along with (2) neurosymbolic AI methods to improve the quality of predictions and to generate plausible explanations. Attention is given to standards, platforms, and methods to wrangle knowledge into simple, but effective semantic and latent representations, and to make these available into standards-compliant and discoverable interfaces that can be used in model building, validation, and explanation. Our work, and those of others in the field, creates a baseline for building trustworthy and easy to deploy AI models in biomedicine.
Bio
Dr. Michel Dumontier is the Distinguished Professor of Data Science at Maastricht University, founder and executive director of the Institute of Data Science, and co-founder of the FAIR (Findable, Accessible, Interoperable and Reusable) data principles. His research explores socio-technological approaches for responsible discovery science, which includes collaborative multi-modal knowledge graphs, privacy-preserving distributed data mining, and AI methods for drug discovery and personalized medicine. His work is supported through the Dutch National Research Agenda, the Netherlands Organisation for Scientific Research, Horizon Europe, the European Open Science Cloud, the US National Institutes of Health, and a Marie-Curie Innovative Training Network. He is the editor-in-chief for the journal Data Science and is internationally recognized for his contributions in bioinformatics, biomedical informatics, and semantic technologies including ontologies and linked data.
Multi-source connectivity as the driver of solar wind variability in the heli...Sérgio Sacani
The ambient solar wind that flls the heliosphere originates from multiple
sources in the solar corona and is highly structured. It is often described
as high-speed, relatively homogeneous, plasma streams from coronal
holes and slow-speed, highly variable, streams whose source regions are
under debate. A key goal of ESA/NASA’s Solar Orbiter mission is to identify
solar wind sources and understand what drives the complexity seen in the
heliosphere. By combining magnetic feld modelling and spectroscopic
techniques with high-resolution observations and measurements, we show
that the solar wind variability detected in situ by Solar Orbiter in March
2022 is driven by spatio-temporal changes in the magnetic connectivity to
multiple sources in the solar atmosphere. The magnetic feld footpoints
connected to the spacecraft moved from the boundaries of a coronal hole
to one active region (12961) and then across to another region (12957). This
is refected in the in situ measurements, which show the transition from fast
to highly Alfvénic then to slow solar wind that is disrupted by the arrival of
a coronal mass ejection. Our results describe solar wind variability at 0.5 au
but are applicable to near-Earth observatories.
Introduction:
RNA interference (RNAi) or Post-Transcriptional Gene Silencing (PTGS) is an important biological process for modulating eukaryotic gene expression.
It is highly conserved process of posttranscriptional gene silencing by which double stranded RNA (dsRNA) causes sequence-specific degradation of mRNA sequences.
dsRNA-induced gene silencing (RNAi) is reported in a wide range of eukaryotes ranging from worms, insects, mammals and plants.
This process mediates resistance to both endogenous parasitic and exogenous pathogenic nucleic acids, and regulates the expression of protein-coding genes.
What are small ncRNAs?
micro RNA (miRNA)
short interfering RNA (siRNA)
Properties of small non-coding RNA:
Involved in silencing mRNA transcripts.
Called “small” because they are usually only about 21-24 nucleotides long.
Synthesized by first cutting up longer precursor sequences (like the 61nt one that Lee discovered).
Silence an mRNA by base pairing with some sequence on the mRNA.
Discovery of siRNA?
The first small RNA:
In 1993 Rosalind Lee (Victor Ambros lab) was studying a non- coding gene in C. elegans, lin-4, that was involved in silencing of another gene, lin-14, at the appropriate time in the
development of the worm C. elegans.
Two small transcripts of lin-4 (22nt and 61nt) were found to be complementary to a sequence in the 3' UTR of lin-14.
Because lin-4 encoded no protein, she deduced that it must be these transcripts that are causing the silencing by RNA-RNA interactions.
Types of RNAi ( non coding RNA)
MiRNA
Length (23-25 nt)
Trans acting
Binds with target MRNA in mismatch
Translation inhibition
Si RNA
Length 21 nt.
Cis acting
Bind with target Mrna in perfect complementary sequence
Piwi-RNA
Length ; 25 to 36 nt.
Expressed in Germ Cells
Regulates trnasposomes activity
MECHANISM OF RNAI:
First the double-stranded RNA teams up with a protein complex named Dicer, which cuts the long RNA into short pieces.
Then another protein complex called RISC (RNA-induced silencing complex) discards one of the two RNA strands.
The RISC-docked, single-stranded RNA then pairs with the homologous mRNA and destroys it.
THE RISC COMPLEX:
RISC is large(>500kD) RNA multi- protein Binding complex which triggers MRNA degradation in response to MRNA
Unwinding of double stranded Si RNA by ATP independent Helicase
Active component of RISC is Ago proteins( ENDONUCLEASE) which cleave target MRNA.
DICER: endonuclease (RNase Family III)
Argonaute: Central Component of the RNA-Induced Silencing Complex (RISC)
One strand of the dsRNA produced by Dicer is retained in the RISC complex in association with Argonaute
ARGONAUTE PROTEIN :
1.PAZ(PIWI/Argonaute/ Zwille)- Recognition of target MRNA
2.PIWI (p-element induced wimpy Testis)- breaks Phosphodiester bond of mRNA.)RNAse H activity.
MiRNA:
The Double-stranded RNAs are naturally produced in eukaryotic cells during development, and they have a key role in regulating gene expression .
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.
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.
Richard's entangled aventures in wonderlandRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
This pdf is about the Schizophrenia.
For more details visit on YouTube; @SELF-EXPLANATORY;
https://www.youtube.com/channel/UCAiarMZDNhe1A3Rnpr_WkzA/videos
Thanks...!
Richard's aventures in two entangled wonderlandsRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
2. Integration
Integration is the calculation of an integral. Integrals in maths are used
to find many useful quantities such as areas, volumes, displacement, etc.
When we speak about integrals, it is related to usually definite integrals.
The indefinite integrals are used for antiderivatives. The integration
denotes the summation of discrete data. The integral is calculated to find
the functions which will describe the area, displacement, volume, that
occurs due to a collection of small data, which cannot be measured
singularly.
3. Definite Integral
An integral that contains the upper and
lower limits then it is a definite
integral. On a real line, x is restricted
to lie. Riemann Integral is the other
name of the Definite Integral.
A definite Integral is represented as:
Indefinite Integral
Indefinite integrals are defined
without upper and lower limits. It is
represented as:
∫f(x)dx = F(x) + C
Where C is any constant and the
function f(x) is called the integrand.
5. List of Anti-Derivative Rule
The list of most commonly used antiderivative rules for the product,
quotient, sum, difference, and the composition of functions is as
follows:
● Antiderivative Power Rule
● Antiderivative Chain Rule
● Antiderivative Product Rule
● Antiderivative Quotient Rule
● Antiderivative Rule for Scalar Multiple of Function
● Antiderivative Rule for Sum and Difference of Functions
6. 1. Anti-Derivative Power Rule
The antiderivative rule of power of x is given by ∫xn dx = xn+1/(n +
1) + C, where n ≠ -1. This rule is commonly known as the
antiderivative power rule.
● ∫x2 dx = x2+1/(2+1) + C = x3/3 + C
● ∫x-4 dx = x-4+1/(-4+1) + C = x-3/(-3) + C = -x-3/3 + C
7. 2. Anti-Derivative Chain Rule
The chain rule of derivatives gives us the antiderivative chain rule which is
also known as the u-substitution method of antidifferentiation. The
antiderivative chain rule is used if the integral is of the form ∫u'(x) f(u(x))
dx.
Solve ∫2x cos (x2) dx
Solution: Assume x2 = u ⇒ 2x dx = du. Substitute this into the integral,we
have
∫2x cos (x2) dx = ∫cos u du
= sin u + C
= sin (x2) + C
8. 3. Anti-Derivative Product Rule
It is one of the important antiderivativerulesand is used when the antidifferentiation of the product of
functionsis to be determined. The formula for theantiderivativeproduct rule is ∫f(x).g(x) dx = f(x) ∫g(x) dx
− ∫(f′(x) [ ∫g(x) dx)]dx +C.
This method is also commonly known as the ILATE or LIATE method of integration which is abbreviated of:
● I - Inverse Trigonometric Function
● L - LogarithmicFunction
● A - AlgebraicFunction
● T - Trigonometric Function
● E - Exponential Function
∫x ln x dx = ln x ∫x dx - ∫[(ln x)' ∫x dx] dx
= (x2/2) ln x - ∫(1/x)(x2/2) dx
= (x2/2) ln x - ∫(x/2) dx
= (x2/2) ln x - x2/4 + C
9. 4. Anti-Derivative Quotient Rule
The antiderivativequotient rule is used when the function is given in the form of numerator
and denominator. If the function includes algebraic functions, then we can use the
integration by partial fractions method of antidifferentiation.
consider a function of the form f(x)/g(x). Now, differentiating this we have,
d(f(x)/g(x))/dx = [f'(x)g(x) - g'(x)f(x)]/[g(x)]2
Now, integrating both sides of the above equation, we have
f(x)/g(x) = ∫{[f'(x)g(x) - g'(x)f(x)]/[g(x)]2} dx
= ∫[f'(x)/g(x)] dx - ∫[f(x)g'(x)/[g(x)]2] dx
⇒ ∫[f'(x)/g(x)] dx = f(x)/g(x) + ∫[f(x)g'(x)/[g(x)]2] dx
If f(x) = u and g(x) = v, then we have the antiderivative quotient rule as:
∫du/v = u/v + ∫[u/v2] dv
10. 5. Anti-Derivative for Scalar Multiple
To find the antiderivative of scalar multiple of a function f(x), we can
find it using the formula given by, ∫kf(x) dx = k ∫f(x) dx. This implies,
the antidifferentiation of kf(x) is equal to k times the
antidifferentiation of f(x), where k is a scalar. An example using this
antiderivative rule is:
∫4x dx = 4 ∫xdx
= 4 × x2/2 + C
= 2x2 + C
11. 6. Anti-Derivative for Sum and Difference
When the antidifferentiation of the sum and difference of functions is to
be determined, then we can do it by using the following formulas:
● ∫[f(x) + g(x)] dx = ∫f(x) dx + ∫g(x) dx
● ∫[f(x) - g(x)] dx = ∫f(x) dx - ∫g(x) dx
Some of the examples of the antiderivative rule for sum and difference
of functions are as follows:
● ∫[4 + x2] dx = ∫4 dx + ∫x2 dx = 4x + x3/3 + C
● ∫(sin x - log x) dx = ∫sin x dx - ∫ log x dx = -cos x - x log x +
x + C
12. Anti-Derivative Rule for Trigonometric Functions
We have six main trigonometric functions, namely sine, cosine, tangent, cotangent,
secant, and cosecant. Now, we will explore their antiderivative rules of these
trigonometric functions as follows:
● ∫sin x dx = -cos x + C
● ∫cos x dx = sin x + C
● ∫tan x dx = ln |sec x| + C
● ∫cot x dx = ln |sin x| + C
● ∫sec x dx = ln |sec x + tan x| + C
● ∫csc x dx = ln |cosec x - cot x| + C
13. Anti-Derivative Rule for Inverse Trigonometric
Functions
We have six main inverse trigonometric functions, namely inverse sine, inverse cosine,
inverse tangent, inverse cotangent, inverse secant, and inverse cosecant.Now, we will
explore their antiderivative rules of these trigonometric functions as follows:
● ∫sin-1x dx = x sin-1x + √(1 - x2) + C
● ∫cos-1x dx = x cos-1x - √(1 - x2) + C
● ∫tan-1x dx = x tan-1x - (1/2) ln(1 + x2) + C
● ∫cot-1x dx = x cot-1x + (1/2) ln(1 + x2) + C
● ∫sec-1x dx = x sec-1x - ln |x + √(x2 - 1)| + C
● ∫csc-1x dx = x csc-1x + ln |x + √(x2 - 1)| + C