This document discusses vector calculus theorems including Stokes' theorem and the divergence theorem. Stokes' theorem relates the line integral of a vector field around a closed curve to the surface integral of the curl of the vector field over any surface bounded by the curve. The divergence theorem relates the volume integral of the divergence of a vector field over a volume to the surface integral of the vector field over the boundary surface of that volume. The document provides proofs of these theorems and examples of their applications to problems involving conservative vector fields and evaluating integrals.
In this presentation we will learn Del operator, Gradient of scalar function , Directional Derivative, Divergence of vector function, Curl of a vector function and after that solved some example related to above.
Gradient in math
Directional derivative in math
Divergence in math
Curl in math
Gradient , Directional Derivative , Divergence , Curl in mathematics
Gradient , Directional Derivative , Divergence , Curl in math
Gradient , Directional Derivative , Divergence , Curl
In this presentation we will learn Del operator, Gradient of scalar function , Directional Derivative, Divergence of vector function, Curl of a vector function and after that solved some example related to above.
Gradient in math
Directional derivative in math
Divergence in math
Curl in math
Gradient , Directional Derivative , Divergence , Curl in mathematics
Gradient , Directional Derivative , Divergence , Curl in math
Gradient , Directional Derivative , Divergence , Curl
A vector has magnitude and direction. There is an algebra and geometry of vectors which makes addition, subtraction, and scaling well-defined.
The scalar or dot product of vectors measures the angle between them, in a way. It's useful to show if two vectors are perpendicular or parallel.
Successive Differentiation is the process of differentiating a given function successively times and the results of such differentiation are called successive derivatives. The higher order differential coefficients are of utmost importance in scientific and engineering applications.
Mathematical description of Legendre Functions.
Presentation at Undergraduate in Science (math, physics, engineering) level.
Please send any comments or suggestions to improve to solo.hermelin@gmail.com.
More presentations can be found on my website at http://www.solohermelin.com.
A vector has magnitude and direction. There is an algebra and geometry of vectors which makes addition, subtraction, and scaling well-defined.
The scalar or dot product of vectors measures the angle between them, in a way. It's useful to show if two vectors are perpendicular or parallel.
Successive Differentiation is the process of differentiating a given function successively times and the results of such differentiation are called successive derivatives. The higher order differential coefficients are of utmost importance in scientific and engineering applications.
Mathematical description of Legendre Functions.
Presentation at Undergraduate in Science (math, physics, engineering) level.
Please send any comments or suggestions to improve to solo.hermelin@gmail.com.
More presentations can be found on my website at http://www.solohermelin.com.
I am Samantha K. I am a Physics Assignment Expert at eduassignmenthelp.com. I hold a Ph.D. in Physics, from McGill University, Canada. I have been helping students with their homework for the past 8 years. I solve assignments related to Physics.
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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.
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
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 .
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.
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.
Slide 1: Title Slide
Extrachromosomal Inheritance
Slide 2: Introduction to Extrachromosomal Inheritance
Definition: Extrachromosomal inheritance refers to the transmission of genetic material that is not found within the nucleus.
Key Components: Involves genes located in mitochondria, chloroplasts, and plasmids.
Slide 3: Mitochondrial Inheritance
Mitochondria: Organelles responsible for energy production.
Mitochondrial DNA (mtDNA): Circular DNA molecule found in mitochondria.
Inheritance Pattern: Maternally inherited, meaning it is passed from mothers to all their offspring.
Diseases: Examples include Leber’s hereditary optic neuropathy (LHON) and mitochondrial myopathy.
Slide 4: Chloroplast Inheritance
Chloroplasts: Organelles responsible for photosynthesis in plants.
Chloroplast DNA (cpDNA): Circular DNA molecule found in chloroplasts.
Inheritance Pattern: Often maternally inherited in most plants, but can vary in some species.
Examples: Variegation in plants, where leaf color patterns are determined by chloroplast DNA.
Slide 5: Plasmid Inheritance
Plasmids: Small, circular DNA molecules found in bacteria and some eukaryotes.
Features: Can carry antibiotic resistance genes and can be transferred between cells through processes like conjugation.
Significance: Important in biotechnology for gene cloning and genetic engineering.
Slide 6: Mechanisms of Extrachromosomal Inheritance
Non-Mendelian Patterns: Do not follow Mendel’s laws of inheritance.
Cytoplasmic Segregation: During cell division, organelles like mitochondria and chloroplasts are randomly distributed to daughter cells.
Heteroplasmy: Presence of more than one type of organellar genome within a cell, leading to variation in expression.
Slide 7: Examples of Extrachromosomal Inheritance
Four O’clock Plant (Mirabilis jalapa): Shows variegated leaves due to different cpDNA in leaf cells.
Petite Mutants in Yeast: Result from mutations in mitochondrial DNA affecting respiration.
Slide 8: Importance of Extrachromosomal Inheritance
Evolution: Provides insight into the evolution of eukaryotic cells.
Medicine: Understanding mitochondrial inheritance helps in diagnosing and treating mitochondrial diseases.
Agriculture: Chloroplast inheritance can be used in plant breeding and genetic modification.
Slide 9: Recent Research and Advances
Gene Editing: Techniques like CRISPR-Cas9 are being used to edit mitochondrial and chloroplast DNA.
Therapies: Development of mitochondrial replacement therapy (MRT) for preventing mitochondrial diseases.
Slide 10: Conclusion
Summary: Extrachromosomal inheritance involves the transmission of genetic material outside the nucleus and plays a crucial role in genetics, medicine, and biotechnology.
Future Directions: Continued research and technological advancements hold promise for new treatments and applications.
Slide 11: Questions and Discussion
Invite Audience: Open the floor for any questions or further discussion on the topic.
A brief information about the SCOP protein database used in bioinformatics.
The Structural Classification of Proteins (SCOP) database is a comprehensive and authoritative resource for the structural and evolutionary relationships of proteins. It provides a detailed and curated classification of protein structures, grouping them into families, superfamilies, and folds based on their structural and sequence similarities.
1. Course: Electromagnetic Theory
paper code: EI 503
Course Coordinator: Arpan Deyasi
Department of Electronics and Communication Engineering
RCC Institute of Information Technology
Kolkata, India
Topics: Vector Integration
29-09-2021 Arpan Deyasi, EM Theory 1
Arpan Deyasi
Electromagnetic
Theory
5. Line Integration Surface Integration
If 𝑨 is continuous and differentiable at any open surface S formed
by a closed, non-intersecting curve C
( ) ˆ
. .
S
A dr A ndS
=
( )
. .
S
A dr A dS
=
This is Stoke’s theorem
where ෝ
𝒏 is the positive outward normal on the surface S
29-09-2021 Arpan Deyasi, EM Theory 5
Arpan Deyasi
Electromagnetic
Theory
6. Statement of Stoke’s theorem
( )
. .
S
A dr A dS
=
Closed line integration of any continuous and differentiable vector is
numerically equal to the surface integration of curl of that vector where
surface is formed by that non-intersecting closed curve
29-09-2021 Arpan Deyasi, EM Theory 6
Arpan Deyasi
Electromagnetic
Theory
7. Proof of Stoke’s theorem
X
Y
Z
A (x, y) B (x+Δx, y)
C (x+Δx, y+Δy)
D (x, y+Δy)
Open surface formed by the closed path
Closed path ABCDA
𝑷 is continuous and differentiable
at the surface
29-09-2021 Arpan Deyasi, EM Theory 7
Arpan Deyasi
Electromagnetic
Theory
8. Proof of Stoke’s theorem
Value of the vector from A to B
x
P x
=
Value of the vector from B to C
y
y
P
P x y
x
= +
y
y
P
P y x y
x
= +
29-09-2021 Arpan Deyasi, EM Theory 8
Arpan Deyasi
Electromagnetic
Theory
9. Proof of Stoke’s theorem
Value of the vector from C to D
x
x
P
P x y x
y
= − −
x
x
P
P y x
y
= − +
Value of the vector from C to D
Value of the vector from D to A
y
P y
= −
29-09-2021 Arpan Deyasi, EM Theory 9
Arpan Deyasi
Electromagnetic
Theory
10. Proof of Stoke’s theorem
Value of the vector over ABCDA
y x
P P
x y
x y
= −
( ) .
z
P dS
=
( ) y x
z
P P
P
x y
= −
29-09-2021 Arpan Deyasi, EM Theory 10
Arpan Deyasi
Electromagnetic
Theory
11. Proof of Stoke’s theorem
X
Y
O
For plane XOY
( )
. .
XOY z
S
P dr P dS
=
For plane YOZ
( )
. .
YOZ x
S
P dr P dS
=
For plane ZOX
( )
. .
ZOX y
S
P dr P dS
=
29-09-2021 Arpan Deyasi, EM Theory 11
Arpan Deyasi
Electromagnetic
Theory
12. Proof of Stoke’s theorem
Combining all the planes
( )
. .
S
P dr P dS
=
29-09-2021 Arpan Deyasi, EM Theory 12
Arpan Deyasi
Electromagnetic
Theory
13. Stoke’s theorem
for every closed curve is that 𝑨 is irrotational
. 0
Adr =
Prob 1: Show that necessary and sufficient condition that
Soln
𝑨 is irrotational ( ) 0
A
=
( )
. . 0
S
A dr A dS
= =
So the condition is sufficient
29-09-2021 Arpan Deyasi, EM Theory 13
Arpan Deyasi
Electromagnetic
Theory
14. Stoke’s theorem
Let
( ) ˆ
A n
= β is positive constant
( )
ˆ ˆ
. . 0
S
Adr n n dS
=
This contradicts the hypothesis
( ) 0
A
= is the necessary condition also
29-09-2021 Arpan Deyasi, EM Theory 14
Arpan Deyasi
Electromagnetic
Theory
15. Conservative Vector Field
If 𝑷 is irrotational, then the vector field is conservative
0
P
=
If
Then 𝑷 is conservative vector field
P
=
We know that
0
=
29-09-2021 Arpan Deyasi, EM Theory 15
Arpan Deyasi
Electromagnetic
Theory
16. Conservative Vector Field
Prob 2: Show that for conservative field, work done is path independent
Soln
Work done
2
1
.
P
P
W F dr
=
2
1
.
P
P
W dr
=
( )
2
1
ˆ ˆ
ˆ ˆ ˆ ˆ
. ( )
P
P
W i j k dxi dyj dzk
x y z
= + + + +
29-09-2021 Arpan Deyasi, EM Theory 16
Arpan Deyasi
Electromagnetic
Theory
17. Conservative Vector Field
2
1
P
P
W dx dy dz
x y z
= + +
W d
=
2 1
P P
W
= −
Therefore, work done depends on the potentials of starting and end points,
but not on the path joining them
29-09-2021 Arpan Deyasi, EM Theory 17
Arpan Deyasi
Electromagnetic
Theory
18. If 𝑨 is continuous and differentiable at any volume V formed by a
closed, non-intersecting surface S
( ) ˆ
. .
V
S
A dV A ndS
=
( )
. .
V
S
A dV A dS
=
where ෝ
𝒏 is the positive outward normal on the surface S
This is Divergence theorem
Volume Integration Surface Integration
29-09-2021 Arpan Deyasi, EM Theory 18
Arpan Deyasi
Electromagnetic
Theory
19. Statement of Divergence theorem
( )
. .
V
S
A dV A dS
=
Volume integration of divergence of any continuous and differentiable
vector over any closed surface is numerically equal to the surface
integration of that vector where volume is formed by the non-intersecting
closed surface
29-09-2021 Arpan Deyasi, EM Theory 19
Arpan Deyasi
Electromagnetic
Theory
20. Proof of Divergence theorem
Z
X
Y
O
A (x, y) B (x+Δx, y)
C (x+Δx,
y+Δy)
Net outward flux while moving From A to B
1
2
x
x
P
P dx dydz
x
+
Net outward flux while moving From B to C
1
2
y
y
P
P dy dzdx
y
+
29-09-2021 Arpan Deyasi, EM Theory 20
Arpan Deyasi
Electromagnetic
Theory
21. Proof of Divergence theorem
A (x, y) B (x+Δx, y)
C (x+Δx,
y+Δy)
O
Z
X
Y
Net outward flux while moving From C to D
1
2
x
x
P
P dx dydz
x
−
Net outward flux while moving From D to A
1
2
y
y
P
P dy dzdx
y
−
29-09-2021 Arpan Deyasi, EM Theory 21
Arpan Deyasi
Electromagnetic
Theory
22. Proof of Divergence theorem
Net outward flux along X-direction
1
2.
2
x x
P P
dxdydz dxdydz
x x
=
Net outward flux along Y-direction
1
2.
2
y y
P P
dydzdx dydzdx
y y
=
Net outward flux along Z-direction
1
2.
2
z z
P P
dzdxdy dzdxdy
z z
=
29-09-2021 Arpan Deyasi, EM Theory 22
Arpan Deyasi
Electromagnetic
Theory
23. Proof of Divergence theorem
Total outward flux
. y
x z
S
P
P P
P dS dxdydz
x y z
= + +
( )
. .
V
S
P dS P dV
=
29-09-2021 Arpan Deyasi, EM Theory 23
Arpan Deyasi
Electromagnetic
Theory
24. Prob 3: Evaluate
Divergence theorem
( )
. .
V
S
r dS r dV
=
.
S
r dS
Soln
Using Divergence theorem
ˆ ˆ
ˆ ˆ ˆ ˆ
. .( )
V
S
r dS i j k xi yj zk dV
x y z
= + + + +
29-09-2021 Arpan Deyasi, EM Theory 24
Arpan Deyasi
Electromagnetic
Theory
25. Divergence theorem
.
V
S
r dS x y z dV
x y z
= + +
. 3
V
S
r dS dV
=
. 3
S
r dS V
=
29-09-2021 Arpan Deyasi, EM Theory 25
Arpan Deyasi
Electromagnetic
Theory
26. Divergence theorem
Prob 4: Show that
( ) ˆ.
V
S
dV n dS
=
Soln
Using Divergence theorem
( )
. .
V
S
A dV A dS
=
Let
A B
= 𝑩 is constant
Let
A B
=
29-09-2021 Arpan Deyasi, EM Theory 26
Arpan Deyasi
Electromagnetic
Theory
27. Divergence theorem
( )
. .
V
S
B dV B dS
=
( ) ˆ
. .
V
S
B dV B ndS
=
( ) ˆ
. ( . )
V
S
B dV B n dS
=
29-09-2021 Arpan Deyasi, EM Theory 27
Arpan Deyasi
Electromagnetic
Theory
28. Divergence theorem
( ) ˆ
. . ( )
V
S
B dV B n dS
=
( ) ˆ
( )
V
S
dV n dS
=
29-09-2021 Arpan Deyasi, EM Theory 28
Arpan Deyasi
Electromagnetic
Theory
29. Divergence theorem
Prob 5: Show that ( ) ˆ
( )
V
S
A dV n A dS
=
Soln
Using Divergence theorem ( )
. .
V
S
P dV P dS
=
Let
P A B
=
29-09-2021 Arpan Deyasi, EM Theory 29
Arpan Deyasi
Electromagnetic
Theory
30. Divergence theorem
( )
.[ ] [ ].
V
S
A B dV A B dS
=
( ) ˆ
. . [ ]
V
S
B A dV B n A dS
=
( ) ˆ
[ ]
V
S
A dV n A dS
=
29-09-2021 Arpan Deyasi, EM Theory 30
Arpan Deyasi
Electromagnetic
Theory
31. Green’s theorem
Let
( )
. .
V
S
P dV P dS
=
From Divergence theorem
P
=
( )
. .
V
S
dV dS
=
29-09-2021 Arpan Deyasi, EM Theory 31
Arpan Deyasi
Electromagnetic
Theory
32. Green’s theorem
( )
2
. .
V
S
dV dS
+ =
………. (2)
This is Green’s First Identity
Interchanging φ and ψ
( )
2
. .
V
S
dV dS
+ =
………. (1)
29-09-2021 Arpan Deyasi, EM Theory 32
Arpan Deyasi
Electromagnetic
Theory
33. Green’s theorem
This is Green’s Second Identity
Substituting (2) from (1)
( ) ( )
2 2
.
V
S
dV dS
− = −
29-09-2021 Arpan Deyasi, EM Theory 33
Arpan Deyasi
Electromagnetic
Theory
34. Helmhotz’s theorem
A vector field can be uniquely defined by its divergence and curl
Proof
We consider two vectors 𝑷 and 𝑸 defined, continuous and differentiable
In a closed, non-intersecting surface S which forms the volume V
ˆ ˆ
. .
P n Q n
=
ˆ
( ). 0
P Q n
− = ………. (1)
29-09-2021 Arpan Deyasi, EM Theory 34
Arpan Deyasi
Electromagnetic
Theory
35. Helmhotz’s theorem
At any arbitrary point
. .
P Q
=
.( ) 0
P Q
− = ………. (2)
Again, at that point
P Q
=
( ) 0
P Q
− = ………. (3)
29-09-2021 Arpan Deyasi, EM Theory 35
Arpan Deyasi
Electromagnetic
Theory
36. Helmhotz’s theorem
Let
( )
R P Q
= −
Since 𝑹 is both solenoidal and irrotational
R
=
where φ is arbitrary scalar function
………. (4)
. 0
=
From (2)
29-09-2021 Arpan Deyasi, EM Theory 36
Arpan Deyasi
Electromagnetic
Theory
37. Helmhotz’s theorem
2
0
=
Using Green’s First Identity
( )
2
. .
V
S
dV dS
+ =
For φ = ψ
( )
2
. .
V
S
dV dS
+ =
29-09-2021 Arpan Deyasi, EM Theory 37
Arpan Deyasi
Electromagnetic
Theory
38. Helmhotz’s theorem
( )
2
.
V
S
dV dS
=
( )
2
.
V
S
dV R dS
=
ˆ
( ). 0
P Q n
− =
From (1)
ˆ
. 0
R ndS =
29-09-2021 Arpan Deyasi, EM Theory 38
Arpan Deyasi
Electromagnetic
Theory
39. Helmhotz’s theorem
ˆ
. 0
R ndS =
( )
2
0
V
dV
=
0
=
0
P Q
− =
29-09-2021 Arpan Deyasi, EM Theory 39
Arpan Deyasi
Electromagnetic
Theory
40. Helmhotz’s theorem
P Q
=
Therefore, the vector is unique which can be defined
by its divergence and curl
29-09-2021 Arpan Deyasi, EM Theory 40
Arpan Deyasi
Electromagnetic
Theory