Laplace Transform
content:
PIERRE-SIMON LAPLACE
Existence of Laplace Transform
Laplace Transform of some basic functions
Piece Wise continuous function
Image Processing by using Laplace Transform
Real Life Application of Laplace Transform
Limitations of Laplace Transform
Conclusion
Laplace Transform
content:
PIERRE-SIMON LAPLACE
Existence of Laplace Transform
Laplace Transform of some basic functions
Piece Wise continuous function
Image Processing by using Laplace Transform
Real Life Application of Laplace Transform
Limitations of Laplace Transform
Conclusion
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.
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.
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.
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.
Nutraceutical market, scope and growth: Herbal drug technologyLokesh Patil
As consumer awareness of health and wellness rises, the nutraceutical market—which includes goods like functional meals, drinks, and dietary supplements that provide health advantages beyond basic nutrition—is growing significantly. As healthcare expenses rise, the population ages, and people want natural and preventative health solutions more and more, this industry is increasing quickly. Further driving market expansion are product formulation innovations and the use of cutting-edge technology for customized nutrition. With its worldwide reach, the nutraceutical industry is expected to keep growing and provide significant chances for research and investment in a number of categories, including vitamins, minerals, probiotics, and herbal supplements.
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.
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.
1. Path Integration: One-Degree of Freedom Scalar Field Along Time Direction
F. J. P. Roa
In the absence of time-dependent sources, we can write the scalar field transition matrix
(1.1)
⟨𝜑′|𝑒𝑥𝑝(
−𝑖𝑇𝐻[ 𝐽 = 0 ]
ℏ
)|𝜑⟩
This matrix gives the probability (transition) amplitude that the particle in an initial state | 𝜑⟩ at
time 𝑡 = 0 will be in some other state | 𝜑′⟩ at the later time 𝑇 > 0. The system Hamiltonian
𝐻[ 𝐽 = 0 ] here does not contain time-dependent source 𝐽(𝑡) so that the time-evolution operator
simply given as
(1.2)
𝑒𝑥𝑝 (
−𝑖
ℏ
∫ 𝑑𝑡 𝐻[ 𝐽 = 0 ]
𝑇
0
) = 𝑒𝑥𝑝(
−𝑖𝑇𝐻[ 𝐽 = 0 ]
ℏ
)
The Hamiltonian contained in (1.2) is an operator although we omit the putting of a hat over it
except in confusing circumstances where we are obliged to emphasize it with a hat as an
operator. Classically, this Hamiltonian can be derived through Legendre transformation which
we shall demonstrate in the later portions of this draft.
The evaluation of (1.1) can be done via Path Integration
(1.3)
⟨𝜑′|𝑒𝑥𝑝(
−𝑖𝑇𝐻[ 𝐽 = 0 ]
ℏ
)|𝜑⟩ = ∫ 𝐷𝜑 𝑒𝑥𝑝(𝑖𝑆 𝜑/ℏ)
𝜑( 𝐵) = 𝜑′
𝜑( 𝐴) = 𝜑
and in this draft we try to get results that are close to those of doing Path Integration in
elementary particle quantum mechanics by considering a scalar field with one degree of freedom
along time direction.
2. Since the scalar field is confined to have only one degree of freedom along time direction its
action is to be given by
(1.4)
𝑆 𝜑 = ∫ 𝑑𝑡 ℓ 𝜑 = ∫ 𝑑𝑡 (
1
2
(𝜕𝑡 𝜑)2
−
1
2
𝑀2
𝜑2
)
and as cautionary, the action has the appropriate units of 𝑗𝑜𝑢𝑙𝑒𝑠 ∙ 𝑠𝑒𝑐𝑜𝑛𝑑𝑠 so we also take note
of the correct units of our one-degree of freedom scalar field in this case. This we shall do in the
later while.
Our integral limits define the interval
(1.5)
0 < 𝑡 < 𝑇
with the corresponding labelling that 𝜑( 𝐴) = 𝜑 as the initial value of the scalar field at 𝑡 = 0
(though this field value somewhat confusing with the field variable appearing in (1.4) so we must
be reminded that the former is distinct from the latter), while 𝜑( 𝐵) = 𝜑′ as the later value given
at 𝑡 = 𝑇.
In addition to confining the scalar field in one degree of freedom along time direction with the
aim to derive result that is close to that of doing path integrals involving particle quantum
mechanics, we shall also consider the scalar field as a perturbation field above the mean classical
𝜑𝑐. So
(1.6)
𝜑 = 𝜑𝑐 + 𝜑 𝑝
where 𝜑 𝑝 is the perturbation field that must vanish at the time boundaries defined in (1.5).
Given (1.6), consequently (1.4) is decomposed as
(1.7)
𝑆 𝜑 = 𝑆 𝜑𝑐 + 𝑆 𝜑𝑝 + 𝑆̅ 𝜑
(1.7.1)
𝑆 𝜑𝑐 = ∫ 𝑑𝑡 (
1
2
(𝜕𝑡 𝜑𝑐)2
−
1
2
𝑀2
𝜑𝑐
2
)
(1.7.2)
3. 𝑆 𝜑𝑝 = ∫ 𝑑𝑡 (
1
2
(𝜕𝑡 𝜑 𝑝)2
−
1
2
𝑀2
𝜑 𝑝
2
)
(1.7.3)
𝑆̅ 𝜑 = ∫ 𝑑𝑡 ((𝜕𝑡 𝜑𝑐)(𝜕𝑡 𝜑 𝑝) − 𝑀2
𝜑𝑐 𝜑 𝑝)
The last part is done through partial integration so we may write that as
(1.8)
𝑆̅ 𝜑 = [(𝜕𝑡 𝜑𝑐)𝜑 𝑝]
𝐴
𝐵
− ∫ 𝑑𝑡 ( 𝜕𝑡
2
𝜑𝑐 + 𝑀2
𝜑𝑐 ) 𝜑 𝑝
This simply vanishes given the time boundary conditions for the perturbation field
(1.9.1)
𝜑 𝑝( 𝐴) = 𝜑 𝑝( 𝐵) = 0
and the classical equation of motion
(1.9.2)
𝜕𝑡
2
𝜑𝑐 + 𝑀2
𝜑𝑐 = 0
Following this, (1.7) reduces to an effective action
(1.10)
𝑆 𝜑(𝑒𝑓𝑓) = 𝑆 𝜑𝑐 + 𝑆 𝜑𝑝
Immediately, we provide details for the derivation of the equation of motion (1.9.2).
For the classical equation of motion (1.9.2) we take the variation 𝛿𝑆 𝜑𝑐 of the classical action
(1.7.1) in terms of the variation 𝛿𝜑𝑐 of the classical field and to take note of the commutativity
(1.11)
𝛿𝜕𝑡 = 𝜕𝑡 𝛿
So after partial integration we write this variation as
(1.12)
𝛿𝑆 𝜑𝑐 = [(𝜕𝑡 𝜑𝑐)𝛿𝜑𝑐] 𝐴
𝐵
− ∫ 𝑑𝑡 ( 𝜕𝑡
2
𝜑𝑐 + 𝑀2
𝜑𝑐 ) 𝛿𝜑𝑐
4. and we shall also impose that the varied field vanishes at the time boundaries
(1.12.1)
𝛿𝜑𝑐( 𝐴) = 𝛿𝜑𝑐( 𝐵) = 0
Letting the action (1.7.1) be stationary that is,
(1.12.2)
𝛿𝑆 𝜑𝑐 = 0
we arrive at (1.9.2).
Being given with an effective action (1.10), we can now put the path integral in (1.3) in its
factored form
(1.13)
⟨𝜑′|𝑒𝑥𝑝 (
−𝑖𝑇𝐻[ 𝐽 = 0 ]
ℏ
)|𝜑⟩ = 𝑒𝑥𝑝(𝑖𝑆 𝜑𝑐/ℏ) ∫ 𝒟𝜑 𝑝 𝑒𝑥𝑝(𝑖𝑆 𝜑𝑝/ℏ)
𝜑 𝑝( 𝐴)= 𝜑 𝑝( 𝐵)=0
where the actual path integration is done on the perturbation field.
Note here that although the varied classical field must vanish at the time boundaries (see
(1.12.1)) the classical field itself assumes the non-vanishing time boundary conditions given
earlier
(1.14.1)
𝜑𝑐( 𝐴) = 𝜑( 𝐴) = 𝜑
and
(1.14.2)
𝜑𝑐( 𝐵) = 𝜑( 𝐵) = 𝜑′
Now proceeding with the actual path integration
(2)
∫ 𝒟𝜑 𝑝 𝑒𝑥𝑝(𝑖𝑆 𝜑𝑝/ℏ)
𝜑 𝑝 ( 𝐴)= 𝜑 𝑝( 𝐵)=0
5. We take note of the time-boundary conditions on the perturbation field (1.9.1) and one form of
this perturbation that immediately satisfies (1.9.1) is given by
(2.1)
𝜑 𝑝 ( 𝑡) = ∑ 𝐴𝑙 𝑠𝑖𝑛
𝜋𝑙
𝑇
𝑡
𝑙
where the summation index 𝑙 takes on both the odd and even integral values. We could have
chosen 2𝜋𝑙, which is exclusive to the even multiples of 𝜋 but since we are aiming for results that
are close to those of doing path integral in particle quantum mechanics, we simply choose 𝜋𝑙,
where 𝑙 includes both the odd and even integral values.
For convenience let us put
(2.2)
𝑘0( 𝑙) =
𝜋𝑙
𝑇
and go for the Fourier transformation of (2.1) with integral limits that define the interval
(2.3)
−𝑇 < 𝑡 < 𝑇
(2.4)
𝜑̃ 𝑝(𝑘0( 𝑙′)) =
1
𝑇
∫ 𝑑𝑡 𝜑 𝑝(𝑡) exp(−𝑖𝑘0( 𝑙′) 𝑡)
𝑇
−𝑇
The integration involves the complete set
(2.5.1)
∫ 𝑑𝑡 𝑠𝑖𝑛
𝜋𝑙
𝑇
𝑡 cos
𝜋𝑙′
𝑇
𝑡 = 0
𝑇
−𝑇
and
(2.5.2)
1
𝑇
∫ 𝑑𝑡 𝑠𝑖𝑛
𝜋𝑙
𝑇
𝑡 sin
𝜋𝑙′
𝑇
𝑡 = 𝛿𝑙𝑙′
𝑇
−𝑇
6. These for all integral values of 𝑙. Thus, we get the Fourier components
(2.6)
𝜑̃ 𝑝(𝑘0( 𝑙′)) = −𝑖𝐴𝑙′
Then for 𝜑̃ 𝑝(−𝑘0( 𝑙′)) as 𝑘0( 𝑙′) → −𝑘0( 𝑙′) we have
(2.7)
𝜑̃ 𝑝(−𝑘0( 𝑙′)) =
1
𝑇
∫ 𝑑𝑡 𝜑 𝑝( 𝑡) exp( 𝑖𝑘0( 𝑙′) 𝑡) = 𝑖𝐴𝑙′
𝑇
−𝑇
We can take the complex conjugation of (2.4)
(2.8)
[To be continued…]
Ref’s
[1] Townsend, P. K., Blackholes – Lecture Notes, http://xxx.lanl.gov/abs/gr-qc/9707012
[2]van Baal, P., A Course In Field Theory
[3]Siegel, W., Fields, http://insti.physics.sunysb.edu/~/siegel/plan.html