Cover page

Image Name: The Pleiades.

The Pleiades, or Messier 45 (M45), is a captivating open star cluster situated in the Taurus constellation.

Recognizable for its prominent blue glow, the Pleiades is one of the nearest clusters to Earth, containing hot,

young stars. Commonly referred to as the Seven Sisters from Greek mythology, the Pleiades have cultural

signiﬁcance in various traditions and hold astronomical importance for observational studies.

Telescope: Askar ACL200 APO

Camera: Canon 6D

302 exposures of 60" each stacked, comprising 5 hours of total data

Tracking mount: iOptron smartEQ pro

Image Courtesy: Aditya Kinjawadekar https://www.instagram.com/deepsky

wonders/

Managing Editor Chief Editor Editorial Board Correspondence

Ninan Sajeeth Philip Abraham Mulamootil K Babu Joseph The Chief Editor

Ajit K Kembhavi airis4D

Geetha Paul Thelliyoor - 689544

Arun Kumar Aniyan India

Jorunal Publisher Details

Publisher : airis4D, Thelliyoor 689544, India

Website : www.airis4d.com

Email : nsp@airis4d.com

Phone : +919497552476

Editorial

by Fr Dr Abraham Mulamoottil

airis4D, Vol.2, No.1, 2024

www.airis4d.com

The February 2024 edition of airis4D features

Aditya Kinjawadekar, an award-winning astropho-

tographer, whose work has gained recognition from

ISRO, NASA, and ESA. The cover story focuses on the

Pleiades, an open star cluster in the Taurus constella-

tion known for its distinctive blue glow. Also known as

the Seven Sisters from Greek mythology, the Pleiades

holds cultural and astronomical signiﬁcance, making it

a captivating subject for observational studies.

The article ”Unveiling the Power of Attention

Networks” by Blesson George explores the transfor-

mative impact of attention networks in artiﬁcial in-

telligence. It discusses the unique characteristics of

attention mechanisms, emphasizing their selective in-

formation processing and likening it to human cogni-

tive focus. Case studies, including language transla-

tion and image recognition, demonstrate the signiﬁ-

cant role of attention networks. The paper highlights

inﬂuential works like ”Attention Is All You Need” by

Vaswani et al., which introduced the transformative

Transformer architecture. The self-attention mecha-

nism in the Transformer model allows for selective

focus on diﬀerent parts of input sequences, addressing

challenges faced by traditional neural networks. The

article also touches on multi-head attention, its contri-

bution to parallel processing, and its relevance in im-

age analysis for improved interpretability. The author

anticipates further exploration of attention networks,

particularly the Transformer architecture, in upcoming

issues, focusing on their role in redeﬁning memory

processing benchmarks in deep learning.

In ”Black Hole Stories-7: Particle Paths in Gen-

eral Relativity – The Dependence on Angular Momen-

tum” by Ajit Kembhavi, the author explores the mo-

tion of particles in the gravitational ﬁeld around a black

hole, focusing on the Schwarzschild metric. The article

delves into the eﬀective potential and its dependence

on angular momentum, emphasizing its signiﬁcance

in understanding the trajectories of particles. It dis-

cusses how changes in angular momentum impact the

shape of the eﬀective potential and the astrophysical

implications for black hole systems. Additionally, the

concept of Innermost Stable Circular Orbits (ISCO)

and the extraction of energy from particles in circular

orbits around black holes are brieﬂy covered. The au-

thor highlights the astrophysical importance of these

phenomena.

In ”Colour-Magnitude Diagram, Part-1” by Sindhu

G, the author introduces the concept of a color-magnitude

diagram (CMD) as a powerful tool in astronomy for

analyzing stellar populations. The article explains the

fundamental concepts of magnitudes, apparent magni-

tude (m), and absolute magnitude (M). It delves into

the impact of interstellar extinction on the apparent

brightness of celestial objects and introduces the dis-

tance modulus formula for estimating absolute magni-

tude based on apparent magnitude and distance. The

author, Sindhu G, is a research scholar specializing

in Astronomy & Astrophysics, focusing on variable

stars and machine learning algorithms. The upcoming

Part-2 will explore color indices and delve further into

Color-Magnitude diagrams.

In the article ”Odonates: Sensitive Indicators of

Aquatic Ecosystem Health and Pollution” by Geetha

Paul, the author discusses the signiﬁcance of Odonata,

including dragonﬂies and damselﬂies, as indicators of

environmental quality in aquatic ecosystems. Odonata

are sensitive to various pollutants and changes in habi-

tat conditions, making them valuable for bioassess-

ment and biomonitoring. The article highlights how

alterations in habitat structure, habitat degradation,

and pollution, including persistent organic pollutants,

agricultural practices, and eutrophication, can impact

Odonata populations. The author emphasizes the role

of Odonata as bioindicators, providing insights into

the health of aquatic ecosystems and freshwater qual-

ity. Speciﬁc Odonata species are discussed, including

those found in polluted waters, serving as indicators of

water quality. The upcoming article will delve into the

taxonomic description of pollution indicator species

mentioned in the paper.

The article ”Introduction to Aging Clocks” by

Jinsu Ann Mathew discusses the dynamic and indi-

vidualized nature of aging. It deﬁnes aging as a gradual

decline in essential physiological functions and intro-

duces terms like lifespan and healthspan. The distinc-

tion between chronological age and biological age is

explained, with a focus on various markers used in

aging clocks such as telomere length, DNA methyla-

tion, protein levels, metabolic markers, immune system

markers, and inﬂammation. These markers provide

insights into the aging process, allowing for the esti-

mation of biological age and prediction of age-related

disease risks. The article concludes by acknowledging

the evolving nature of aging research and hints at future

exploration of related topics.

The article ”Unveiling the Cosmos: The Rise of

Smartphone Astrophotography and the Role of Arti-

ﬁcial Intelligence and Machine Learning” by Aditya

Kinjawadekar explores the popularity of smartphone

astrophotography among amateur astronomers. Focus-

ing on major brands like Apple, Samsung, and Google,

the article details how machine learning and artiﬁcial

intelligence enhance smartphone cameras’ capabilities

to capture detailed images of celestial objects. Speciﬁc

features of each brand, such as Apple’s Deep Fusion,

Samsung’s Moon Shot mode with high zoom capabil-

ities, and Google’s Astrophotography mode using AI-

driven scene recognition, are highlighted. The article

concludes by emphasizing the transformative impact

of machine learning and AI on making astrophotog-

raphy more accessible and promising for enthusiasts

worldwide.

The article ”The Lunar Occultation of Antares”

by Atharva Pathak discusses a celestial event occur-

ring on February 5, 2024. The Moon will pass in front

of Antares (Alpha Scorpii), resulting in a lunar occul-

tation visible from speciﬁc parts of Asia. The author

provides details on the visibility of this event, mention-

ing that it will be observable from the western coast of

India, with speciﬁc timings for Pune. Atharva Pathak,

an astronomer, software engineer, and data manager, is

a life member of Jyotirvidya Parisanstha, an associa-

tion of amateur astronomers in India, and is involved

in the IOTA-India Occultation section.

iii

Contents

Editorial ii

I Artiﬁcial Intelligence and Machine Learning 1

1 Unveiling the Power of Attention Networks 2

1.1 Unique Characteristics and Capabilities of Attention Mechanisms. . . . . . . . . . . . . . . . 2

1.2 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

II Astronomy and Astrophysics 5

1 Black Hole Stories-7

Particle Paths in General Relativity

The Dependence on Angular Momentum 6

1.1 Dependence of the Eﬀective Potential on the Angular Momentum . . . . . . . . . . . . . . . 6

1.2 Innermost Stable Circular Orbits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

1.3 Energy Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

2 Colour - Magnitude Diagram, Part-1 9

2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

2.2 Magnitude . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

2.3 Interstellar Extinction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

2.4 Distance Modulus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

III Biosciences 12

1 Odonates: Sensitive Indicators of Aquatic Ecosystem Health and Pollution 13

1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

1.2 Alteration in Habitat Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

1.3 Habitat Degradation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

1.4 Eutrophication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

1.5 Persistent Organic Pollutants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

1.6 Agricultural Practices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

1.7 Odonata Species Commonly Found in Freshwater Streams and Rivers . . . . . . . . . . . . . 16

2 Introduction to Aging Clocks 18

2.1 Lifespan and Healthspan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

2.2 Telomere length . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

2.3 DNA Methylation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

2.4 Protein Levels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

2.5 Metabolic Markers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

CONTENTS

2.6 Immune System Markers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

2.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

IV General 23

1 Unveiling the Cosmos: The Rise of Smartphone Astrophotography and the Role of

Artiﬁcial Intelligence and Machine Learning 24

1.1 Apple . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

1.2 Google . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

1.3 Samsung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

2 The Lunar Occultation of Antares 28

V Fiction 29

1 Perishing Without Publishing 30

Part I

Artiﬁcial Intelligence and Machine Learning

Unveiling the Power of Attention Networks

by Blesson George

airis4D, Vol.2, No.1, 2024

www.airis4d.com

In the dynamic landscape of artiﬁcial intelligence,

one groundbreaking concept has emerged as the linch-

pin for enhanced computational understanding – atten-

tion networks. As we embark on a journey through the

realms of machine learning and cognitive computation,

it becomes increasingly evident that attention networks

are not merely an incremental advance; they represent

a paradigm shift in how algorithms process informa-

tion. This article serves as an illuminating gateway

into the intricate world of attention networks, peeling

back the layers to reveal their fundamental principles

and the transformative potential they hold.

In an era where the volume of data is staggering,

attention networks act as the intelligent gatekeepers,

discerning patterns and nuances that elude conven-

tional models. At their core, these networks mirror the

selective focus of human attention, allowing machines

to prioritize and process information with a granular-

ity that was once thought to be exclusive to biological

cognition.

As we navigate through the intricacies of attention

mechanisms, we’ll uncover the mathematical underpin-

nings that enable these networks to adapt, learn, and

make nuanced decisions. From the elegance of soft-

max attention to the eﬃciency of scaled dot-product at-

tention, we’ll demystify the mechanisms that empower

attention networks to sift through the noise and capture

the essence of relevant information.

Beyond the theoretical foundations, attention net-

works manifest in tangible and impactful ways. They

reshape the landscape of natural language processing,

illuminate the intricacies of computer vision, and pi-

oneer new frontiers in artiﬁcial intelligence. Through

insightful case studies, we will witness the tangible out-

comes of attention networks, from enhancing language

translation to revolutionizing image recognition.

1.1 Unique Characteristics and

Capabilities of Attention

Mechanisms.

The assertion that ”attention networks represent

a substantial innovation or departure from conven-

tional approaches in artiﬁcial intelligence” is rooted in

the unique characteristics and capabilities of attention

mechanisms.

1. Selective Information Processing: Attention

networks enable selective information process-

ing, allowing models to focus on speciﬁc parts

of input data. This mimics the human cognitive

process of selectively attending to relevant infor-

mation, enhancing the model’s ability to capture

context and relationships.

In the paper by Bahdanau et al [1], the selec-

tive focussing is discussed. It showcases the

capability to selectively concentrate on distinct

segments of the input sequence during the gen-

eration of each output word, departing from a

reliance on a ﬁxed-length vector representation

of the entire input sequence. This characteristic

enhances the model’s ﬂexibility, enabling it to

adeptly manage extended input sequences.

The paper ”Attention Is All You Need” by Vaswani

et al.[2] stands as a monumental contribution to

the ﬁeld of attention networks, making it one of

1.1 Unique Characteristics and Capabilities of Attention Mechanisms.

the most signiﬁcant and inﬂuential works in this

domain. This seminal paper not only introduced

the Transformer architecture but also fundamen-

tally altered the landscape of natural language

processing and machine learning.

Vaswani et al.’s groundbreaking insights into at-

tention mechanisms have left an indelible mark

on the way researchers conceptualize and im-

plement attention networks. The Transformer

model’s ability to selectively focus on diﬀerent

parts of input sequences, as detailed in this paper,

has become a hallmark of modern deep learning

architectures.

The Transformer model uses multi-head atten-

tion, which allows the model to attend to diﬀer-

ent parts of the input sequence in parallel. Each

attention head can focus on diﬀerent aspects of

the input sequence, allowing the model to selec-

tively process diﬀerent parts of the data.

The contribution by Xu et al.[3] underscores the

pivotal role of attention in facilitating the emer-

gence of salient features dynamically, particu-

larly when confronted with image clutter. The

authors accentuate the importance of acquiring

the skill to attend to various locations to generate

a caption eﬀectively. They introduce two distinc-

tive variants of attention mechanisms: a ”hard”

stochastic attention mechanism and a ”soft” de-

terministic attention mechanism. This selective

processing capability empowers the model to

concentrate on information crucial to the task,

culminating in the generation of more enriched

and descriptive captions. The work by Xu et al.

thus stands as a testament to the transformative

impact of attention mechanisms in enhancing the

interpretability and performance of image cap-

tioning models.

2. Long-Range Dependencies: Attention networks

exhibit a remarkable proﬁciency in capturing

both temporal and spatial long-range dependen-

cies within input sequences and images. This

dual capability enables them to discern intricate

relationships across distant elements in various

domains, enhancing their eﬀectiveness in tasks

ranging from natural language processing to im-

age analysis.

The Transformer model discussed in [2] achieves

long-range dependence through its self-attention

mechanism, which allows the model to selec-

tively attend to diﬀerent parts of the input se-

quence and capture dependencies between words

that are far apart.

In traditional recurrent neural networks (RNNs),

capturing long-range dependencies can be chal-

lenging because the information from earlier time

steps can become diluted or lost as it is passed

through the network. However, the self-attention

mechanism in the Transformer model allows the

model to selectively attend to diﬀerent parts of

the input sequence, enabling it to eﬀectively cap-

ture long-range dependencies and relationships

within the data.

The self-attention mechanism in the Transformer

model works by computing attention weights

for each word in the input sequence based on

the similarity between the representations of the

words. Each word can attend to all other words in

the sequence, regardless of their position, allow-

ing the model to capture dependencies between

words that are far apart.

In addition, the Transformer model uses multi-

head attention, which allows the model to attend

to diﬀerent parts of the input sequence in par-

allel. Each attention head can focus on diﬀer-

ent aspects of the input sequence, allowing the

model to selectively process diﬀerent parts of the

data and capture long-range dependencies more

eﬀectively.

The parallel nature of self-attention in the Trans-

former model also contributes to its ability to

capture long-range dependencies, as it allows the

model to process the input sequence more eﬃ-

ciently and eﬀectively attend to multiple parts of

the sequence simultaneously.

The innovative approach by Bello et al. [4] com-

bines self-attention with convolutional networks

to capture long-range interactions and improve

image classiﬁcation and object detection tasks.

1.2 Conclusion

Long-range dependencies are achieved through

the use of self-attention mechanisms in Atten-

tion Augmented Convolutional Networks. Self-

attention produces a weighted average of values

computed from hidden units, where the weights

used in the weighted average operation are pro-

duced dynamically via a similarity function be-

tween hidden units. This allows self-attention to

capture long-range dependencies between input

signals, as the interaction between input signals

depends on the signals themselves rather than be-

ing predetermined by their relative location like

in convolutions.

1.2 Conclusion

In the next issue, we will delve into how the

Transformer architecture, with its prominent at-

tention mechanisms, surpasses traditional mod-

els like RNN and LSTM in terms of memory

ability. By exploring its innovative approach

to handling sequential data and understanding

long-range dependencies, we aim to unravel the

factors that make attention networks a compelling

choice in the evolving landscape of artiﬁcial in-

telligence. Stay tuned for an in-depth exploration

of how attention networks redeﬁne the bench-

marks for memory processing in the realm of

deep learning.

References

[1] Bahdanau, D., Cho, K.,and Bengio, Y. (2014).

Neural machine translation by jointly learning to

align and translate. arXiv preprint arXiv:1409.0473.

[2] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit,

J., Jones, L., Gomez, A. N., ... and Polosukhin, I.

(2017). Attention is all you need. Advances in neural

information processing systems, 30.

[3] Xu, K., Ba, J., Kiros, R., Cho, K., Courville, A.,

Salakhudinov, R., ... and Bengio, Y. (2015, June).

Show, attend and tell: Neural image caption gener-

ation with visual attention. In International confer-

ence on machine learning (pp. 2048-2057). PMLR.

[4] Bello, I., Zoph, B., Vaswani, A., Shlens, J., & Le,

Q. V. (2019). Attention augmented convolutional

networks. In Proceedings of the IEEE/CVF inter-

national conference on computer vision (pp. 3286-

3295).

About the Author

Dr. Blesson George presently serves as an

Assistant Professor of Physics at CMS College Kot-

tayam, Kerala. His research pursuits encompass the

development of machine learning algorithms, along

with the utilization of machine learning techniques

across diverse domains.

Part II

Astronomy and Astrophysics

Black Hole Stories-7

Particle Paths in General Relativity

The Dependence on Angular Momentum

by Ajit Kembhavi

airis4D, Vol.2, No.1, 2024

www.airis4d.com

In Black Hole Stories-6 we considered the motion

of particles in the Schwarzschild metric, which de-

scribes the gravitational ﬁled around a black hole. We

found there that the concept of eﬀective potential is

very useful in understanding the nature of the trajecto-

ries. In this story we will consider how the shape of the

eﬀective potential depends on the value of the angular

momentum and mention brieﬂy why the angular mo-

mentum dependence is important for the astrophysics

of black hole systems.

1.1 Dependence of the Eﬀective

Potential on the Angular

Momentum

We found in BH6 that for the Schwarzschild case,

the eﬀective potential V

e,S

is given by

e,S

= −

−

where L is the constant angular momentum of a

particle of mass m moving in the gravitational ﬁeld of

the black hole, which has mass M. The change in radial

coordinate r and angular coordinate φ with proper time

are given by

(

dτ

)

= E − V

e,S

and

dϕ

dτ

For given constant values of the energy E and angular

momentum L of the particle, the trajectory of the parti-

cle can be determined as a function of the proper time

τ using the above equations.

The shape of the eﬀective potential is shown in

Figure 1. In the Schwarzschild case the potential has

a potential well at intermediate values of r. At smaller

values of r the potential passes through a maximum

and then, as r decreases further, it plunges to large

negative values. We have seen in BH6 that when the

particle energy E is negative, the particle is bound and

has a precessing nearly elliptical orbit. A particle with

negative energy corresponding to the red dot in the

ﬁgure has a stable circular orbit, while a particle at the

maximum of the potential as shown by the green dot

has a unstable circular orbit.

We have shown in Figure 2 the eﬀective potential

for diﬀerent values of L. The radial coordinate r in

units of the Schwarzschild radius R

= 2GM (in the

c=1 units that we are using) is indicated along the

horizontal axis, and the eﬀective potential along the

vertical axis. As the angular momentum decreases the

following changes take place. (1) The point at which

the minimum in the potential barrier occurs, indicated

by the blue dots, moves closer to the black hole at r=0,

and the value of the eﬀective potential at the minimum

1.2 Innermost Stable Circular Orbits

Figure 1: The eﬀective potential for the Schwarzschild

case. The potential for eﬀective Newtonian gravity is

shown for comparision.

becomes more negative. Therefore, with decreasing

angular momentum, the radius of the circular orbits

corresponding to the blue dot decreases and the orbits

become more tightly bound. The reason of course is

that the closer a orbit is to the black hole, the greater

is the eﬀect of the gravitational ﬁeld on it. (2) The

height of the potential barrier, that is the point at which

the maximum occurs reduces with reducing angular

momentum. This happens because the barrier is due

to the centrifugal force (see BH5 and BH6), which

decreases as the angular momentum reduces. The point

at which the maximum occurs moves slowly outwards.

(3) It can be shown that when the angular momentum

reduces to the value L = 2

√

3GM, the minimum and

maximum occur at the same point. For still smaller

L values, there is neither a minimum nor a maximum

in the eﬀective potential, and it keeps decreasing as r

decreases from large values of r to r=0. Particles with

such angular momentum values do not see a potential

barrier at all and will plunge to the black hole regardless

of their energy.

1.2 Innermost Stable Circular Orbits

Consider a value of the angular momentum L for

which both the maximum and the minimum in the ef-

fective potential are present. A particle with negative

energy E moving in such a potential will have a bound

orbit which is almost elliptical and slowly precessing.

If now some of the energy E of the particle is taken

away, keeping its angular momentum constant, the par-

Figure 2: The eﬀective potential for diﬀerent values

of L. The dotted line shows the Newtonian potential

for comparision.

ticle sinks further in the potential and its orbit becomes

less elliptical. As more and more of the energy is taken

away, a stage is reached at which the particle is at the

minimum of the potential well and its orbit is circular.

If some of the angular momentum L is now removed

from the particle, the minimum moves closer to the cen-

tre. Then the circular orbit in which the particle settles

will have a smaller radius. One could continue with

this process until the angular momentum reduces to the

value L = 2

√

3GM. The circular orbit at this value

of L is known as the innermost stable circular orbit

(ISCO) which has the radius r

ISCO

= 6GM = 3R

Orbits with radius less than r

ISCO

are not possible

because as L reduces below 2

√

3GM, there is no po-

tential well. r

ISCO

therefore is the smallest possible

orbital radius for a given mass M of the black hole.

All our considration so far was for the Schwarzschild

case, which corresponds to a black hole which has mass

but no rotation, that is no spin angular momentum. For

spinning black holes, the space-time geometry is de-

scribed by the Kerr metric, which was discovered by

Roy Kerr in 1963. The Kerr case is far more compli-

cated than the Schwarzschild case and a large variety

of trajectories is possible. The simplest trajectories are

those corresponding to paths of particles moving in

a plane perpendicular the spin axis of the black hole.

Analysis of these trajectories shows that the ISCO for

1.3 Energy Extraction

the Kerr case occurs when the black hole has (1) the

maximum possible spin allowed by the constraints of

the Kerr geometry and the sense of the particle rotation

is the same as spin of the black. This tightest possible

circular orbit has the radius r

ISCO,Kerr

= GM. This

is just half the Schwarzschild radius of 2GM/c

(we

have introduced the value of c here but have c =1 in

all the expressions above) for a black hole with zero

spin. That does not matter since the r

ISCO,Kerr

re-

mains greater than the radius of the black hole for the

Kerr geometry (see BH1 for concepts related to the

radius of a black hole).

1.3 Energy Extraction

Consider a particle of mass m which is in a cir-

cular orbit around a Schwarzschild black hole with

orbital radius r

ISCO

. The particle has negative total

energy in this orbit. When the particle is at rest at large

distance, the total energy of the particle is simply its

rest mass energy mc

. Because a particle in a bound

orbit must have come from a great distance, energy

has been removed from it since the total energy in the

bound orbit is negative while it is mc

at a great dis-

tance. It can be shown that when a particle has fallen

to the Schwarzschild innermost stable circular orbit at

ISCO

, the energy extracted from the particle is 5.7

percent of its rest mass. For an extreme Kerr black

hole, this energy extracted is as high as 42 percent of

the rest mass of the particle. It is this extracted en-

ergy from matter rotating close to a black hole which

powers active galactic nuclei and X-ray binary systems.

The eﬃciency of energy release in this manner is much

greater than the eﬃciency of generating energy from

nuclear processes. In converting hydrogen all the way

to iron though nuclear fusion, the eﬃciency of energy

extraction is only about 0.8 percent. The mechanisms

through which the energy is extracted and used to power

energetic sources will be described in future black hole

stories.

About the Author

Professor Ajit Kembhavi is an emeritus

Professor at Inter University Centre for Astronomy

and Astrophysics and is also the Principal Investiga-

tor of the Pune Knowledge Cluster. He was the former

director of Inter University Centre for Astronomy and

Astrophysics (IUCAA), Pune, and the International

Astronomical Union vice president. In collaboration

with IUCAA, he pioneered astronomy outreach ac-

tivities from the late 80s to promote astronomy re-

search in Indian universities. The Speak with an

Astronomer monthly interactive program to answer

questions based on his article will allow young enthu-

siasts to gain profound knowledge about the topic.

Colour - Magnitude Diagram, Part-1

by Sindhu G

airis4D, Vol.2, No.1, 2024

www.airis4d.com

2.1 Introduction

A color-magnitude diagram (CMD) is a power-

ful tool in astronomy used to analyze and interpret the

characteristics of stellar populations within a particu-

lar region of the sky. This diagram plots the apparent

magnitude of stars against their color index, revealing

essential information about their properties, evolution-

ary stages, and distribution. Prior to delving into the

study of color-magnitude diagrams (CMDs), it is es-

sential to familiarize ourselves with the concepts of

magnitudes and color indices in the ﬁeld of astronomy.

One of the fundamental characteristics of stars that as-

tronomers study is their brightness, a quality that is

intricately linked to their magnitude. The brightness

of a star refers to the amount of light it emits and how

luminous it appears when observed from a speciﬁc van-

tage point, typically from Earth. It is a measure of the

intensity of the star’s radiation reaching an observer

and plays a crucial role in our perception of celestial

objects in the night sky.

2.2 Magnitude

Magnitude is a numerical scale used by astronomers

to quantify the brightness of celestial objects, includ-

ing stars. The concept of magnitude dates back to

ancient Greece, where the astronomer Hipparchus de-

veloped a system ranking stars from 1 to 6 based on

their apparent brightness, with 1 being the brightest

and 6 the faintest. Modern astronomers have reﬁned

and expanded this system.

Magnitude values lack a speciﬁc unit. The scale

Figure 1: Real Number Line Credit: H Padleckas

Figure 2: An illustration of light sources from mag-

nitude 1 to 3.5, in 0.5 increments Credit: CactiStacc-

ingCrane

operates on a logarithmic basis, ensuring that a star

with a magnitude of 1 is precisely 100 times brighter

than a star with a magnitude of 6. Objects possessing

a negative magnitude exhibit greater brightness com-

pared to those with a positive magnitude. The lower the

numerical value, the more luminous the object. Enti-

ties positioned further to the left on this scale manifest

increased brightness, while those positioned farther to

the right appear less radiant.

2.2.1 Apparent Magnitude(m)

The apparent magnitude of a star is a measure

of its brightness as observed from Earth. Apparent

magnitude is inﬂuenced by its intrinsic luminosity, dis-

tance from the observer, and the impact of extinction

diminishing its radiance. The lower the apparent mag-

2.3 Interstellar Extinction

Figure 3: Apparent magnitude Credit: Chelynne Cam-

pion/ EarthSky

Figure 4: Absolute magnitude Credit: Learn the Sky

LLC

nitude, the brighter the star appears in the night sky.

The brightest stars, like Sirius and Vega, have nega-

tive magnitudes, while fainter stars may have positive

magnitudes.

2.2.2 Absolute Magnitude(M)

Absolute magnitude(Fig: 4) accounts for a star’s

intrinsic brightness, providing a standard measure that

eliminates the eﬀects of distance. It represents how

bright a star would appear if it were located at a standard

distance of 10 parsecs (about 32.6 light-years) from

Earth.

Figure 5: Absolute magnitude Credit: Cosmos

Figure 6: Extinction and interstellar reddening Credit:

Cosmos

2.3 Interstellar Extinction

Interstellar extinction, also known simply as ex-

tinction, refers to the reduction in the apparent bright-

ness of celestial objects, such as stars, due to the ab-

sorption and scattering of light by interstellar dust and

gas. As light from a distant star travels through the vast

spaces between stars in our galaxy, it encounters tiny

particles and gas molecules present in the interstellar

medium.

Interstellar extinction involves two main processes:

absorption and scattering. Absorption occurs when

interstellar dust particles absorb speciﬁc wavelengths

of light, preventing them from reaching the observer.

Scattering, on the other hand, involves the redirection

of light in various directions, contributing to a diﬀuse

glow that may diminish the apparent brightness of the

observed object. The degree of extinction depends

on the wavelength of light. Shorter wavelengths (blue

light) are more strongly scattered and absorbed than

longer wavelengths (red light). As a result, stars may

appear redder than they actually are due to the prefer-

ential scattering of shorter wavelengths by interstellar

particles.

Interstellar extinction aﬀects the apparent magni-

tude of celestial objects. The observed brightness of

a star, for instance, may be dimmer than its intrinsic

brightness due to the attenuation caused by interstellar

dust and gas. Diﬀerent stars may experience varying

levels of extinction based on their distance from Earth,

the density of interstellar material along the line of

sight, and the speciﬁc characteristics of the intervening

dust clouds. This selective extinction can result in a

range of observed colors for stars.

2.4 Distance Modulus

Scientists and astronomers take interstellar extinc-

tion into account when studying distant celestial ob-

jects. Correcting for this phenomenon allows them

to estimate the intrinsic brightness, or absolute mag-

nitude, of stars and other astronomical entities accu-

rately. Understanding interstellar extinction is crucial

for interpreting observational data and obtaining more

accurate information about the properties of celestial

objects within our galaxy and beyond.

2.4 Distance Modulus

Absolute magnitude is derived from apparent mag-

nitude and distance using the formula:

m − M = 2.5 log





= 5 (log

d − 1)

This calculation takes into account the fact that

intensity diminishes in proportion to the square of the

distance, known as the distance modulus. Here, d

represents the distance to the star measured in parsecs,

m is the apparent magnitude, and M is the absolute

magnitude.

In instances where the line of sight between the

object and observer is impacted by extinction, result-

ing from the absorption of light by interstellar dust

particles, the apparent magnitude of the object will

consequently appear fainter. When accounting for A

magnitudes of extinction, the relationship between ap-

parent and absolute magnitudes adjusts to:

m − M = 5(log

d − 1) + A

”In the upcoming article, ’Color-Magnitude Di-

agram, Part-2,’ we will delve into the topic of color

indices and explore Color-Magnitude diagrams.”

References:

Magnitude (astronomy)

Absolute Magnitude

Apparent versus Absolute Magnitude

What is stellar magnitude?

Extinction

The Stellar Magnitude Scale

Astronomical Toolkit

About the Author

Sindhu G is a research scholar in Physics

doing research in Astronomy & Astrophysics. Her

research mainly focuses on classiﬁcation of variable

stars using diﬀerent machine learning algorithms. She

is also doing the period prediction of diﬀerent types

of variable stars, especially eclipsing binaries and on

the study of optical counterparts of X-ray binaries.

Part III

Biosciences

Odonates: Sensitive Indicators of Aquatic

Ecosystem Health and Pollution

by Geetha Paul

airis4D, Vol.2, No.1, 2024

www.airis4d.com

1.1 Introduction

Odonata, including dragonﬂies and damselﬂies,

are widely proposed as indicators of environmental

quality in aquatic ecosystems due to their sensitivity

to various pollutants and habitat conditions. Their

nymphs are aquatic, and they are used for bioassess-

ment and biomonitoring of aquatic habitats, includ-

ing the measure of biodiversity, waterbody health, and

the detection of biological impacts of climate warm-

ing. Odonata is sensitive to organic pollution, ther-

mal pollution, chemical pollution, habitat degradation,

and physicochemical changes in water bodies. Addi-

tionally, it has been observed that dragonﬂies tend to

be much more sensitive to pollution than damselﬂies.

The use of Odonata as bioindicators oﬀers several ad-

vantages, as they are widespread, well-studied, rela-

tively easy to observe and identify and highly depen-

dent on the ecological conditions of the environment.

Therefore, their response to diﬀerent types of pollu-

tion can provide valuable insights into the health of

aquatic ecosystems and freshwater quality. The diver-

sity and distribution of Odonata larvae are generally

inﬂuenced by alterations in the habitat structure, in-

creasing their potential to be applied as indicators for

environmental pollution in aquatic ecosystems. The

sensitivity of Odonata to various environmental fac-

tors is well-documented and their community structure

closely follows changes in the environment and habitat

settings. Therefore, Odonata serve as important in-

dicators of habitat degradation and water quality, and

their presence and diversity can be used to assess the

impact of human activities on freshwater ecosystems.

1.2 Alteration in Habitat Structure

Alteration in aquatic systems’ habitat structure

can signiﬁcantly impact Odonata populations. Habitat

loss and fragmentation can cause a decline in insect

populations, including Odonata. Changes in land use

can aﬀect dragonﬂy larval stages by altering within-

stream environmental conditions and adults by loss of

perches, shade, and hunting habitat. The reduction in

the number of habitable ponds can lead to a decline in

the connectivity of the landscape, which can result in

the reduction of the diversity of Odonata species. Ad-

ditionally, species-speciﬁc habitat requirements, pol-

lution, and mismanagement of water bodies can result

in what appears to be a dense network of water bodies

being largely unusable for some species. The habitat

must be carefully deﬁned, as many species exhibit id-

iosyncratic requirements of their environment, which

can render even the densest freshwater landscape un-

suitable. Therefore, alteration in habitat structure can

have far-reaching eﬀects on Odonata populations, and

monitoring their populations can provide valuable in-

sights into the health of aquatic ecosystems and the

quality of freshwater.

1.3 Habitat Degradation

Image courtesy: https://www.google.com/search?q=waterbody+habitat+loss&tbm=isch&chips=q:

waterbody+habitat+loss,online chips:reclamation:zpUpufqMyeg%3D&hl=e

Figure 1: Image shows the alteration in habitat struc-

ture in natural land and water systems.

1.3 Habitat Degradation

Habitat degradation can have signiﬁcant impacts

on Odonata populations. Changes in water pH, eu-

trophication, and siltation can aﬀect Odonata popu-

lations and other populations such as ﬁsh, molluscs,

and zooplankton. Land-use change can aﬀect drag-

onﬂy larval stages by altering within-stream environ-

mental conditions and adults by losing perches, shade,

and hunting habitat. Additionally, ecological deterio-

ration caused by any kind of stressor (pollution, habitat

degradation) can cause a decline in insect populations,

including Odonata. Human-caused habitat loss and

fragmentation have become major threatening factors

during the last decade, and several studies explore the

inﬂuence of habitat loss on terrestrial populations. Rel-

atively few studies focus on the relationship between

landscape changes and aquatic invertebrates. There-

fore, habitat degradation can have far-reaching eﬀects

on Odonata populations, and monitoring their popula-

tions can provide valuable insights into the health of

aquatic ecosystems and the quality of freshwater.

1.4 Eutrophication

High contents of organic matter and fertilisers

causing eutrophication can lead to algal and bacterial

blooms, decreasing dissolved oxygen levels in water

and potentially aﬀecting odonata species. The rich-

ness of Odonata species has been found to increase

Image courtesy:https://experts.illinois.edu/en/publications/

dragonﬂies-and-damselﬂies-model-organisms-for-ecological-and-ev

Figure 2: High contents of organic matter and fertilis-

ers causing eutrophication.

with dissolved oxygen, water temperature, and vegeta-

tion cover but decrease with eutrophication.

1.5 Persistent Organic Pollutants

Odonata larvae have been found to bioaccumu-

late persistent organic pollutants such as heavy metals

and PCBs (Polychlorinated biphenyls), which can im-

pact their ﬁtness and transfer contaminants to other

organisms via trophic interactions. Odonata larvae

can bioaccumulate persistent organic pollutants such

as heavy metals and PCBs, impacting their ﬁtness

and transferring contaminants to other organisms via

trophic interactions. For instance, a study investigated

the accumulation of persistent organic pollutants in

damselﬂy larvae and found that carbon nanoﬁbers are

bioaccumulated in Ishnura elegans (Odonata).

1.6 Agricultural Practices

Image courtesy: https://my.spokanecity.org/publicworks/wastewater/pcbs/

Figure 3: PCBs Bioaccumulate in the Food Web.

1.6 Agricultural Practices

Agricultural activities, such as soil erosion and the

runoﬀ of nutrients and pesticides, can majorly impact

Odonata survival, aﬀecting their ﬁtness and abundance.

Image courtesy: https://www.sciencedirect.com/science/article/pii/S026974912301833X

Figure 4: The combined eﬀects of pesticide exposure

and water temperature rise due to global warming ac-

celerates the decline of Odonata species in paddy ﬁelds.

Irrespective of all these, some exceptional species

of odonates are seen in polluted waters. The odonata

species commonly found in polluted waters include

Brachythemis contaminata, Zyxomma petiolatum,

Rhodothemis rufa and Ceriagrion cerinorubellum etc.

These species have been reported to be present abun-

dantly in polluted or contaminated water bodies, can

tolerate some degree of pollution, and are valuable en-

vironmental indicators. Overall, the presence of these

Odonata species in polluted water is a signiﬁcant obser-

vation in the context of water quality and environmental

health.

Figure 5: Odonata species commonly found in pol-

luted waters. (a) The image illustrates the high level of

pesticides ﬂoating in the water and a commonly seen

Brachythemis contaminata male species. (b) Contam-

inated water with discarded pesticide bottles.

1.7 Odonata Species Commonly Found in Freshwater Streams and Rivers

Figure 6: Some Odonata species found in pol-

luted water. 1. Brachythemis contaminata (m), 2.

Brachythemis contaminata (f), 3. Zyxomma petiola-

tum (m), 4. Zyxomma petiolatum (f), 5. Rhodothemis

rufa (m), 6. Rhodothemis rufa (f), 7. Ceriagrion ceri-

norubellum(m), 8. Ceriagrion cerinorubellum(f)

1.7 Odonata Species Commonly

Found in Freshwater Streams

and Rivers

Some other speciﬁc species of odonates are only

seen in freshwater streams and rivers (unpolluted wa-

ter). Some common ones are Euphaea fraseri, Neu-

robasis chinensis, Vestalis apicalis, Vestalis gracilis,

Heliocypha bisignata etc.

Figure 7: Some Odonata species found in unpolluted

fresh water. 1. Euphaea fraseri(m), 2. Euphaea

fraseri(f), 3. Neurobasis chinensis (m), 4. Neuroba-

sis chinensis (f), 5. Vestalis apicalis (m), 6. Vestalis

apicalis (f), 7. Vestalis gracilis (m), 8. Vestalis gra-

cilis (f), 9. Heliocypha bisignata (m), 10. Heliocypha

bisignata (f)

1.7 Odonata Species Commonly Found in Freshwater Streams and Rivers

Figure 8: (a) Euphaea fraseri and (b) Neurobasis chi-

nensis in their freshwater habitats

Figure 9: Stream ruby (Heliocypha bisignata) laying

eggs on wet rocks of a freshwater habitat.

The species shown in Figs. 8 to 9 are restricted to

unpolluted water and serve as indicators of good water

quality.

The upcoming article will include the taxonomic

description of the pollution indicator species men-

tioned in this paper.

References

Bried, J.T., Hager, B.J., Hunt, P.D., Fox, J.N.,

Jensen, H.J.& Vowels, K.M. (2012a).Bias of reduced-

eﬀort community surveys for adult Odonata of lentic

waters.Insect Conservation and Diversity, 5: 213–222.

https://discovery.ucl.ac.uk/id/eprint/10124182/1/icad.

12450.pdf

https://resjournals.onlinelibrary.wiley.com/doi/abs/

10.1111/j.1752-4598.2011.00156.x

https://www.ijirset.com/upload/2017/september/144

53 water%20paper N.pdf

Corbet, P.S. (1999) Dragonﬂies: Behaviour and

Ecology of Odonata. Cornell University Press, New

York.

Ameilia, Z.S., Salmah, M.R.C.&Hassan, A. (2006).

Distribution of Dragonﬂy (Odonata: Insecta) in the Ke-

rian River Basin, Kedah-Perek, Malaysia. USU Repos-

itory: 1-14.

https://www.nhbs.com/dragonﬂies-and-damselﬂies-book

https://www.google.com/search?q=waterbody+habitat+

loss&tbm=isch&chips=q:waterbody+habitat+loss,online

chips:reclamation:zpUpufqMyeg%3D&hl=e

https://www.sciencedirect.com/science/article/abs/

pii/S0048969712002574

About the Author

Geetha Paul is one of the directors of

airis4D. She leads the Biosciences Division. Her

research interests extends from Cell & Molecular Bi-

ology to Environmental Sciences, Odonatology, and

Aquatic Biology.

Introduction to Aging Clocks

by Jinsu Ann Mathew

airis4D, Vol.2, No.1, 2024

www.airis4d.com

Getting older, isn’t it weird? We change and grow

just like the seasons, sometimes gracefully, sometimes

with a few bumps and scrapes. But it’s not just about

wrinkles and grumbling (though those can be fun too!).

Aging is a grand adventure, a unique path for each of

us.

Aging refers to the gradual decline over time in the

physiological functions essential for survival and fer-

tility. The features of aging, distinct from age-related

diseases like cancer and heart disease, impact every

individual within a species. Aging is characterized by

a progressive loss of physiological integrity, resulting

in impaired function and heightened susceptibility to

death. At the biological level, aging results from the

impact of the accumulation of a wide variety of molecu-

lar and cellular damage over time. This results in a slow

decline in both physical and mental abilities, increas-

ing the chances of getting sick and eventually dying.

These changes don’t happen at a steady pace and aren’t

directly tied to a person’s age in years. This discussion

focuses on understanding aging and the emerging ﬁeld

that tries to measure it, known as ”Aging Clocks.”

2.1 Lifespan and Healthspan

The concept of aging is closely tied to two dis-

tinct terms that revolve around the passage of time:

healthspan and lifespan.

Lifespan: This term refers to the total length of

time a person is alive, from birth to death. Lifespan

is a quantitative measure that provides an overall as-

sessment of the duration of an individual’s existence.

It doesn’t distinguish between periods of health and

(image courtesy:https://stonechiro.com/blog/260052-healthspan-vs-lifespan)

Figure 1: Healthspan and Lifespan

periods of illness, oﬀering a broad perspective on the

entire span of life.

Healthspan: In contrast, healthspan focuses specif-

ically on the duration of an individual’s life during

which they enjoy good health and are free from signif-

icant diseases or disabilities. It represents the period

of time when an individual is physically and mentally

well, emphasizing the quality of life. Healthspan is

a qualitative measure, emphasizing the importance of

maintaining well-being and functionality throughout

the aging process. So, while lifespan measures the total

length of one’s life, healthspan hones in on the portion

of that life spent in good health(Figure 1). Both con-

cepts are valuable in understanding the complexities of

aging and overall well-being. Increasing healthspan is

a goal in ﬁelds like gerontology and public health, aim-

ing to enhance not only the quantity but also the quality

of life as people age. This highlights the demand for

universally applicable metrics, often termed ”clocks,”

capable of discerning and quantifying biological age

as distinct from chronological age. Thus, what exactly

do these terms, biological age and chronological age,

signify?

2.2 Telomere length

Chronological age is the straightforward count of

years since birth, while biological age delves into the

intricate details of the body’s wear and tear at diﬀer-

ent levels. Consider a 45-year-old man who prioritizes

a nutritious diet, engages in regular physical activity,

and practices stress management. His biological age

might be lower than his chronological age, reﬂecting

a well-maintained and resilient physiology. In con-

trast, another 45-year-old man with poor dietary habits,

sedentary behavior, and high stress levels may exhibit

a higher biological age, indicating greater strain on his

body despite being the same age chronologically. This

showcases how lifestyle choices can inﬂuence biolog-

ical age independent of the years lived. Think of it

this way: Chronological age is like the distance marker

on a road trip, while biological age is like the fuel

gauge in your car. They might not always match up

perfectly! Some people age faster (fuel burns quicker)

than others due to various factors like genes, lifestyle,

and environment.

Markers in Aging Clocks

How does an aging clock work? Scientists have

identiﬁed diﬀerent markers in our cells that change as

we age.These clocks utilize various molecular, cellular,

and physiological markers to estimate how quickly an

individual is aging. Here are some common markers

used in aging clocks:

2.2 Telomere length

Telomere length is a critical biological age marker

that plays a key role in understanding cellular aging

and overall health. Telomeres are structures located

at the ends of chromosomes, which are the thread-like

structures containing genetic material within a cell.

Think of telomeres as protective caps that prevent the

ends of chromosomes from fraying or sticking to each

other(Figure 2).

As cells divide, their telomeres naturally shorten.

This process is a normal part of cellular aging and is

associated with the biological clock of a cell. Once

the telomeres become critically short, cells may either

(image courtesy:https://www.metabolomicmedicine.com/english/telomere analysis-na-192.html )

Figure 2: Telomeres

stop dividing or undergo cellular senescence, a state in

which the cells remain metabolically active but cease to

divide. In some cases, cells with very short telomeres

may enter a state of apoptosis, or programmed cell

death.

The measurement of telomere length is used as an

indicator of cellular aging and, by extension, biological

age. Shorter telomeres are often associated with older

biological age and an increased risk of age-related dis-

eases. Conversely, individuals with longer telomeres

may be considered to have a younger biological age at

the cellular level.

2.3 DNA Methylation

Picture your DNA as a double helix, with each

strand made up of four chemical bases: A, T, C, and G.

Methylation adds a small methyl group speciﬁcally to

the cytosine (C) base(Figure 3), inﬂuencing how tightly

the DNA coils and ultimately how accessible genes are

for transcription (gene expression). As we age, the pat-

terns of methylation on our DNA change subtly. Some

genes become more methylated, silencing their expres-

sion, while others lose these methyl tags, potentially

becoming hyperactive. These changes can aﬀect vari-

ous cellular processes, impacting our health and aging

trajectory.

Unlike telomere length, which primarily reﬂects

cell division, DNA methylation patterns are inﬂuenced

by a wider range of factors, including genetics, envi-

2.4 Protein Levels

(image

courtesy:https://towardsdatascience.com/predicting-age-with-dna-methylation-data-99043406084)

Figure 3: DNA methylation

ronment, and lifestyle choices. This makes it a more

comprehensive gauge of biological age, potentially re-

vealing aspects of aging not captured by other markers.

Scientists can analyze DNA methylation patterns from

blood, tissue samples, or even saliva. By comparing

these patterns to reference databases or analyzing spe-

ciﬁc age-associated methylation changes, they can esti-

mate someone’s biological age and potentially predict

their risk for certain age-related diseases.

2.4 Protein Levels

Proteins, the tireless workhorses of our cells, play

a starring role in almost every biological process. It’s

no surprise then, that their levels and functions can

become indicators of our biological age. Imagine an

orchestra where each protein is a musician, playing a

speciﬁc tune crucial to the symphony of life. As we

age, the composition of this orchestra can shift. Some

proteins might become scarcer, like instruments tucked

away in dusty corners, while others might become over-

abundant, playing a discordant melody. These changes

in protein levels and their activity can impact cell func-

tion, metabolism, and ultimately, our health and sus-

ceptibility to age-related diseases.

Scientists can measure the levels and activity of

various proteins in blood, tissue samples, or even saliva.

By analyzing these protein proﬁles, they can identify

speciﬁc patterns associated with diﬀerent age groups

or even predict individual aging rates. Proteins are di-

rectly involved in cellular processes, oﬀering a closer

reﬂection of biological function compared to some

(image courtesy:https://www.physio-pedia.com/Insulin Resistance)

Figure 4: Insulin resistance

other markers. Additionally, analyzing speciﬁc pro-

tein panels can provide insights into speciﬁc aspects of

aging, like inﬂammation or metabolism.

2.5 Metabolic Markers

Metabolic markers of aging encompass a range

of indicators reﬂecting changes in the body’s energy

utilization and storage processes as individuals grow

older. One signiﬁcant metabolic marker is insulin re-

sistance, where cells become less responsive to insulin,

leading to elevated blood sugar levels(Figure 4). This

phenomenon is particularly relevant in the context of

age-related metabolic disorders like type 2 diabetes.

Alterations in glucose metabolism, including impaired

glucose tolerance and insulin sensitivity, are indicative

of age-related metabolic dysregulation and contribute

to the progression of metabolic disorders.

Lipid proﬁles serve as crucial metabolic markers,

highlighting changes in cholesterol levels and triglyc-

erides associated with aging. Dyslipidemia, character-

ized by abnormal lipid levels, becomes more prevalent

with age and is recognized as a signiﬁcant risk factor for

cardiovascular diseases. Another aspect of metabolic

aging involves mitochondrial function, as these cellular

powerhouses experience age-related decline. Changes

in mitochondrial function can impact energy produc-

tion and contribute to oxidative stress, playing a role in

the aging process and the development of age-related

diseases.

2.6 Immune System Markers

Inﬂammation emerges as a pervasive metabolic

marker associated with aging, leading to the concept

of inﬂammaging. Chronic, low-grade inﬂammation

contributes to various age-related conditions, including

metabolic disorders and cardiovascular diseases. Ad-

ditionally, hormonal changes and shifts in body compo-

sition, such as increased visceral fat and decreased lean

muscle mass, serve as metabolic markers that further

underscore the complexity of metabolic aging. Un-

derstanding these markers provides insights into the

intricate interplay between metabolism and aging, of-

fering avenues for interventions aimed at promoting

healthy aging and mitigating the risks associated with

age-related metabolic disorders.

2.6 Immune System Markers

The aging process signiﬁcantly inﬂuences the im-

mune system, and various markers reﬂect these changes.

One key aspect is the decline in T-cell function, where

the production of new T cells diminishes, aﬀecting the

body’s ability to respond eﬀectively to new pathogens.

Similarly, B-cell function undergoes alterations, im-

pacting antibody diversity and potentially limiting the

immune system’s ability to recognize and combat a

broad spectrum of pathogens. The emergence of chronic,

low-grade inﬂammation, known as inﬂammaging, con-

tributes to the release of pro-inﬂammatory cytokines,

inﬂuencing immune responses and increasing the risk

of age-related diseases. Changes in natural killer (NK)

cell activity and phagocytic function further contribute

to the complexity of immune aging, aﬀecting the body’s

ability to defend against infections and maintain over-

all health. Additionally, age-related shifts in vaccine

responsiveness and an increased predisposition to au-

toimmune conditions highlight the intricate relation-

ship between immune system markers and the aging

process.

Eﬀorts to comprehend and address these immune

system markers are crucial for developing strategies to

support healthy aging and enhance immune function in

older individuals. Research in this ﬁeld aims to unravel

the mechanisms behind immunosenescence, paving the

way for interventions that may mitigate age-related de-

clines in immune responses, improve vaccination ef-

ﬁcacy, and reduce the susceptibility to infections and

inﬂammatory disorders in the elderly.

2.7 Conclusion

In conclusion, our exploration into aging and the

markers utilized in aging clocks unveils the intricate

interplay of biological processes that deﬁne the aging

trajectory. From epigenetic changes like DNA methy-

lation to the dynamic shifts in protein levels and the

nuanced alterations in immune system function, these

markers oﬀer glimpses into the complex landscape of

aging. Understanding and measuring biological age

through these markers not only enhances our ability

to predict health outcomes but also opens avenues for

targeted interventions promoting healthy aging. As

we conclude this discussion, it’s important to acknowl-

edge that the ﬁeld of aging research is dynamic and

continually evolving. Our exploration has touched

upon several key aspects, yet there are numerous other

facets to consider. Future articles will delve deeper into

emerging research, exploring topics such as the role of

lifestyle factors, genetic inﬂuences, and advancements

in technology that contribute to reﬁning our under-

standing of aging and the development of more precise

aging clocks.

References

Aging and aging-related diseases: from molec-

ular mechanisms to interventions and treatments

Aging: The Biology of Senescence

Ageing and health

The Hallmarks of Aging

How do we measure aging?

Aging Clocks

TELOMERE ANALYSIS

Predicting Age with DNA methylation data

2.7 Conclusion

About the Author

Jinsu Ann Mathew is a research scholar

in Natural Language Processing and Chemical Infor-

matics. Her interests include applying basic scientiﬁc

research on computational linguistics, practical appli-

cations of human language technology, and interdis-

ciplinary work in computational physics.

Part IV

General

Unveiling the Cosmos: The Rise of

Smartphone Astrophotography and the Role

of Artiﬁcial Intelligence and Machine

Learning

by Aditya Kinjawadekar

airis4D, Vol.2, No.1, 2024

www.airis4d.com

Smartphone astrophotography has gained immense

popularity among amateur astronomers, oﬀering a con-

venient means to capture celestial beauty with a pocket-

sized device. This exploration delves into the world of

smartphone astrophotography, focusing on how sig-

niﬁcant brands utilize advanced technologies, such as

machine learning and artiﬁcial intelligence, to enhance

the capabilities of their astrophotography modes.

One of the primary drivers behind the growing

interest in smartphone astrophotography is the sheer

simplicity of these devices. Smartphones have be-

come integral to our daily lives, featuring user-friendly

interfaces and uncomplicated controls. Unlike tra-

ditional telescopes and specialized astrophotography

equipment, smartphones are portable, lightweight, and

readily available to a broad audience. This simplicity

makes them an ideal tool for individuals who may not

have extensive knowledge of astronomy or the techni-

cal know-how required for traditional astrophotography

setups.

Furthermore, the advancements in smartphone

camera technology have played a pivotal role in fuel-

ing the rise of smartphone astrophotography. Modern

smartphones are equipped with high-quality cameras,

boasting impressive resolution, low-light capabilities,

and sophisticated image processing features. These ad-

vancements enable users to capture clear, detailed im-

ages of celestial objects without requiring specialized

equipment. The heart of the article revolves around

integrating machine learning algorithms and artiﬁcial

intelligence features into smartphone cameras. Dif-

ferent brands, including industry giants like Apple,

Samsung, and Google, employ advanced computa-

tional techniques to overcome the inherent limitations

of smartphone sensors. Machine learning algorithms

aid in noise reduction, sharpening details, and optimiz-

ing exposure settings, thereby elevating the quality of

astrophotographs.

The article also sheds light on the speciﬁc fea-

tures implemented by each brand. For instance, Ap-

ple’s Deep Fusion technology utilizes multiple expo-

sures to enhance image quality, Samsung’s Astro mode

employs AI-driven scene recognition, and Google’s

Astrophotography mode leverages long-exposure pro-

cessing to capture intricate details of the night sky.

1.1 Apple

Apple utilizes sophisticated Artiﬁcial Intelligence

(AI) and Machine Learning (ML) techniques to en-

hance nighttime low-light images, ensuring that users

can capture stunning and detailed photos even in chal-

1.2 Google

Figure 1: Simple handheld shot showing the Sagittar-

ius constellation and a hint of the Milky Way galaxy

Figure 2: Longer exposure image shot with intelligent

astrophotography mode in Samsung devices

Figure 3: Google Pixel Astrophotography modes

lenging lighting conditions. The critical technology

driving this enhancement is Apple’s innovative com-

putational photography approach.

The Night Mode is one notable feature in Apple

devices that contributes to improved low-light photog-

raphy. Enabled by AI and ML algorithms, Night Mode

automatically activates in low-light scenarios, allow-

ing the camera to capture more light over an extended

period. The device takes a series of short and long

exposure shots. Then, using advanced algorithms, Ap-

ple’s software combines these images to create a ﬁnal

photo with reduced noise, enhanced details, and well-

balanced exposure.

AI determines optimal exposure settings based on

the scene and ambient lighting conditions. The de-

vice’s AI system intelligently analyzes the scene in

real time, identifying elements such as the presence of

people, movement, or the overall composition. This

information is then used to adjust the exposure dura-

tion for each part of the image, ensuring that essential

details are preserved and the photo appears natural.

1.2 Google

The Google Pixel’s Astrophotography mode stands

out as a stellar example of how Artiﬁcial Intelligence

(AI) and Machine Learning (ML) revolutionize smart-

phone photography, particularly in capturing mesmer-

izing images of the night sky. This innovative mode

allows users to eﬀortlessly capture detailed and breath-

taking shots of celestial wonders.

When Astrophotography mode is activated on a

Google Pixel device, it employs a combination of AI

and ML algorithms to optimize the camera settings

for capturing the night sky. The primary challenge in

astrophotography is dealing with low-light conditions,

and Google’s solution revolves around the intelligent

utilization of computational photography.

The mode works by taking a series of long-exposure

shots, capturing light over an extended period. During

this time, the camera’s AI system comes into play, an-

alyzing the scene in real time. It identiﬁes various

elements, such as stars, constellations, and other celes-

tial objects. Additionally, the AI considers the overall

composition, ambient lighting, and any potential move-

ment in the frame.

Machine learning plays a crucial role in the abil-

ity of astrophotography mode to adapt and improve

over time. Google has trained its algorithms on a mas-

sive dataset of astrophotographic images, allowing the

AI to recognize patterns and reﬁne its understanding of

celestial scenarios. As users engage with the Astropho-

tography mode, the ML algorithms continuously learn

and enhance their ability to optimize camera settings

for various nighttime scenes.

One remarkable aspect of Google’s approach is

its emphasis on reducing noise and maintaining image

clarity. The ML algorithms distinguish between gen-

uine celestial details and unwanted noise, ensuring that

the ﬁnal image reﬂects the true beauty of the night sky

with minimal interference.

The result is an astrophotograph that is visually

stunning and represents a harmonious blend of ad-

vanced technology and artistic expression. By har-

nessing the power of AI and ML, Google has made

astrophotography accessible to a broader audience, al-

(image courtesy:https://www.billing-coding.com/full-article.cfm?articleID=6066 )

Figure 4: Images shot by the author on a smartphone

device. (Samsung Galaxy S23 Ultra)

lowing users to capture the wonders of the cosmos with

a simple tap on their Pixel devices. This mode show-

cases how the marriage of cutting-edge technology and

computational prowess can elevate smartphone pho-

tography to new heights, turning every user into an am-

ateur astronomer capable of capturing the night sky’s

brilliance.

1.3 Samsung

Samsung, not to be left behind, has added some

unique features to its astrophotography tools. Apart

from stacking images like Google, Samsung adds an

Augmented Reality (AR) star map on the live camera

view. This helps beginners identify constellations and

frame their shots better. Depending on the time, loca-

tion, and date, the AR star map changes to match the

real stars at your location. Its high focal length zoom

1.3 Samsung

capabilities set Samsung apart from the other brands.

The Moon Shot mode is a standout feature from Sam-

sung, showcasing the brand’s commitment to pushing

boundaries. Powered by AI, Moon Shot allows users

to capture detailed shots of the moon at an impressive

100x zoom. The AI algorithms intelligently sharpen

these extreme zoomed-in shots, ensuring clarity and

precision in capturing lunar details. The engine devel-

oped by the Galaxy team recognizes the moon as an

object based on a range of moon shapes and details.

It recognizes everything from full to crescent moon.

When the AI model has completed its learning, it can

detect the area occupied by the moon, even in images

that weren’t used in training. However, if clouds ob-

scure the moon, the camera will not recognize it.

When the image of the moon is at an appropriate

level, you can press the capture button. The camera

will then take several steps to deliver a bright, clear

image of the moon.

First, Scene Optimiser uses AI processing to con-

ﬁrm whether to apply the detail enhancement engine.

The camera then takes multiple pictures and syn-

thesizes them into a single bright image with reduced

noise.

Once multi-frame processing has taken place, the

Galaxy camera uses Scene Optimiser’s deep learning-

based AI detail enhancement engine to eliminate any

remaining noise and enhance the details of the image

even further.

In conclusion, the integration of machine learn-

ing and artiﬁcial intelligence has signiﬁcantly trans-

formed smartphone astrophotography, making the ex-

ploration and capture of the cosmos more accessible

to enthusiasts. The continuous advancements in tech-

nology suggest a promising future at the crossroads

of smartphones and astrophotography, potentially un-

locking uncharted territories for amateur astronomers

across the globe.

About the Author

Aditya Kinjawadekar is an electronics en-

gineer with an interest in astronomical instrumenta-

tion. He works at the IUCAA instrumentation lab in

the domain of optics and imaging detectors. Aditya

is an award-winning astrophotographer, and his work

has been recognized and published by ISRO, NASA,

and ESA.

The Lunar Occultation of Antares

by Atharva Pathak

airis4D, Vol.2, No.1, 2024

www.airis4d.com

Figure 1: Jyotirvidya Parisanstha’s invitation poster

on Lunar Occultation of Antares

On Monday, the 5th of February 2024, about

04:46 (IST), the Moon will pass in front of Antares (Al-

pha Scorpii), creating a lunar occultation visible from

some parts of Asia. Because the Moon is so close to

the Earth that its position in the sky varies by as much

as two degrees worldwide, the occultation will be vis-

ible from only a limited region of the planet, shown

in Figure 2. Separate contours show where the disap-

pearance of Antares (Alpha Scorpii) is visible (shown

in red) and where its reappearance is visible (shown

in blue). Solid contours show where each event will

(image courtesy:

https://in-the-sky.org/news/occultations/occultation 20240205 0105 visibility.svg)

Figure 2: The locations from where the occultation is

visible

likely be visible through binoculars at a reasonable al-

titude in the sky. Dotted contours indicate where each

event occurs above the horizon but may not be visible

due to the sky being too bright or the Moon being very

close. The occultation will be visible from the western

coast of India. If viewed from Pune, it will begin with

the disappearance of Antares (Alpha Scorpii) behind

the Moon at 04:46 IST in the south-eastern sky at an

altitude of 24.1 degrees. Its reappearance will be visi-

ble at 05:59 IST at 35.7 degrees. There will be slight

diﬀerences in the degrees depending on your latitude.

About the Author

Atharva Pathak is an Astronomer, Soft-

ware Engineer & Data Manager for the Pune Knowl-

edge Cluster, Pune. He is a life member of India’s Old-

est association of Amateur Astronomers, Jyotirvidya

Parisanstha, and looks after the IOTA-India Occulta-

tion section as a webmaster and data curator.

Part V

Fiction

Perishing Without Publishing

by Ninan Sajeeth Philip

airis4D, Vol.2, No.1, 2024

www.airis4d.com

[This is a story known to many and often quoted

for encouraging students to publish their work before

someone else does!]

Once upon a time, in the lush landscapes of Ker-

ala, a tale unfolded that echoed the whispers of New-

tonian physics long before Sir Isaac Newton himself

penned his renowned laws. In a village nestled amidst

swaying coconut trees, the locals’ curious minds delved

into the universe’s mysteries.

Our story begins with an unnamed scientist, a sage

of his time, who possessed an innate understanding of

the laws governing motion. The people marvelled at

his wisdom, acknowledging that he had unravelled the

secrets of nature centuries before the rest of the world.

Yet, this extraordinary scholar remained hidden in the

shadows, secluded in his pursuit of knowledge.

One fateful day, under the shade of a towering

coconut palm, the scientist conducted an experiment

that would forever etch his name into the annals of

science. He pondered the forces at play as he gazed

up at the swaying coconuts. It was not an apple that

tumbled from above but a ripe coconut guided by the

invisible hand of gravity.

The coconut descended with a thud, striking the

scientist squarely on the head. Instead of heralding

a moment of enlightenment, the impact left him un-

conscious. News of the incident spread like wildﬁre

through the village, and the villagers rushed to the

fallen sage.

Despite their eﬀorts, the scientist remained in a

deep slumber, unable to share the profound insights

he had uncovered. The village mourned the loss of

a brilliant mind, realising that the world would never

know of their sage’s discoveries.

As time passed, the coconut incident faded into

local lore, and the secrets of Kerala’s Newton were

conﬁned to whispers among the villagers. The vil-

lage, surrounded by the towering coconut trees that

witnessed the event, became a place of mystery and

wonder.

The story of the scientist who perished without

publishing became a cautionary tale, reminding the

world that knowledge, even when discovered, could

be lost if not shared with the broader community. The

coconut tree that bore witness to this untold story stood

tall, its fronds weaving a silent narrative of a brilliant

mind lost to time.

And so, hidden in the heart of Kerala, the village

held a unique place in the history of science—a place

where the Newton’s laws of motion were known, but

where the scientist himself vanished, leaving only the

rustle of coconut leaves to echo his silent discoveries.

About the Author

Professor Ninan Sajeeth Philip is a Vis-

iting Professor at the Inter-University Centre for As-

tronomy and Astrophysics (IUCAA), Pune. He is also

an Adjunct Professor of AI in Applied Medical Sci-

ences [BCMCH, Thiruvalla] and a Senior Advisor for

the Pune Knowledge Cluster (PKC). He is the Dean

and Director of airis4D and has a teaching experience

of 33+ years in Physics. His area of specialisation is

AI and ML.

About airis4D

Artiﬁcial Intelligence Research and Intelligent Systems (airis4D) is an AI and Bio-sciences Research Centre.

The Centre aims to create new knowledge in the ﬁeld of Space Science, Astronomy, Robotics, Agri Science,

Industry, and Biodiversity to bring Progress and Plenitude to the People and the Planet.

Vision

Humanity is in the 4th Industrial Revolution era, which operates on a cyber-physical production system. Cutting-

edge research and development in science and technology to create new knowledge and skills become the key to

the new world economy. Most of the resources for this goal can be harnessed by integrating biological systems

with intelligent computing systems oﬀered by AI. The future survival of humans, animals, and the ecosystem

depends on how eﬃciently the realities and resources are responsibly used for abundance and wellness. Artiﬁcial

intelligence Research and Intelligent Systems pursue this vision and look for the best actions that ensure an

abundant environment and ecosystem for the planet and the people.

Mission Statement

The 4D in airis4D represents the mission to Dream, Design, Develop, and Deploy Knowledge with the ﬁre of

commitment and dedication towards humanity and the ecosystem.

Dream

To promote the unlimited human potential to dream the impossible.

Design

To nurture the human capacity to articulate a dream and logically realise it.

Develop

To assist the talents to materialise a design into a product, a service, a knowledge that beneﬁts the community

and the planet.

Deploy

To realise and educate humanity that a knowledge that is not deployed makes no diﬀerence by its absence.

Campus

Situated in a lush green village campus in Thelliyoor, Kerala, India, airis4D was established under the auspicious

of SEED Foundation (Susthiratha, Environment, Education Development Foundation) a not-for-proﬁt company

for promoting Education, Research. Engineering, Biology, Development, etc.

The whole campus is powered by Solar power and has a rain harvesting facility to provide suﬃcient water supply

for up to three months of drought. The computing facility in the campus is accessible from anywhere through a

dedicated optical ﬁbre internet connectivity 24×7.

There is a freshwater stream that originates from the nearby hills and ﬂows through the middle of the campus.

The campus is a noted habitat for the biodiversity of tropical Fauna and Flora. airis4D carry out periodic and

systematic water quality and species diversity surveys in the region to ensure its richness. It is our pride that

the site has consistently been environment-friendly and rich in biodiversity. airis4D is also growing fruit plants

that can feed birds and provide water bodies to survive the drought.