Cover page

Image: M51, the ”Whirlpool” galaxy in the constellation Canes Venatici, is about 30 million light years [10Mpc]

away from our galaxy.

Read more: https://www.astro.princeton.edu/

∼

rhl/PrettyPictures/

Managing Editor Chief Editor Editorial Board Correspondence

Ninan Sajeeth Philip Abraham Mulamoottil K Babu Joseph The Chief Editor

Ajit K Kembhavi airis4D

Geetha Paul Thelliyoor - 689544

Arun Kumar Aniyan India

Sindhu G

Journal 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.3, No.10, 2025

www.airis4d.com

The October edition begins with the article

“AlphaFold: Unlocking the Secrets of Life’s Building

Blocks” by Dr Arun Aniyan, which highlights how

Google DeepMind’s AlphaFold has revolutionised

biology by solving the long-standing protein folding

problem. Proteins, essential molecular machines of

life, function based on their unique 3D structures,

but determining these structures had traditionally

been slow, costly, and complex. AlphaFold, using

advanced deep learning and evolutionary data, can

now predict protein shapes with remarkable accuracy,

earning it the 2024 Nobel Prize in Chemistry.

This breakthrough is transforming drug discovery,

enabling faster understanding of disease mechanisms,

designing new enzymes, advancing material science,

and even paving the way for personalized medicine.

While experimental validation remains important,

AlphaFold has democratized access to structural

biology, accelerating innovation across science and

medicine. The article concludes that AlphaFold marks

a new era of biological research, with AI set to play an

ever-growing role in unlocking life’s deepest mysteries

and driving a global scientiﬁc revolution.

The article “Entropy Across Scales: From

Micro-Level Randomness to Macro-Level Order” by

Jinsu Ann Mathew explores entropy as a unifying

principle across physics, language, biology, and social

systems. It explains how local randomness—whether

in molecular motion, letter sequences, genetic

mutations, or individual choices—scales up to produce

predictable patterns and order at higher levels, such as

thermodynamic laws, meaningful discourse, resilient

ecosystems, and structured social dynamics. In physics,

entropy bridges molecular chaos and thermodynamic

stability; in language, it balances predictability and

creativity; in biology, it links genetic variation to

ecosystem resilience; and in society, it connects

individual unpredictability to collective patterns. The

article concludes that entropy is not merely a measure

of disorder but a universal framework that shows

how small-scale uncertainty drives large-scale order,

adaptability, and coherence across diverse domains.

Abishek’s article “Plasma Physics Comets

explores how plasma physics helps explain the dynamic

behaviour of comets as they interact with the Sun

and solar wind. Since plasma makes up most of

the universe, its role in space environments—from

solar ﬂares to cometary activity—is crucial. Comets,

originating from the Kuiper Belt and Oort Cloud,

consist of nuclei of ice, dust, and rock that release

gas and dust when heated by the Sun, forming comae,

hydrogen envelopes, and distinct plasma and dust

tails. The study details key plasma processes such

as outgassing, ionisation, dust–plasma interactions,

and solar wind coupling, which generate large-scale

structures like bow shocks and ion tails. Insights

from missions like Rosetta, Stardust, and Deep Impact

have revealed complex plasma environments, comet

morphology, and their role in delivering volatiles

and organics to planets. While challenges remain

in modelling dusty plasmas and capturing their

evolving behaviour, upcoming missions like ESA’s

Comet Interceptor promise breakthroughs. The article

concludes that comets serve as natural laboratories for

plasma physics, oﬀering vital knowledge for planetary

science, astrophysics, and understanding the early solar

system.

The article “X-ray Astronomy: Through Missions”

by Aromal P. highlights the rapid progress in X-ray

astronomy in the 2020s through landmark satellite

missions. It begins with IXPE (Imaging X-ray

Polarimetry Explorer), launched in 2021 by NASA and

ASI, which pioneered X-ray polarimetry and revealed

the magnetic ﬁeld structures of black holes, magnetars,

pulsars, and supernova remnants. XRISM, launched

in 2023 by JAXA in collaboration with NASA and

ESA, advanced high-resolution X-ray spectroscopy

with its Resolve microcalorimeter and Xtend wide-

ﬁeld telescope, uncovering the complex dynamics in

supernova remnants and microquasars. India’s XPoSat,

launched in 2024, marked the country’s ﬁrst dedicated

X-ray polarimetry mission, equipped with POLIX and

XSPECT instruments to study polarisation and spectral

evolution of cosmic sources. China’s Einstein Probe,

also launched in 2024, uses innovative lobster-eye

optics and complementary telescopes to detect fast

X-ray transients and contribute to multi-messenger

astronomy. Together, these missions are revolutionising

high-energy astrophysics by probing magnetic ﬁelds,

plasma environments, and transient cosmic events,

opening new frontiers in our understanding of the

universe.

Robin Thomas discusses the article “Introduction:

Galaxies in Motion”, how the environment shapes the

evolution of barred galaxies, drawing on the study by

Virginia Cuomo and collaborators on the Virgo Cluster

and the surrounding cosmic web. Bars—elongated

stellar structures in disk galaxies—play a crucial role in

redistributing gas and stars, inﬂuencing star formation

and galaxy dynamics. The study shows that galaxies

in dense environments like the Virgo Cluster have

shorter, less prominent bars due to tidal interactions,

gas stripping, and dynamical friction, which hinder bar

growth. In contrast, galaxies in ﬁlaments and especially

in the ﬁeld, where disruptive forces are weaker, retain

more gas and form larger, more prominent bars. These

ﬁndings highlight the signiﬁcant role of the cosmic

environment in determining galaxy morphology and

evolution, oﬀering deeper insights into how galaxies

interact with the cosmic web over time.

Sindhu G explains in “Main Sequence Stars”

the structure, classiﬁcation, energy generation, and

signiﬁcance of stars on the main sequence—the stage

where stars spend most of their lifetimes. A star’s

position on the sequence is determined mainly by its

mass, which dictates its temperature, luminosity, and

lifespan. Massive O- and B-type stars are hot, luminous,

and short-lived, while faint M-type red dwarfs are long-

lived and form the majority of stars in the galaxy. Main

sequence stars maintain stability through hydrostatic

equilibrium, with energy produced by hydrogen fusion

via the proton–proton chain in low-mass stars and the

CNO cycle in massive stars. Energy is transported

by radiation or convection, depending on stellar mass.

Stars are classiﬁed into spectral types O, B, A, F, G,

K, and M, each with distinct properties and lifetimes

ranging from millions to trillions of years. Once core

hydrogen is exhausted, stars evolve into red giants or

more complex end stages. Main-sequence stars are vital

for astrophysics, serving as standard candles, models

for stellar evolution, and hosts for exoplanets. They

remain central to understanding cosmic structure and

the search for habitable worlds.

Geetha Paul highlights in the article “BLAST

and FASTA: Cornerstones of Sequence Alignment in

Biosciences”, the critical role of sequence alignment

in bioinformatics and how heuristic tools like FASTA

and BLAST transformed the ﬁeld. Early exhaustive

algorithms like Smith–Waterman were accurate but

computationally impractical for large databases, leading

to the development of faster, word-based methods.

FASTA, introduced in the 1980s, was the ﬁrst widely

used heuristic alignment tool, known for its sensitivity

in detecting distant sequence similarities. BLAST,

developed in 1990, advanced the approach with greater

speed, statistical rigor, and variants such as BLASTN,

BLASTP, and BLASTX for diﬀerent biological queries.

While BLAST is typically faster and widely used in

large-scale genome annotation and routine database

searches, FASTA remains valuable for its sensitivity

and versatility in research-focused analyses. Together,

these tools underpin modern genomics, proteomics,

evolutionary biology, and biomedical research by

enabling eﬃcient, accurate detection of functional and

iii

evolutionary relationships among DNA, RNA, and

protein sequences. Their enduring relevance makes

them foundational in both laboratory practice and

bioinformatics education. T he article “Minimising

Synchronisation Overhead in Parallel Computing” by

Ajay Vibhute examines the performance challenges

caused by synchronisation in multi-core and many-

core systems. While synchronisation tools like

locks, barriers, and critical sections are essential

for correctness when threads share data, they often

introduce contention, blocking, context switching, and

idle time that hinder scalability. Issues such as false

sharing and barrier synchronisation can further degrade

eﬃciency, with Amdahl’s Law highlighting how

even small synchronised sections cap overall speedup.

To address this, the article outlines key strategies:

avoiding shared state through data partitioning or

message passing, using lock-free algorithms with

atomic operations, reducing reliance on barriers,

batching or merging synchronisation events, and

applying ﬁne-grained locking. These techniques

maximise concurrency, reduce contention, and improve

throughput, making parallel programs more scalable

and easier to maintain. The article emphasises that

careful synchronisation management is central to

unlocking the full potential of parallel computing.

News Desk - airis4D mentoring session

The airis4D mentoring program started with Dr Balamurali, Distinguished Scientist, TCS research, Bangalore

and the Dr Arun Kumar, Cheief Architect, Deep Alert, UK leading the interactive sessions. Students from the

Computer Science and Electronics departments of Ranni St Thomas College and Engineering College,

Kalloopara participated.

Contents

Editorial ii

I Artiﬁcial Intelligence and Machine Learning 1

1 AlphaFold: Unlocking the Secrets of Life’s Building Blocks 2

1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

1.2 Protien Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

1.3 Unfolding with AI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

1.4 Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

1.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

2 Entropy Across Scales: From Micro-Level Randomness to Macro-Level Order 6

2.1 Physics: From Molecules to Thermodynamics . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

2.2 Language: From Letters to Discourse . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

2.3 Biology: From Genes to Ecosystems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

2.4 Social Systems: From Individual Choices to Collective Patterns . . . . . . . . . . . . . . . . . . 8

2.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

II Astronomy and Astrophysics 10

1 Plasma Physics & Comets 11

1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

1.2 Structure of Comet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

1.3 Plasma processes in Comets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

1.4 Observational Insights from Space Missions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

1.5 Challenges and Opportunities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

1.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

2 X-ray Astronomy: Through Missions 16

2.1 Satellites in 2020s . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

3 Introduction: Galaxies in Motion 20

3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

3.2 Examining the Role of Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

3.3 Key Findings: Bars in Diﬀerent Environments . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

3.4 What’s Behind These Diﬀerences? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

3.5 Connecting the Dots: Environmental Impact on Galaxy Evolution . . . . . . . . . . . . . . . . . 22

3.6 Why Bars Matter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

3.7 Conclusion: A Deeper Understanding of Galaxy Evolution . . . . . . . . . . . . . . . . . . . . . 22

4 Main Sequence Stars 24

CONTENTS

4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

4.2 The Hertzsprung - Russell Diagram and the Main Sequence . . . . . . . . . . . . . . . . . . . . . 24

4.3 Stellar Structure and Energy Generation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

4.4 Classiﬁcation of Main Sequence Stars . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

4.5 Lifetimes of Main Sequence Stars . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

4.6 End of Main Sequence Phase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

4.7 Importance of Main Sequence Stars . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

4.8 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

III Biosciences 27

BLAST and FASTA: Cornerstones of Sequence Alignment in Biosciences and

Bioinformatics: 28

1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

1.2 What is FASTA? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

1.3 FASTA as a Format: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

IV Computer Programming 32

1 Minimizing Synchronization Overhead in Parallel Computing 33

1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

1.2 Why Synchronization Hurts Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

1.3 Reducing Synchronization Overhead . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

1.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

vii

Part I

Artiﬁcial Intelligence and Machine Learning

AlphaFold: Unlocking the Secrets of Life’s

Building Blocks

by Arun Aniyan

airis4D, Vol.3, No.10, 2025

www.airis4d.com

1.1 Introduction

Imagine a world where we can understand diseases

at their most fundamental level, design new medicines

with unprecedented precision, and even create novel

materials with properties never before seen. This might

sound like science ﬁction, but thanks to groundbreaking

advancements in artiﬁcial intelligence, particularly with

tools like Google’s AlphaFold, this future is rapidly

becoming a reality. AlphaFold isn’t just another tech

gadget; it’s a revolutionary leap in our ability to

understand the very building blocks of life: proteins.

AlpahFold also won the Nobel Prize for Chemistry in

2024.

1.2 Protien Structure

To truly appreciate the impact of AlphaFold, we

ﬁrst need to understand what proteins are and why their

structure is so important. Think of proteins as the

tiny, molecular machines that run our bodies. They are

involved in almost every biological process imaginable,

from digesting our food and ﬁghting oﬀ infections to

building our tissues and transmitting signals in our

brains. Each protein is made up of a long chain of

smaller units called amino acids, linked together like

beads on a string. What makes proteins so remarkable

is that these chains don’t just stay straight; they fold

into incredibly complex and speciﬁc three-dimensional

shapes. It’s this unique 3D shape that dictates a

protein’s function. If a protein isn’t folded correctly, it

cannot perform its function, and this misfolding is often

at the root of many diseases, including Alzheimer’s,

Parkinson’s, and certain cancers.

For decades, scientists have been trying to ﬁgure

out these intricate protein shapes. This challenge,

known as the ”protein folding problem,” has been

one of the grandest and most enduring mysteries in

biology. Why is it so diﬃcult? Because even though

the chain of amino acids is linear, the number of

ways it can theoretically fold is astronomically large –

more possibilities than there are atoms in the universe!

Traditionally, determining a protein’s structure has been

a painstaking and time-consuming process, relying

on experimental techniques like X-ray crystallography

or cryo-electron microscopy. These methods are

expensive, require specialised equipment, and often

take years to yield results for even a single protein.

This bottleneck severely limited our understanding

of countless proteins, hindering drug discovery and

fundamental biological research.

1.3 Unfolding with AI

Enter AlphaFold, a game-changer developed by

DeepMind, a subsidiary of Google. AlphaFold is an

artiﬁcial intelligence system that can predict the 3D

structure of a protein directly from its amino acid

sequence with astonishing accuracy. This is like being

able to look at a tangled piece of string and instantly

know exactly how it will coil and fold into a speciﬁc,

functional object. The implications of this ability are

1.4 Applications

profound.

So, how does AlphaFold work its magic? At

its core, AlphaFold uses a deep learning approach, a

type of artiﬁcial intelligence inspired by the structure

and function of the human brain’s neural networks.

Imagine giving a computer millions of examples of

protein sequences and their corresponding 3D structures.

The computer then ”learns” the complex relationships

between the sequence of amino acids and how they fold.

More speciﬁcally, AlphaFold employs a

sophisticated neural network architecture that combines

several key ideas. One crucial aspect is its ability

to reason about the physical and chemical constraints

that govern protein folding. It doesn’t just guess; it

understands that certain amino acids attract or repel

each other, and that proteins tend to settle into a state

of lowest energy. The system considers all possible

interactions between the amino acids in the chain, much

like a meticulous puzzle solver.

A pivotal component of AlphaFold’s success is its

attention mechanism. In simple terms, this allows the AI

to ”focus” on diﬀerent parts of the amino acid sequence

simultaneously, understanding how distant parts of the

chain might interact with each other to inﬂuence the

overall fold. It’s like having a hyper-attentive chef who

can keep track of every ingredient and cooking step,

even those that seem unrelated at ﬁrst glance, to ensure

the perfect ﬁnal dish.

Furthermore, AlphaFold uses an ”evoformer”

module, which leverages evolutionary information.

Proteins with similar functions often have similar

structures, and by analysing how protein sequences

have changed over millions of years of evolution,

AlphaFold gains valuable clues about their likely 3D

shapes. It’s like looking at a family tree of proteins and

noticing shared traits that reveal underlying structural

similarities.

Finally, AlphaFold reﬁnes its predictions

iteratively. It starts with an initial guess for the protein’s

structure and then continuously adjusts and optimises

it, much like a sculptor reﬁning a clay model until it

perfectly matches their vision. This iterative reﬁnement,

combined with its deep understanding of biological

principles, allows AlphaFold to produce incredibly

accurate and detailed protein structures.

1.4 Applications

The impact of AlphaFold on protein research and

its relevance to humanity are immense. Here’s a glimpse

into the transformations it’s already bringing about and

promises to deliver

1.4.1 Accelerated Drug Discovery and

Development

One of the most immediate and impactful

applications of AlphaFold is in the pharmaceutical

industry. Many drugs work by binding to speciﬁc

proteins in the body, either to activate or inhibit

their function. Knowing the precise 3D structure of

these target proteins is crucial for designing drugs

that ﬁt perfectly, like a key in a lock. Before

AlphaFold, determining these structures was a major

bottleneck, often taking years and costing millions.

Now, researchers can rapidly predict the structures

of target proteins, enabling them to design new drug

candidates much faster and more eﬃciently. This will

lead to the development of novel treatments for a wide

range of diseases, from cancer and infectious diseases

to metabolic disorders and neurological conditions.

For example, AlphaMissense, an AI tool developed

using AlphaFold, can classify the eﬀects of 71 million

’missense’mutations, accelerating research in genetics

and making it easier to prioritise resources for studying

diseases.

1.4.2 Understanding Disease Mechanisms

Many diseases, as mentioned earlier, are caused

by misfolded proteins. With AlphaFold, scientists can

now quickly predict the structures of both healthy and

diseased proteins, allowing them to pinpoint precisely

how misfolding occurs and how it disrupts normal

biological processes. This deeper understanding of

disease mechanisms is fundamental to developing

eﬀective therapies. Imagine being able to see exactly

where a protein goes wrong in Alzheimer’s disease; this

insight could unlock entirely new avenues for treatment.

1.5 Conclusion

1.4.3 Designing New Enzymes and Industrial

Catalysts

Proteins also act as enzymes, biological catalysts

that speed up chemical reactions. By understanding

protein structure, scientists can design new enzymes

with enhanced eﬃciency or novel functions for various

industrial applications, such as biofuels production,

waste treatment, and even food processing. This

opens up possibilities for more sustainable and eﬃcient

industrial processes.

1.4.4 Revolutionising Material Science

Proteins are incredibly versatile materials in nature,

forming everything from the silk in a spiderweb to the

collagen in our skin. By understanding and predicting

protein structures, scientists can begin to design novel

proteins with speciﬁc properties, potentially leading

to the creation of new biomaterials for applications in

medicine, engineering, and beyond. Imagine creating

self-healing materials or biodegradable plastics with

unprecedented strength.

1.4.5 Advancing Fundamental Biological

Research

Beyond immediate applications, AlphaFold is

fundamentally changing how biological research is

conducted. It provides a powerful tool for generating

hypotheses, designing experiments, and interpreting

results. Scientists can now explore the structures

of proteins that were previously impossible to study,

unlocking new insights into the intricate workings of

living organisms. This democratisation of structural

biology will lead to a surge of new discoveries across

all branches of biology.

1.4.6 Personalised Medicine

In the future, AlphaFold could play a role

in personalised medicine. As we gain a deeper

understanding of an individual’s genetic makeup, we

could use tools like AlphaFold to predict the structures

of their unique proteins and identify any structural

variations that might predispose them to certain diseases

or aﬀect their response to particular medications. This

could lead to highly tailored treatments, optimised for

each patient.

1.4.7 AI Advancing Science

It’s important to note that while AlphaFold

is incredibly powerful, it’s a computational tool.

Experimental validation of its predictions is still crucial,

and ongoing research continues to reﬁne and expand its

capabilities. However, the speed and accuracy it oﬀers

are unprecedented, transforming the pace and scope of

scientiﬁc discovery.

The excitement surrounding AlphaFold is palpable

within the scientiﬁc community. It’s not just a

technical achievement; it’s a testament to the power

of artiﬁcial intelligence to solve complex problems

that have stumped humanity for decades. This tool is

democratizing access to structural biology, empowering

researchers worldwide to tackle challenges that were

once considered insurmountable.

As we move forward, the synergy between AI

advancements, like those seen in AlphaFold, and

biological research will only grow stronger. The ability

of AI models to understand the ”deep structure” of

language and to apply nuanced responses in complex

scenarios hints at a future where AI can not only

predict but also reason and innovate in scientiﬁc

domains. The recent release of GPT-5, described as a

”universal intelligence partner” with integrated thinking

that automatically recognises when quick answers are

suﬃcient and when more complex reasoning is required,

further highlights the increasing sophistication of AI

in handling intricate problems. These advancements

suggest a future where AI tools will become even more

seamless partners in scientiﬁc discovery, accelerating

the pace of innovation.

1.5 Conclusion

In conclusion, AlphaFold represents a monumental

achievement in scientiﬁc research, marking a new era

in our understanding of proteins and their roles in life.

It’s a tool that empowers scientists to unlock the secrets

1.5 Conclusion

of biological systems, accelerate drug discovery, and

tackle some of humanity’s most pressing challenges.

The potential of AlphaFold to revolutionise medicine,

biotechnology, and our fundamental understanding

of life itself is truly thrilling, and its impact will

undoubtedly be felt for generations to come. We are

standing at the precipice of a new biological revolution,

driven by the incredible power of artiﬁcial intelligence.

References

https://deepmind.google/science/alphafold/

About the Author

Dr.Arun Aniyan is leading the R&D for

Artiﬁcial intelligence at DeepAlert Ltd,UK. He comes

from an academic background and has experience

in designing machine learning products for diﬀerent

domains. His major interest is knowledge representation

and computer vision.

Entropy Across Scales: From Micro-Level

Randomness to Macro-Level Order

by Jinsu Ann Mathew

airis4D, Vol.3, No.10, 2025

www.airis4d.com

In our earlier explorations, entropy appeared ﬁrst

in physics, then in information theory, language, and

networks. At each step, it revealed itself as more

than just a measure of disorder—it became a way of

connecting hidden structures and uncertainties across

diﬀerent domains. So far, we have looked at entropy

within a given system: molecules in a gas, words in

a sentence, or nodes in a network. But entropy’s true

power emerges when we step back and ask a deeper

question: how does randomness at one scale shape

order at another?

Entropy provides a bridge from the micro to

the macro, turning local unpredictability into global

regularity. Whether we are studying particles and

thermodynamics, words and discourse, or individuals

and societies, entropy links the small-scale ﬂuctuations

to the large-scale patterns that emerge from them. This

article explores that scaling role of entropy, showing how

the same principle uniﬁes physics, biology, language,

and social systems.

2.1 Physics: From Molecules to

Thermodynamics

At the heart of physics lies a profound challenge:

while the microscopic world of molecules is governed

by strict physical laws, the sheer number of particles

makes prediction impossible in practice. Imagine a

box ﬁlled with gas. Each molecule moves according

to Newton’s laws, bouncing, colliding, and transferring

energy. If we tried to describe the system at this level,

we would need to know the position and velocity of

trillions of molecules—an impossible task.

This is where entropy steps in. Instead of tracking

each molecule, entropy provides a statistical summary

of the system’s possible conﬁgurations (microstates). It

tells us how many ways the molecules can be arranged

while still producing the same overall, observable

condition (macrostate). For example, we cannot know

the exact motion of every air molecule in a room, but we

can conﬁdently say that the air has a temperature of 25

°C and a pressure of 1 atmosphere. These macroscopic

properties are stable precisely because they emerge

from the law of large numbers acting on countless

microscopic events.

Entropy connects the two levels by counting

possibilities:

Micro-level (molecular world): Each random

arrangement of particles is a microstate.

Macro-level (thermodynamic world):

Temperature, pressure, and volume are macrostates that

summarize many microstates.

Example

Consider two rooms connected by a door, with

all the gas molecules initially conﬁned to one room.

At this moment, the entropy is low, because there are

few possible microstates (most molecules are in the

same place). But once the door opens, molecules begin

to spread out. The number of possible arrangements

2.2 Language: From Letters to Discourse

skyrockets as molecules distribute between both rooms.

The system naturally evolves toward this higher-entropy

state, not because molecules “want” to, but because

there are overwhelmingly more possible microstates

corresponding to it. This illustrates the second law of

thermodynamics: in an isolated system, entropy tends

to increase, because high-entropy macrostates are vastly

more probable than low-entropy ones.

Entropy, therefore, acts as the bridge between the

random dance of molecules and the predictable ﬂow

of thermodynamics. While the micro-world is chaotic,

the macro-world is regular: gases expand, heat ﬂows

from hot to cold, and equilibrium is reached. Entropy

explains why these large-scale patterns emerge from

countless small-scale uncertainties.

2.2 Language: From Letters to

Discourse

At the smallest scale of language are the letters

themselves. If we look closely at English, for example,

we ﬁnd that some letters appear often while others are

rare. The letter e is the most common, while z or

q appear only occasionally. This uneven distribution

creates a characteristic entropy for the language. A

random sequence of letters like xqzjpk has high entropy

because it is diﬃcult to predict the next symbol, whereas

a monotonous sequence like aaaaaa has almost no

entropy at all. Real words, such as apple or forest, lie

somewhere in between: partly predictable because they

follow the rules of spelling, yet varied enough to convey

information.

When we move from letters to words, entropy takes

on a richer meaning. Consider two short passages:

● “Yes, yes, yes, yes.”

●

“The river bends quietly under the old stone

bridge.”

The ﬁrst passage uses only one word repeated,

producing very low entropy—there is little variety or

surprise. The second passage, by contrast, draws on a

wider vocabulary. Here entropy captures the diversity

of word choice, showing how language can scale from

the dull repetition of a few tokens to the richness of

expressive imagery. At this level, entropy becomes a

measure of lexical creativity and informational density.

Sentences introduce a new kind of structure.

Words must ﬁt into grammatical patterns, and this

restricts how unpredictable they can be. A phrase

such as “cat the chased dog the” is technically high in

entropy because it deﬁes expectations, but it fails to

communicate meaning. On the other hand, a formulaic

phrase like “I am ﬁne, thank you” is highly predictable,

with low entropy, but also limited in informational value.

The sweet spot lies in the middle, where sentences like

“The child chased the dog across the garden” follow

grammar yet leave room for novelty. Here entropy

highlights how syntax balances order and variation.

At the largest scale—paragraphs and whole

texts—entropy describes the ﬂow of ideas. A story

that repeats the same statement again and again quickly

collapses into redundancy, like a system with entropy

approaching zero. But a story that leaps chaotically

between topics, never returning to a coherent theme,

has entropy that is too high, making it confusing to

follow. The best writing maintains a balance: each new

sentence introduces some uncertainty, some novelty, yet

remains tied to the context established before. A novel

that develops its plot gradually, or a scientiﬁc article that

builds argument upon argument, achieves this balance,

moving from local unpredictability to global coherence.

Thus, just as molecules in motion give rise

to the thermodynamic laws of heat, the small-scale

unpredictability of letters and words accumulates into

the large-scale order of discourse. Entropy is the

thread that connects these levels, showing how language

evolves from the randomness of characters to the

structured richness of human communication.

2.3 Biology: From Genes to

Ecosystems

Biological systems, like networks, reveal the

interplay between local randomness and global order.

At the genetic level, mutations occur unpredictably.

A single nucleotide change in DNA might seem

like a minor, random event with little immediate

2.4 Social Systems: From Individual Choices to Collective Patterns

consequence. Yet these small variations accumulate

across generations, producing genetic diversity within

a population. For example, in a population of fruit ﬂies,

some individuals may randomly acquire mutations that

confer resistance to a particular pathogen. While each

mutation is a local, stochastic event, collectively these

variations provide the raw material for natural selection

and adaptation. Entropy at this micro-level captures the

unpredictability of genetic changes and the richness of

possible traits in a population.

When we scale up to populations and communities,

the eﬀects of genetic entropy become visible in

ecosystem-level patterns. Diverse traits within species

increase the resilience of populations, allowing them

to survive environmental ﬂuctuations. In coral reef

ecosystems, for instance, genetic variability among

corals and their symbiotic algae determines how well

the reef can recover from bleaching events caused

by temperature changes. Similarly, the diversity

of plant species in a rainforest ensures that some

species will thrive under diﬀerent conditions, stabilizing

the ecosystem as a whole. Even though individual

organisms behave unpredictably, the aggregation of

these micro-level variations produces robust, adaptive

systems.

Entropy thus provides a lens to understand biology

across scales. At the gene level, it measures the

uncertainty of mutations; at the species and ecosystem

level, it quantiﬁes the resulting diversity and resilience.

Micro-level randomness does not imply chaos; rather,

it underlies the emergence of structured, functional

systems. This scaling—from stochastic mutations to

the stability of entire ecosystems—illustrates a core

principle: disorder at small scales fuels order and

adaptability at large scales, making life both dynamic

and robust.

2.4 Social Systems: From Individual

Choices to Collective Patterns

Human societies are complex networks of

interactions, where individual actions combine to create

patterns that are often surprising and highly structured.

At the micro-level, each person’s decisions—whether

to buy a stock, share a social media post, or vote

in an election—carry uncertainty. These decisions

are inﬂuenced by personal preference, available

information, social inﬂuence, and sometimes sheer

chance. From the perspective of a single individual,

behavior may appear unpredictable. Entropy captures

this local uncertainty by quantifying the diversity

and unpredictability of choices within a population.

A market with many equally plausible investment

decisions, for example, has high local entropy, while

a tightly coordinated or heavily inﬂuenced population

would show lower local entropy.

When aggregated across millions of individuals,

these local uncertainties give rise to predictable patterns

at the macro-level. In ﬁnancial markets, while

individual trades are largely random, the collective

activity produces measurable volatility, trends, and

cycles. In social media, seemingly random decisions

to share content can generate viral phenomena, where

a few posts spread rapidly through networks while

most remain largely unseen. Similarly, in elections, the

unpredictability of individual votes contrasts with the

emergence of clear majority outcomes at the population

level. This aggregation illustrates how local randomness

is not chaos; rather, it feeds into emergent order.

Entropy provides the framework to connect these

scales. By measuring the uncertainty of individual

choices and comparing it to the variability of collective

outcomes, researchers can better understand how

micro-level behavior translates into macro-level social

dynamics. For instance, entropy can help identify

which individuals or groups have disproportionate

inﬂuence—“opinion leaders” in social networks, or

key investors in ﬁnancial markets—whose actions

signiﬁcantly shape the system’s global behavior. In

this way, entropy bridges the gap between the

unpredictability of single agents and the patterns

that deﬁne societies, markets, and cultural evolution,

demonstrating once again that local disorder often

underlies large-scale order.

2.5 Conclusion

From the motion of particles and the structure

of language to genetic diversity and human societies,

entropy reveals a consistent principle: local

unpredictability fuels global order. Whether through

molecular randomness, word choice, genetic variation,

or individual decisions, small-scale uncertainty

accumulates to produce structure, diversity, and

resilience at higher scales. This cross-domain

perspective highlights entropy as a universal measure

of complexity, showing how disorder at one level can

generate emergent patterns, adaptability, and coherence

at another. By examining entropy across these layers,

we gain a deeper understanding of the hidden structures

that shape the natural and social world.

References

Entropy - Physics

Entropy Is Universal Rule of Language

Universal Entropy of Word Ordering Across

Linguistic Families

Entropy and Information Approaches to Genetic

Diversity and its Expression: Genomic

Geography

Information entropy as a measure of genetic

diversity and evolvability in colonization

Social Entropy and Normative Network

About the Author

Jinsu Ann Mathew is a research scholar

in Natural Language Processing and Chemical

Informatics. Her interests include applying basic

scientiﬁc research on computational linguistics,

practical applications of human language technology,

and interdisciplinary work in computational physics.

Part II

Astronomy and Astrophysics

Plasma Physics & Comets

by Abishek P S

airis4D, Vol.3, No.10, 2025

www.airis4d.com

1.1 Introduction

In space science, plasma physics plays a

foundational role in understanding the behaviour of

the universe at both large and small scales. Since

plasma constitutes over 99% of the visible universe,

it is the dominant medium through which energy and

matter interact in space. From the solar wind and

planetary magnetospheres to interstellar clouds and

galactic jets, plasma governs the dynamics of cosmic

environments[1]. One of the most critical applications

is in the study of space weather is the interaction between

solar plasma and Earth’s magnetic ﬁeld. Solar ﬂares and

coronal mass ejections release vast amounts of plasma

that can disrupt satellite operations, GPS systems,

and even terrestrial power grids. Plasma physics

helps model these events using magnetohydrodynamics

(MHD) and kinetic theory, allowing scientists to predict

and mitigate their eﬀects

Comets are icy, small celestial bodies that orbit

the Sun, often originating from the distant reaches of

the solar system such as the Kuiper Belt and the Oort

Cloud. Composed primarily of frozen gases, dust, and

rocky material, they are sometimes described as “dirty

snowballs.” When a comet approaches the Sun, solar

heat causes its ices to sublimate, releasing gas and

dust that form a glowing coma and often two distinct

tails, one of dust and one of ionized plasma. These

spectacular features make comets some of the most

visually striking objects in the night sky[2]. Plasma

processes in comets are central to understanding how

these icy bodies interact with their space environment,

especially as they approach the Sun. When a comet

nears the Sun, solar radiation heats its nucleus, causing

volatile gases to sublimate and escape into space. These

gases, once released, become ionized by ultraviolet

radiation and collisions with solar wind particles,

forming a plasma environment around the comet.

1.2 Structure of Comet

The structure of comets is a fascinating and

multilayered system that reﬂects both their primordial

origins and their dynamic interactions with the solar

environment. Comets are composed of ﬁve primary

components: the nucleus, coma, hydrogen envelope,

plasma (ion) tail, and dust tail, each playing a distinct

role in the comet’s behaviour and evolution[2,3]. The

nucleus is the solid core of the comet and the source of

all observable activity. It is typically irregular in shape

and composed of a mixture of volatile ices (primarily

water ice, but also CO[2082?], CO, CH[2084?], and

NH[2083?]), silicate dust, and organic compounds.

These materials are thought to be remnants from the

early solar system, making comet nuclei valuable

archives of primordial matter. Nuclei range in size

from a few hundred meters to tens of kilometres. Their

surfaces are often covered in a dark, carbon-rich crust

that insulates the interior and inﬂuences sublimation

patterns.

1.3 Plasma processes in Comets

Fig. 1: Structure of Comet

Image courtesy :Parts of a Comet: Name, Composition, & Labelled Diagram

As the comet approaches the Sun, solar heating

causes the ices in the nucleus to sublimate, releasing

gas and dust that form a surrounding atmosphere known

as the coma. This region can extend thousands of

kilometres and contains neutral molecules, radicals,

and dust particles. The coma is the visible “head” of the

comet and serves as the transition zone between the solid

nucleus and the external environment. Surrounding the

coma is an extended cloud of neutral hydrogen atoms,

formed when solar ultraviolet radiation dissociates water

molecules. This hydrogen envelope can stretch millions

of kilometres and is detectable only in ultraviolet

wavelengths. It plays a key role in the comet’s

interaction with solar radiation and helps trace the

water content of the nucleus.

The plasma tail forms when ionized gas from the

coma interacts with the solar wind. Charged particles

like CO[207A?] and H[2082?]O[207A?] are swept

away by the Sun’s magnetic ﬁeld, creating a tail that

points directly away from the Sun. This tail can extend

tens of millions of kilometres and often glows blue

due to emissions from ionized carbon monoxide. The

structure of the plasma tail includes features like bow

shocks, contact surfaces, and cometopauses, which are

shaped by the solar wind’s pressure and magnetic ﬁeld

lines. The dust tail consists of small solid particles

released from the nucleus. These particles are pushed

outward by solar radiation pressure, forming a curved

tail that follows the comet’s orbital path. The dust tail

reﬂects sunlight and typically appears yellowish-white.

It contains larger particles than the plasma tail and can

persist long after the comet has passed perihelion.

1.3 Plasma processes in Comets

1.3.1 Outgassing and Ionization

Outgassing and ionization are two fundamental

plasma processes that govern the dynamic behaviour

of comets as they interact with the solar environment.

As a comet approaches the Sun, solar heating causes

the volatile ices within its nucleus, primarily water

(H[2082?]O), carbon dioxide (CO[2082?]), and carbon

monoxide (CO) to sublimate, releasing neutral gas and

dust into space. This process, known as outgassing,

forms the coma, a vast and expanding atmosphere

surrounding the nucleus. The density and composition

of the coma vary with the comet’s proximity to the

Sun and its intrinsic activity[4]. Once released, the

neutral molecules in the coma are exposed to intense

ultraviolet radiation from the Sun, which ionizes

them through photoionization and charge exchange

processes. This transformation marks the beginning

of the comet’s plasma environment, as neutral atoms

become charged particles, electrons and ions that

respond to electromagnetic forces. The newly formed

plasma interacts with the solar wind, a stream of charged

particles emitted by the Sun, leading to the development

of large-scale structures such as bow shocks, magnetic

pile-up regions, and the ion tail. These interactions

are not static; they evolve as the comet’s activity

changes, oﬀering researchers a rare opportunity to

study both collisional and collisionless plasma regimes

in a naturally occurring setting. Missions like ESA’s

Rosetta have provided high-resolution measurements

of these processes, revealing how plasma boundaries

form, shift, and dissipate in response to solar wind

conditions and cometary outgassing rates. Through

these observations, scientists gain critical insights into

the physics of ionized gases, the behaviour of dusty

plasmas, and the mechanisms that shape cometary

evolution and solar system dynamics.

1.3.2 Dust Plasma Interactions

Dust-plasma interactions in comets represent

a critical and complex aspect of cometary physics,

oﬀering unique insights into how charged particles and

1.4 Observational Insights from Space Missions

solid grains behave in space. As a comet approaches

the Sun, solar heating causes volatile gases to sublimate

from the nucleus, entraining dust particles in the outﬂow.

These dust grains, composed of silicates, carbonaceous

material, and ice, become immersed in the surrounding

plasma formed by ionized gas molecules. Through

processes such as photoelectric charging, collisions with

electrons and ions, and secondary electron emission,

the dust particles acquire electric charges, transforming

the cometary environment into a dusty plasma, a

medium where both charged particles and charged

dust grains interact dynamically[4,5]. This charged

dust inﬂuences the local electric ﬁelds, modiﬁes plasma

wave propagation, and contributes to the formation of

large-scale structures like the bow shock and plasma tail.

Studies from missions like Rosetta and Stardust have

shown that dust-plasma coupling aﬀects the morphology

of the coma and tail, alters particle trajectories, and can

even lead to the formation of ﬁlamentary structures

and jets. Moreover, the presence of dust changes

the energy balance and conductivity of the plasma,

making cometary environments ideal for studying non-

equilibrium plasma behaviour. These interactions are

not only vital for understanding comet evolution but also

have broader implications for planetary ring systems,

interplanetary dust dynamics, and astrophysical plasmas

1.3.3 Solar Wind Interaction

Solar wind interaction is one of the most

critical plasma processes in comets, shaping their

ion environment and revealing fundamental physics

about mass-loaded plasmas in space. As a comet

approaches the Sun, its nucleus begins to sublimate,

releasing neutral gases into the coma. These gases are

subsequently ionized by solar ultraviolet radiation and

collisions with solar wind particles, forming a plasma-

rich atmosphere. The solar wind, a continuous stream

of charged particles emitted by the Sun encounters

this expanding plasma, leading to a dynamic and

complex interaction. This process results in the

formation of large-scale structures such as bow shocks,

magnetic pile-up regions, and the cometopause, which

marks the boundary between solar wind-dominated

and cometary plasma-dominated regions. Observations

from ESA’s Rosetta mission at comet 67P/Churyumov–

Gerasimenko showed that as the comet moved from 3.6

AU to 2.0 AU from the Sun, the number of water ions

accelerated by the solar wind increased dramatically,

with daily rates rising by a factor of 10,000 [6,7].

These ions, once formed, are swept away by the solar

wind’s electric and magnetic ﬁelds, contributing to

the formation of the comet’s ion tail. Additionally,

solar wind particles can penetrate the coma and strike

the comet’s surface, causing sputtering, a process that

liberates atoms from the surface into space, further

enriching the plasma environment. The interaction is

highly variable and depends on factors such as solar

activity, the comet’s outgassing rate, and its distance

from the Sun. This variability allows researchers to

study transient plasma phenomena, including wave-

particle interactions and magnetic reconnection, in a

naturally evolving system. Comets, therefore, oﬀer a

unique opportunity to observe how solar wind interacts

with non-magnetized, mass-loading bodies, advancing

our understanding of plasma physics in both planetary

and astrophysical contexts.

Fig. 2: 67P/Churyumov–Gerasimenko

1.4

Observational Insights from Space

Missions

Observational insights from cometary missions

have revolutionized our understanding of these ancient

bodies, transforming them from enigmatic wanderers

into well-characterized laboratories of solar system

science. Spacecraft such as ESA’s Rosetta, NASA’s

Deep Impact, Stardust, and Giotto have provided

unprecedented in situ data on cometary nuclei, comae,

1.5 Challenges and Opportunities

and plasma environments. For instance, Rosetta’s multi-

year study of comet 67P/Churyumov–Gerasimenko

revealed a bilobate nucleus with complex surface

morphology, including cliﬀs, pits, and jets, as well

as seasonal changes in outgassing and dust activity.

Instruments aboard Rosetta measured variations in

plasma density, ion composition, and magnetic ﬁeld

strength, oﬀering real-time insights into how solar

wind interacts with a non-magnetized, mass-loading

body. Similarly, Stardust returned samples from comet

Wild, allowing laboratory analysis of mineral grains

and organic compounds that conﬁrmed the presence

of high-temperature materials, suggesting that comets

contain components formed in diverse regions of the

early solar nebula. More recently, NASA’s James

Webb Space Telescope and Hubble have collaborated to

study interstellar comet 3I/ATLAS, revealing a carbon

dioxide-dominated gas coma and providing clues about

the chemistry of objects formed outside our solar

system[8]. These missions have also enabled remote

sensing of nucleus properties such as size, rotation,

albedo, and fragmentation behaviour, with radar and

infrared surveys expanding our catalogue of cometary

characteristics. Collectively, these observational

campaigns have deepened our understanding of

cometary evolution, plasma interactions, and the role of

comets in delivering volatiles and organics to planetary

surfaces, making them indispensable to planetary

science and astrophysics.

Fig. 3: Rosetta spacecraft

1.5 Challenges and Opportunities

Studying plasma processes in comets presents a

rich landscape of scientiﬁc challenges and opportunities,

especially as we move toward more sophisticated

missions and modelling techniques. One of the

foremost challenges lies in modelling dusty plasmas,

which requires the integration of ﬂuid dynamics with

electromagnetic theory. Unlike conventional plasmas,

cometary environments contain charged dust grains

that interact with ions and electrons, altering the local

electric ﬁelds and wave propagation. This coupling

introduces nonlinearities and multi-scale behaviour

that are diﬃcult to simulate accurately. Researchers

must account for grain charging mechanisms, particle

collisions, and electromagnetic feedback, often relying

on hybrid models that combine kinetic and ﬂuid

approaches to capture the full complexity of the system.

Another major challenge is the temporal evolution

of plasma properties. As a comet travels through its

orbit, its proximity to the Sun changes dramatically,

leading to variations in outgassing rates, ionization

levels, and solar wind interaction. These changes

can transform the plasma environment from a

weakly ionized, collisional regime into a highly

dynamic, collisionless one. Instruments aboard ESA’s

Rosetta mission revealed that comet 67P/Churyumov–

Gerasimenko exhibited signiﬁcant variability in plasma

density, magnetic ﬁeld strength, and boundary

formation as it approached perihelion. This temporal

evolution complicates data interpretation and demands

continuous monitoring to understand how plasma

structures form, evolve, and dissipate.

Comparative studies across diﬀerent comets

further highlight the diversity of plasma behaviour.

While earlier ﬂyby missions provided snapshots

of cometary environments, Rosetta oﬀered long-

term, close-up observations that revealed unexpected

complexity. For example, 67P displayed asymmetric

outgassing, localized jets, and a highly structured

plasma tail, challenging pre-existing models that

assumed more uniform behaviour. These ﬁndings

underscore the need for multi-comet studies to identify

universal plasma processes versus those that are comet-

speciﬁc, shaped by factors such as nucleus composition,

rotation, and orbital dynamics.

Looking ahead, future missions like ESA’s Comet

Interceptor promise to open new frontiers in cometary

1.6 Conclusion

plasma research. Scheduled for launch in 2029, this

mission will target a dynamically new comet, one that

has never entered the inner solar system before. By

intercepting such a pristine object, scientists hope

to observe plasma interactions in an environment

untouched by previous solar exposure. The mission’s

innovative design includes multiple spacecraft that will

perform simultaneous measurements from diﬀerent

vantage points, enabling three-dimensional mapping

of plasma structures and real-time analysis of solar

wind interaction. This multi-point approach will help

resolve longstanding questions about plasma boundary

formation, dust charging dynamics, and the role of

comets in shaping heliospheric conditions.

1.6 Conclusion

The study of cometary plasmas is entering a

transformative era. While modelling and observational

challenges remain, the opportunities for discovery,

especially with next-generation missions are vast and

deeply promising for both planetary science and

fundamental plasma physics.

References

[1] Francis, F, Chen., (2015). Introduction

to Plasma Physics and Controlled Fusion (3rd ed.).

Springer Cham.https://doi.org/10.1007/978-3-319-223

09-4

[2]Greenberg, J. M. (1998). “Making a comet

nucleus.” Astronomy and Astrophysics. v. 330, p.

375-380, 330, 375-380.

[3] Klaude, M., Eriksson, S., Nygren, J., &

Ahnstr

om, G. (1996). “The comet assay: mechanisms

and technical considerations.” Mutation Research/DNA

Repair, 363(2), 89-96.

[4] Galeev, A. A., & Lipatov, A. S. (1984). “Plasma

processes in cometary atmospheres.” Advances in

Space Research, 4(9), 229-237.

[5] Gombosi, T. I. (1991). “The plasma

environment of comets.” Reviews of Geophysics,

29(S2), 976-984.

[6] Altwegg, K., Balsiger, H., Bar-Nun, A.,

Berthelier, J. J., Bieler, A., Bochsler, P., ... &

Wurz, P. (2015). “67P/Churyumov-Gerasimenko, a

Jupiter family comet with a high D/H ratio.” Science,

347(6220), 1261952..

[7] Sierks, H., Barbieri, C., Lamy, P. L., Rodrigo,

R., Koschny, D., Rickman, H., ... & Paetzold, M.

(2015). “On the nucleus structure and activity of comet

67P/Churyumov-Gerasimenko.” Science, 347(6220),

aaa1044.

[8] Loeb, A. (2025). “3I/ATLAS is Smaller or

Rarer than It Looks.” Research Notes of the AAS, 9(7),

178.

About the Author

Abishek P S is a Research Scholar in

the Department of Physics, Bharata Mata College

(Autonomous) Thrikkakara, kochi. He pursues

research in the ﬁeld of Theoretical Plasma physics.

His works mainly focus on the Nonlinear Wave

Phenomenons in Space and Astrophysical Plasmas.

X-ray Astronomy: Through Missions

by Aromal P

airis4D, Vol.3, No.10, 2025

www.airis4d.com

2.1 Satellites in 2020s

The last decade has marked a giant leap forward

in astrophysics with the advent of X-ray polarimetry.

Previously, our understanding of cosmic X-ray sources

was limited to what we could learn from images, spectra,

and timing analysis. Now, thanks to the groundbreaking

success of the Imaging X-ray Polarimeter Experiment

(IXPE), we can study the polarization of X-rays. This

powerful new window into the high-energy universe

provides invaluable information about the magnetic

ﬁeld geometry around extreme cosmic objects.

Although the ﬁrst attempts to measure X-ray

polarization date back to the 1970s, the dream was

long hindered by technological hurdles. The primary

challenge is that polarimetry is a ”photon-hungry”

technique, requiring long observations (often over

80,000 seconds) to gather enough data for a meaningful

measurement. IXPE’s innovative design has ﬁnally

overcome this barrier, revolutionizing our ability to

study the most energetic phenomena in the cosmos.

2.1.1 IXPE

Imaging X-ray Polarimetry Explorer(IXPE) is

a game-changer for X-ray astronomy. As the ﬁrst

observatory built speciﬁcally for X-ray polarimetry, this

joint NASA and Italian Space Agency (ASI) mission

gives us a whole new way to see the universe. It adds

polarization measurements to the traditional toolkit

of images, timing, and spectroscopy, capturing all of

this data at the same time. IXPE was successfully

launched on December, 2021, aboard a SpaceX Falcon

9 rocket. The spacecraft operates in a circular low

Earth orbit (LEO) at an altitude of 600 km with an

inclination of approximately 0 degrees. The mission’s

main goal is to look for cosmic features that have a

speciﬁc direction or structure, which can’t be seen with

regular X-ray telescopes. This includes things like

organized magnetic ﬁelds, lopsided clouds of matter,

and the warping eﬀects of intense gravity predicted by

general relativity. Mission had a lifetime of 2 years and

it is begin to start its ﬁfth year operation in space. IXPE

uses three identical mirror assemblies(MMA) to focus

X-rays. Each mirror assembly converges the X-rays

onto a detector placed exactly 4 meters away.

The real power of IXPE comes from its three

advanced Gas Pixel Detectors, the result of over two

decades of dedicated work by Italian research teams.

When an incoming X-ray photon strikes a gas atom

inside a detector, it knocks an electron free, creating

a tiny, tell-tale track. This track is immediately

captured by a custom-designed, highly sensitive chip.

By analyzing the track’s speciﬁc shape and direction,

scientists can measure not only the X-ray’s energy and

origin but, for the ﬁrst time, its polarization. MMA

have an eﬀective area of 590 cm

at 4 keV with a ﬁeld

of view of of 12.9 arcminutes squared. It also had a

temporal resolution of 100 mircosecond and a spectral

resolution of 0.57 keV at 2 keV.

Since it began its mission in 2022, IXPE has

delivered a stream of groundbreaking discoveries across

the cosmos. By studying supernova remnants, it mapped

the average magnetic ﬁeld of Cassiopeia A and, in a

ﬁrst for X-ray polarimetry, conﬁrmed that the magnetic

ﬁeld wraps around the shockwave rim of RX J1713.7-

3946. In a landmark observation of the black hole

2.1 Satellites in 2020s

Figure 1: The IXPE Observatory highlighting the key

scientiﬁc payload elements (credit:[1])

Cygnus X-1, IXPE provided the ﬁrst direct proof that

the swirling disk of matter falling in is what launches the

powerful jets shooting out. The observatory also gave

us our ﬁrst-ever polarized view of magnetars, including

4U 0142+61, revealing the structure of their intense

magnetic ﬁelds. On top of this, by watching over a

dozen pulsars spin, it has shown that their magnetic

environments are complex and change dramatically

with every rotations

2.1.2 XRISM

The X-Ray Imaging and Spectroscopy Mission

(XRISM) is an international X-ray astronomy satellite

created to advance our knowledge of the hot universe

through high-resolution X-ray spectroscopy. It serves

as the replacement for the ASTRO-H (Hitomi) satellite,

whose mission ended unexpectedly in 2016 after just

one month, despite collecting valuable scientiﬁc data.

XRISM was launched successfully on September,

2023, from the JAXA Tanegashima Space Center

on an H-IIA rocket. The satellite is a cooperative

mission between the Japan Aerospace Exploration

Agency (JAXA) and the National Aeronautics and Space

Administration (NASA), with contributions from the

European Space Agency (ESA) and other international

groups. XRISM circles the earth in a low-Earth orbit of

radius 575 km and inclination of 31

○

speciﬁcally chosen

to enhance scientiﬁc output and reduce background

interference. XRISM carries two complementary

advanced X-ray instruments that together provide

comprehensive observational capabilities which works

in the energy band 0.3-13 keV in total.

Resolve(High-Resolution X-ray

Microcalorimeter) is the main science payload

on the XRISM satellite. At its heart is a special

Figure 2: Schematic view of the XRISM satellite

(credit: [12])

36-pixel sensor that works like a super-sensitive

X-ray camera. To detect the tiny energy from a

single X-ray, this sensor must be cooled to an

extreme temperature of 50 millikelvin, which is

just a fraction of a degree above absolute zero.

The instrument’s key feature is its incredible

energy resolution of about 5 eV, which is even

better than the original design goal. This ability

is crucial because it lets scientists clearly see

individual ”spectral lines. It has a feild of view

of 3

3 arcminute and Resolve can detect X-rays

from 0.3 to 12 keV energy range. Rsolve have a

timing resolution of 1 milliseconds.

Xtend is a wide-ﬁeld X-ray telescope that pairs a

specialized CCD-camera (the Soft X-ray Imager,

or SXI) with a mirror assembly (XMA). Its has

is an exceptionally large ﬁeld of view covering a

38.5

38.5 arcminute area of the sky, the biggest

of any X-ray telescope. It is highly sensitive, with

an eﬀective collection area of 420 cm

for X-rays

at an energy of 1.5 kilo-electron volts (keV). The

instrument detects X-rays in the 0.4 to 13 keV

energy range and can distinguish between their

energies with a precision of 170-180 electron

volts (eV) when observing X-rays at 6 keV.

High-resolution X-ray studies using XRISM

revealed the complex dynamics of several key galactic

objects. In the supernova remnant W49B, astronomers

found the ﬁrst kinematic proof of two-sided, or bipolar,

ﬂows of ejected material. Another study of MAXI

J1744-294 detected intricate iron line structures for

the ﬁrst time, with coexisting emission and absorption

2.1 Satellites in 2020s

Figure 3: XPoSat satellite (credit: URSC, RRI, ISRO)

features that suggest a layered and turbulent medium is

reprocessing the X-rays. Finally, a signiﬁcant discovery

was made around the microquasar V4641 Sgr, where a

large cloud of extended X-ray emission was detected,

conﬁrming more activity around this powerful particle

accelerator.

2.1.3 XPoSat

X-ray Polarimeter Satellite(XPoSat) is India’s ﬁrst

dedicated X-ray polarimetry mission, a signiﬁcant

achievement that makes it only the second country

to deploy such a specialized observatory in space.

Launched successfully by ISRO on New Year’s Day

2024, the satellite now operates from a 650-kilometer

low Earth orbit. Its primary goal is to advance our

understanding of high-energy astrophysical phenomena,

like black holes, by conducting detailed studies of the

polarization and spectroscopic characteristics of their

X-ray emissions. XPOSAT carries two co-aligned

scientiﬁc instruments that work in tandem to provide

comprehensive X-ray observations in the energy range

0.2-30 keV.

Polarimeter Instrument in X-rays (POLIX) is

an X-ray polarimeter designed to study cosmic

sources in the 8-30 keV energy range. Its

design features a central scatterer made of a

low-atomic-mass material, which is surrounded

by four X-ray proportional counters. Incoming

polarized X-rays hit the scatterer and are

anisotropically scattered—a process known as

Thomson scattering—allowing their polarization

to be measured. To ensure that only a single bright

source is observed at a time, a collimator restricts

the instrument’s ﬁeld of view to 3

3 degrees. As

the ﬁrst payload dedicated to polarimetry in the

medium X-ray band.

The X-Ray Spectroscopy and Timing (XSPECT)

instrument, a key payload on the XPoSat mission,

is designed to monitor the long-term spectral

evolution of select celestial sources in the soft

X-ray band. Operating from 0.8 to 15 keV, it

utilizes passively cooled Swept Charge Devices

(SCDs) to ensure a large detection area and high-

quality spectral data. The instrument is equipped

with collimators oﬀering two distinct ﬁelds of

view (2x2 and 3x3 degrees) and boasts a ﬁne

spectroscopic resolution of under 200 eV at 6

keV, along with a respectable timing precision of

2 milliseconds.

2.1.4 Einstein Probe

The Einstein Probe (EP) is a Chinese-led

space mission dedicated to time-domain high-energy

astrophysics. Launched on January 9, 2024, via a Long

March 2C rocket from the Xichang Satellite Launch

Center, the mission is a collaborative eﬀort between

the Chinese Academy of Sciences (CAS), the European

Space Agency (ESA), the Max Planck Institute for

Extraterrestrial Physics (MPE), and the French National

Centre for Space Studies (CNES). The satellite operates

in a sun-synchronous circular orbit at an altitude of 592

kilometers, featuring an orbital period of 97 minutes.

EP carries two complementary X-ray telescopes, works

in 0.5-4 keV X-ray energy range.

The Wide-ﬁeld X-ray Telescope (WXT) utilizes

innovative lobster-eye micro-pore optics (MPO),

a biomimetic design where thousands of square

microscopic channels are arranged in a square-

packed array pointing to a common spherical

center. The instrument is composed of

12 identical modules, each functioning as a

complete telescope with its own optics and

REFERENCES

Figure 4: Layout of the payloads and spacecraft of the

Einstein Probe satellite. The two FXT units are placed

at the centre, surrounded by the twelve WXT modules

(credit: IAMC, CAS)

a complementary metal-oxide-semiconductor

(CMOS) detector. With a focal length of 375

mm, the optics are optimized for the 0.5-4 keV

energy range. The complete 12-module system

provides an instantaneous ﬁeld-of-view of 3,600

square degrees and achieves a spatial resolution

of 4-7 arcminutes FWHM, with a median of 4.2

arcminutes across the ﬁeld.

The Follow-up X-ray Telescope (FXT) comprises

two identical Wolter-I type telescopes, FXT-A

and FXT-B, each functioning as a complete X-ray

imaging system. The telescopes feature a focal

length of 1,600 mm and a combined eﬀective area

of approximately 600 cm

. They achieve a spatial

resolution with a point spread function half-

energy width (PSF HEW) under 20 arcseconds

on-axis at 1.49 keV and provide an individual

ﬁeld of view of about 1 degree in diameter.

Operating across the 0.3-10 keV energy range,

the FXT oﬀers broader spectral coverage than the

WXT, facilitating comprehensive spectroscopic

analysis.

Since beginning operations, the Einstein Probe

has revolutionized the study of fast X-ray transients

and multi-messenger astronomy. In its ﬁrst year, the

mission detected 72 high signal-to-noise fast X-ray

transients (FXTs), discoveries which have provided

unprecedented insights into this mysterious class of

extragalactic phenomena

We have covered the major X-ray astronomical

missions. We will explore the world of X-ray astronomy

in upcoming articles.

References

[1]

Weisskopf et al., 2022,J. Astron. Telesc. Instrum.

Syst., doi:10.1117/1.JATIS.8.2.026002

[2]

Mercuri et al., 2025, ApJ, doi: 10.3847/1538-

4357/adcedb

[3]

Ferrazzoli et al., 2024, ApJL, doi:10.3847/2041-

8213/ad4a68

[4]

Steiner et al., 2024, ApJL, doi:10.3847/2041-

8213/ad58e4

[5]

Terada et al., 2024, SPIE, doi:10.1117/12.3019329

[6]

XRISM Collaboration, 2025, PASJ,

doi:10.1093/pasj/psae111

[7]

XRISM Collaboration, 2025, ApJL,

doi:10.3847/2041-8213/ade138

[8]

Chatterjee et al., 2025, arXiv,

doi:10.48550/arXiv.2506.22964

[9]

Suzuki et al., 2024, arXiv,

doi:10.48550/arXiv.2412.08089

[10]

Radhakrishna et al., 2025, J. Astron. Telesc.

Instrum. Syst., doi:10.1117/1.JATIS.11.3.035001

[11]

Cheng et al., 2025, arXiv,

doi:10.48550/arXiv.2505.18939

[12]

Team, XRISM Science, 2022, arXiv,

doi:10.48550/arXiv.2202.05399

[13]

Aryan et al., 2025, arXiv,

doi:10.48550/arXiv.2504.21096

About the Author

Aromal P is a research scholar in

Department of Astronomy, Astrophysics and Space

Engineering (DAASE) in Indian Institute of

Technology Indore. His research mainly focuses on

studies of Thermonuclear X-ray Bursts on Neutron star

surface and its interaction with the Accretion disk and

Corona.

Introduction: Galaxies in Motion

by Robin Thomas

airis4D, Vol.3, No.10, 2025

www.airis4d.com

3.1 Introduction

This article explores the ﬁndings of the

study ”Properties of Barred Galaxies with the

Environment: II. The case of the Cosmic Web

around the Virgo cluster” by Virginia Cuomo and

collaborators (Cuomo et al. 2025). The research

investigates how environmental factors aﬀect the size

and prominence of bars in disk galaxies, comparing

galaxies located in the dense Virgo Cluster, surrounding

ﬁlaments of the Cosmic Web, and in the ﬁeld.

Galaxies are constantly evolving within the vast

cosmic web—a complex network of ﬁlaments, clusters,

and voids that shapes the universe’s large-scale structure.

Just as our planet’s environment inﬂuences life on

Earth, the environment surrounding galaxies plays a

crucial role in determining their properties. In the

research article *”Properties of Barred Galaxies with the

Environment: II. The case of the Cosmic Web around

the Virgo cluster”*, Virginia Cuomo and collaborators

explore how diﬀerent cosmic environments impact the

formation and evolution of bars in disk galaxies.

A galactic bar is a central, elongated structure made

up of stars, typically seen in spiral galaxies. These bars

can aﬀect the galaxy’s rotation, star formation, and gas

dynamics. Understanding how bars form and evolve

in diﬀerent environments is essential for deciphering

galaxy evolution at large. In this study, the authors

focused on barred galaxies in various environments:

the dense core of the Virgo Cluster, the surrounding

ﬁlaments of the Cosmic Web, and the ﬁeld—regions

far from dense clusters.

Figure 1: Comparison of scales showing our position

in the Milky Way and the Local Group (top row) and

the Local Group position in the nearby part of the

Cosmic Web (bottom panel + background) on top of a

theoretically modelled dark matter map of the nearby

universe showing high density regions in bright color

and low density regions in dark (Credits: The ﬁgure is

based on adaptions of images of NASA, theskylive.com,

GAIA and the CLUES project)

3.2 Examining the Role of

Environment

The study uses a homogeneous sample of barred

galaxies from the DESI Legacy Survey, ensuring

that the sample is unbiased in terms of galaxy color

and magnitude. The researchers then examined the

bars’properties by employing Fourier analysis and

surface brightness ﬁtting techniques. By comparing

galaxies from three diﬀerent environments, they aimed

to uncover how the surrounding cosmic structure

inﬂuences the size, prominence, and overall evolution

of the bars.

3.3 Key Findings: Bars in Diﬀerent Environments

3.3 Key Findings: Bars in Diﬀerent

Environments

The researchers found striking diﬀerences in the

properties of bars depending on their environment.

These diﬀerences provide crucial insights into the

interplay between galaxies and their surroundings.

3.3.1 Bar Radii

The results revealed clear trends in the size of

bars across diﬀerent environments. Galaxies in the

Virgo cluster, a high-density environment, tend to have

smaller bars, with a median bar radius of 2.54 ± 0.34

kpc. In contrast, galaxies in ﬁlaments, which are the

structures that connect galaxy clusters, exhibit slightly

larger bars with a median radius of 3.29 ± 0.38 kpc.

Galaxies located far from the inﬂuence of clusters or

ﬁlaments, in the ﬁeld, show the largest bars, with a

median radius of 4.44 ± 0.81 kpc.

3.3.2 Bar Prominence

The prominence of the bars, which refers to how

large they are relative to the overall galaxy disk, also

varies signiﬁcantly across the diﬀerent environments.

In the Virgo cluster, bars are less prominent, with a ratio

of 1.26 ± 0.09 between the bar radius and disk scale

length. The ratio increases in galaxies in ﬁlaments,

where it reaches 1.72 ± 0.11, indicating slightly more

prominent bars. In ﬁeld galaxies, bars are the most

prominent, with a ratio of 2.57 ± 0.21. These results

show a clear trend: galaxies in dense environments,

such as the Virgo cluster, tend to have shorter and less

prominent bars, while those in the ﬁeld, away from

such environmental pressures, possess larger and more

prominent bars.

3.4

What’s Behind These Diﬀerences?

The observed diﬀerences in the size and

prominence of bars across diﬀerent environments

suggest that the surrounding cosmic environment plays

a signiﬁcant role in shaping the structural properties

of barred galaxies. Galaxies in diﬀerent environments

experience distinct physical processes that inﬂuence

their morphology and dynamics. These processes

include tidal interactions, gas stripping, and the

overall dynamical friction exerted by the surrounding

environment. Let’s delve deeper into each of these

environmental factors and how they may hinder or

facilitate the formation and growth of bars.

3.4.1 Tidal Interactions

In high-density environments such as the Virgo

Cluster, galaxies are more likely to interact with

neighboring galaxies. These interactions can lead to

strong tidal forces, which can distort a galaxy’s disk

and alter its star formation. Tidal interactions often

cause a redistribution of gas within the galaxy, which

may prevent it from accumulating in the central regions

and forming a strong bar. In the case of galaxies in the

Virgo Cluster, the increased frequency of interactions

may disrupt the growth of bars, leading to smaller and

less prominent bars. These tidal forces can prevent

the normal process of bar formation by scattering or

redistributing the gas, making it diﬃcult for the galaxy

to develop a well-deﬁned central bar structure.

3.4.2 Gas Stripping and Strangulation

In clusters like Virgo, galaxies are exposed to harsh

environments that can strip away their gas through

a process known as ram pressure stripping. This

occurs when a galaxy moves through the hot gas of

the intracluster medium, causing the galaxy’s gas to

be pushed out of its disk. Without suﬃcient gas,

galaxies lose the fuel necessary for the growth of bars.

Moreover, the removal of gas can lead to strangulation,

a process in which star formation is suppressed due to

the lack of fresh gas. In such cases, barred galaxies may

experience slower bar evolution, resulting in shorter

and less prominent bars compared to galaxies in the

ﬁeld or in ﬁlaments. This lack of gas availability

signiﬁcantly impacts the bar’s growth and evolution,

as the presence of gas is necessary for the angular

momentum redistribution required for bar formation.

3.5 Connecting the Dots: Environmental Impact on Galaxy Evolution

3.4.3 Dynamical Friction and Cluster Eﬀects

Another factor at play in dense environments is

dynamical friction. As galaxies interact with each other,

the gravitational pull of one galaxy can cause another to

lose energy, slowing its motion and allowing it to sink

toward the center of the cluster. This can result in the

formation of more compact and less deﬁned bars. The

Virgo Cluster, being a dense environment, promotes

such processes, which could contribute to the observed

trend of shorter bars in galaxies residing there. The

accumulation of galaxies toward the cluster’s center

can cause more violent interactions, further hindering

bar development by disrupting the dynamics of each

individual galaxy’s disk. As a result, the bars in these

galaxies are often smaller and less prominent compared

to their counterparts in less dense regions.

3.4.4 Filaments and Low-Density

Environments

In contrast, galaxies in ﬁlaments, which are the

web-like structures that connect clusters, experience

less frequent and intense interactions. The gas content

in these galaxies is also less likely to be stripped or

disrupted, providing a more stable environment for the

formation of bars. In these regions, galaxies can retain

more of their gas, and the growth of bars is less hindered

by the disruptive forces seen in dense clusters. As a

result, galaxies in ﬁlaments tend to have moderately

sized and more prominent bars compared to those in

the cluster core.

Finally, galaxies in the ﬁeld, where environmental

inﬂuences are minimal, are free from the disruptive

forces of tidal interactions and gas stripping. These

galaxies are able to accumulate gas more easily,

and the evolution of their bars can proceed without

hindrance. In the ﬁeld, galaxies enjoy an environment

where their internal dynamics and gas content are

largely unaﬀected by external forces, allowing the

formation of large, prominent bars. These galaxies

can evolve freely, with bar formation progressing over

time without interference from surrounding galaxies or

the intracluster medium.

3.5 Connecting the Dots:

Environmental Impact on Galaxy

Evolution

The ﬁndings presented in this study align with

previous research showing that galaxies in clusters

often have shorter bars compared to those in the ﬁeld.

This study, however, provides new insights by oﬀering

a more homogenous and carefully selected sample,

minimizing potential observational biases.

The fact that bars in the Virgo cluster are shorter

and less prominent suggests that environmental factors

like galaxy-galaxy interactions and gas stripping could

play a critical role in hindering the secular evolution

of barred galaxies. Conversely, galaxies in less dense

environments, such as the ﬁeld or cosmic web ﬁlaments,

can evolve without such hindrances, allowing for the

growth of more substantial and more prominent bars.

3.6 Why Bars Matter

Bars are not just structural features; they are

key players in the secular evolution of galaxies.

They act as mechanisms for redistributing gas and

stars within galaxies, helping drive star formation and

potentially inﬂuencing the central black hole growth.

The study suggests that bars in dense environments

like the Virgo cluster may evolve diﬀerently, impacting

galaxy dynamics and even the potential for future star

formation.

3.7 Conclusion: A Deeper

Understanding of Galaxy

Evolution

The study’s ﬁndings underscore the dynamic

relationship between galaxies and their

environments. It highlights how the cosmic

web—the network of ﬁlaments, clusters, and

voids—can aﬀect galaxy evolution on multiple scales.

In particular, the way the environment inﬂuences

bar formation could serve as a valuable tool for

3.7 Conclusion: A Deeper Understanding of Galaxy Evolution

understanding how galaxies in diﬀerent regions of the

universe evolve over cosmic timescales.

As we continue to explore the properties of barred

galaxies, this research provides a crucial perspective

on how the environment can shape galaxy structures.

By studying the impact of environmental factors, we

can improve our models of galaxy evolution and better

understand the processes that shape the universe’s large-

scale structures.

References

Cuomo, V., Aguerri, J. A. L., Morelli, L.,

Choque-Challapa, N., & Zarattini, S. 2025, arXiv

e-prints, arXiv:2509.23460

About the Author

Dr Robin is currently a Project Scientist at the

Indian Institute of Technology Kanpur. He completed his

PhD in astrophysics at CHRIST University, Bangalore,

with a focus on the evolution of galaxies. With a

background in both observational and simulation-based

astronomy, he brings a multidisciplinary approach to his

research. He has been a core member of CosmicVarta,

a science communication platform led by PhD scholars,

since its inception. Through this initiative, he has actively

contributed to making astronomy research accessible to

the general public.

Main Sequence Stars

by Sindhu G

airis4D, Vol.3, No.10, 2025

www.airis4d.com

4.1 Introduction

Stars are the fundamental building blocks of

galaxies and play a central role in cosmic evolution.

Among the various stellar types, main sequence

stars (Figure 1) represent the most common and

longest-lasting phase of stellar evolution. During

this stage, stars sustain themselves through the

nuclear fusion of hydrogen into helium in their cores,

producing the energy that makes them shine. The

Hertzsprung–Russell (H-R) diagram(Figure 2), which

plots stellar luminosity against surface temperature,

clearly shows the main sequence as a diagonal band

extending from hot, massive, blue stars to cool, low-

mass, red stars.

This article provides a detailed overview of main

sequence stars, including their physical characteristics,

structure, classiﬁcation, energy generation processes,

and their signiﬁcance in astrophysics.

4.2 The Hertzsprung - Russell

Diagram and the Main Sequence

The H-R diagram is one of the most powerful

tools in stellar astrophysics. When stars are plotted

according to their absolute magnitude (or luminosity)

versus spectral type (or temperature), they fall into

distinct regions. The main sequence forms a continuous

band running from the upper left (hot, luminous O-

type stars) to the lower right (cool, faint M-type stars).

Stars spend about 90% of their lifetimes on the main

sequence.

The position of a star on the main sequence is

Figure 1: NASA’s Solar Dynamics Observatory

captured this image of our 4.6-billion-year-old Sun,

a main sequence star. Scientists expect it will remain

one for another 5 billion years before becoming a red

giant.

Image Credit: NASA’s Scientiﬁc Visualization Studio/SDO.

Figure 2: The H-R Diagram.

Image Credit: Chandra.

4.3 Stellar Structure and Energy Generation

determined primarily by its mass, which governs its

temperature, luminosity, and evolutionary timescale.

Massive O- and B-type stars appear at the top left of

the sequence: they are rare, short-lived, but extremely

luminous. At the bottom right, M-type red dwarfs are

faint but represent the majority of stars in the galaxy.

4.3 Stellar Structure and Energy

Generation

4.3.1 Hydrostatic Equilibrium

A main sequence star maintains stability through

hydrostatic equilibrium, where the inward pull of gravity

is balanced by the outward pressure of hot gas produced

by nuclear fusion in the core.

4.3.2 Energy Production

Main sequence stars generate energy by fusing

hydrogen into helium. Two primary mechanisms

dominate:

Proton-Proton (pp) Chain: Predominant in stars

with masses up to about 1.5 solar masses (like the

Sun). Hydrogen nuclei (protons) fuse through a series

of reactions to form helium-4, releasing energy in the

form of photons and neutrinos.

CNO Cycle (Carbon-Nitrogen-Oxygen cycle):

Dominates in stars more massive than the Sun.

Hydrogen fusion occurs with carbon, nitrogen, and

oxygen acting as catalysts. This process is highly

temperature-sensitive, explaining why massive stars are

much more luminous.

4.3.3 Energy Transport

Energy produced in the core is transported outward

by radiation and convection:

Low-mass stars

→

radiative cores, convective

envelopes.

High-mass stars

→

convective cores, radiative

envelopes.

Red dwarfs (very low mass)

→

fully convective,

mixing fresh hydrogen into the core and

prolonging their lifetimes.

4.4 Classiﬁcation of Main Sequence

Stars

Main sequence stars are classiﬁed by their spectral

type and luminosity class V (Roman numeral ﬁve

indicates a main sequence dwarf). The Morgan–Keenan

(MK) system deﬁnes spectral classes: O, B, A, F, G, K,

M, ranging from hottest to coolest.

O-type:

> 30,000 K

, blue, very luminous, short-

lived (few million years)

B-type: 10,000–30,000 K, luminous, tens of

millions of years

A-type: 7,500–10,000 K, white, bright (e.g.,

Sirius A)

F-type: 6,000–7,500 K, yellow-white

G-type: 5,200–6,000 K, yellow (e.g., the Sun,

G2V)

K-type: 3,700–5,200 K, orange, moderately faint

M-type:

< 3,700 K

, red dwarfs, extremely long-

lived

The classiﬁcation directly relates to surface

temperature, luminosity, mass, and lifetime.

4.5 Lifetimes of Main Sequence Stars

The lifetime of a star on the main sequence is

strongly dependent on its mass. The relationship can

be approximated as:

t ∝

where

is stellar mass and

is luminosity. Since

luminosity increases steeply with mass (

L ∝ M

3.5

massive stars burn through their fuel very quickly.

Massive stars (O, B types): lifetimes of a few

million years

Intermediate stars (A, F, G types): lifetimes of

hundreds of millions to several billion years

Low-mass stars (K, M types): lifetimes of tens

to hundreds of billions of years

While massive stars dominate visually due to their

brightness, the majority of stars in the Milky Way are

faint red dwarfs that will outlive all others.

4.6 End of Main Sequence Phase

Once hydrogen in the core is depleted:

The star can no longer maintain hydrostatic

equilibrium.

The core contracts and heats up, while the outer

layers expand.

The star leaves the main sequence and evolves

into a red giant (for low- and intermediate-mass

stars) or into more complex evolutionary stages

(for high-mass stars).

This transition marks a fundamental turning point

in stellar evolution.

4.7 Importance of Main Sequence

Stars

Main sequence stars are crucial to astrophysics for

several reasons:

Standard candles: Their predictable mass-

luminosity relation makes them useful in distance

measurements.

Galactic population studies: Most stars in

galaxies are main sequence stars, especially red

dwarfs.

Stellar evolution models: Studying their

properties allows astronomers to understand how

stars form, evolve, and die.

Habitability: Many exoplanet-hosting stars are

main sequence stars, making them critical to the

search for life beyond Earth.

4.8 Conclusion

Main sequence stars are the backbone of stellar

astrophysics. They provide the light and heat that shape

planetary systems, and they serve as laboratories for

understanding nuclear fusion, energy transport, and

stellar lifetimes. From massive O-type stars that live

fast and die young to red dwarfs that quietly persist

for trillions of years, main sequence stars highlight the

diversity and complexity of the Universe.

As observational techniques advance, especially

with space telescopes like Gaia, Kepler, and TESS,

our understanding of these stars continues to improve,

oﬀering deeper insights into both stellar physics and

cosmic evolution.

References:

Main sequence stars: deﬁnition & life cycle

The H-R Diagram

Hertzsprung-Russell Diagram

Main Sequence Stars

Main sequence

Main Sequence

STARS, GALAXIES, THE UNIVERSE

About the Author

Sindhu G is a research scholar in the

Department of Physics at St. Thomas College,

Kozhencherry. She is doing research in Astronomy

& Astrophysics, with her work primarily focusing

on the classiﬁcation of variable stars using diﬀerent

machine learning algorithms. She is also involved

in period prediction for various types of variable

stars—especially eclipsing binaries—and in the study

of optical counterparts of X-ray binaries.

Part III

Biosciences

BLAST and FASTA: Cornerstones of

Sequence Alignment in Biosciences and

Bioinformatics:

by Geetha Paul

airis4D, Vol.3, No.10, 2025

www.airis4d.com

1.1 Introduction

Sequence alignment is a fundamental technique

in bioinformatics that enables researchers to compare

DNA, RNA, or protein sequences against known

databases to detect regions of similarity. These

similarities can indicate shared evolutionary ancestry,

functional relationships, or structural conservation

among biomolecules. With the surge in genomic and

proteomic data over the past few decades, propelled

by advances in sequencing technologies, the task

of analysing and interpreting biological sequences

has become increasingly complex and data-intensive.

Consequently, the need for computational tools that

can rapidly and accurately align sequences has become

critical to biological research.

Historically, early methods for sequence

comparison, such as the Smith-Waterman algorithm,

employed exhaustive dynamic programming approaches

that guaranteed optimal global or local alignments.

While these methods provide highly precise alignments,

their computational demand grows quadratically with

sequence length, rendering them impractical for

searching large-scale genomic databases that contain

millions of sequences. To overcome this bottleneck,

heuristic algorithms were developed that traded some

accuracy for enormous gains in speed, enabling practical

searches on modern genetic datasets.

In this context, two pioneering heuristic methods

developed in the late 1980s and early 1990s transformed

the ﬁeld: FASTA and BLAST. FASTA, introduced by

David J. Lipman and William R. Pearson in 1985-

1988, was the ﬁrst widely adopted heuristic sequence

alignment tool. It employed strategies that focused on

identifying short, exact matches or hotspots between

two sequences before extending them into longer

alignments. This approach preserved much of the

sensitivity of earlier algorithms while dramatically

reducing computation time.

Building upon this foundation, Altschul and

colleagues introduced BLAST (Basic Local Alignment

Search Tool) in 1990, which further enhanced search

speed and integrated rigorous statistical methods to

assess the reliability of sequence matches. BLAST

revolutionised sequence analysis by providing fast,

statistically meaningful local alignments of nucleotide

or protein sequences against large public and private

databases. It rapidly gained popularity and remains one

of the most widely used bioinformatics tools worldwide.

Both FASTA and BLAST have become

indispensable in molecular biology, genomics, and

evolutionary studies. They operate primarily by

ﬁnding local regions of similarity, which allows

researchers to detect conserved functional domains,

gene families, and evolutionary relationships even in

sequences with substantial divergence. While both tools

employ heuristic word-based searching, they diﬀer in

algorithmic details, customisation options, and typical

1.2 What is FASTA?

applications. This article explores their underlying

principles, comparative functionalities, and roles in

modern bioinformatics workﬂows.

What is BLAST?

BLAST (Basic Local Alignment Search Tool) is

a suite of programs developed by Stephen Altschul

and colleagues at NCBI in 1990. It ﬁnds regions of

local similarity between sequences and is widely used

to compare nucleotide or protein sequences against

databases. BLAST uses ﬁxed-size words to search for

high-scoring ungapped segments and then extends these

for alignment. Its popularity stems from its speed, local

alignment focus, and detailed statistical outputs, such

as E-values, that help assess the likelihood of similarity

occurring by chance. BLAST is not just a tool; it’s a

gateway to understanding the mysteries of life at the

molecular level. As technology advances, BLAST will

continue to play an important role in shaping the future

of biology and medicine.

BLAST: Speed and Speciﬁcity

BLAST uses short word matches as seeds to rapidly

ﬁnd regions of high similarity, and then extends these

alignments to produce local, statistically signiﬁcant

results. It is exceptionally useful for searching

large databases, oﬀering a practical balance between

sensitivity and computational speed, and has various

specialised variants for diﬀerent types of sequence

comparisons.

Example of BLAST Variants:

BLASTN: Used for nucleotide (DNA and RNA)

sequence database searches. It compares a nucleotide

query sequence against a nucleotide database.

BLASTP: Used for protein sequence database

searches. It compares an amino acid query sequence

against a protein sequence database -protein vs protein

search.

BLASTX: Compares the six-frame conceptual

translation products of a nucleotide query sequence

(both DNA and RNA) against a protein sequence

database. This is useful when you have a nucleotide

sequence that might contain a coding region, and you

want to ﬁnd similar proteins.

TBLASTN: Compares a protein query sequence

against a nucleotide sequence database dynamically

Figure 1: BLAST ﬁnds regions of similarity

between biological sequences. The program compares

nucleotide or protein sequences to sequence databases

and calculates the statistical signiﬁcance. Image courtesy:

https://blast.ncbi.nlm.nih.gov/Blast.cgi?CMD=Web&PAGE TYPE=BlastHome

translated in all six reading frames (both strands).

TBLASTX: Compares the six-frame translations

of a nucleotide query sequence against the six-frame

translations of a nucleotide sequence database. Given

its nature, it’s computationally intensive and less

commonly used than the other BLAST types.

Each type of BLAST is designed for a speciﬁc

purpose and can provide diﬀerent insights, depending

on the nature of the query sequence and the information

sought by the researcher. Protein BLAST ClusteredNR

is a collection of protein sequence clusters built from

the current default database, nr. The representative

sequence is chosen for each cluster, which is generally

well-annotated and indicates the function of the proteins

in the cluster, helping you focus on meaningful

biological insights and decreasing redundant results.

1.2 What is FASTA?

FASTA is both a ﬁle format and the name of the

ﬁrst software tool developed for sequence similarity

searching, preceding BLAST. The FASTA algorithm

breaks the query sequence into smaller patterns called

k-tuples or ktups (short sequence words), then searches

for matches in the database. These initial matches

are extended to generate full alignments. FASTA is

known for sensitivity, especially in detecting similarities

between less closely related sequences, though it may

be slightly slower than BLAST with large protein

databases.

1.3 FASTA as a Format:

The FASTA format is a universal sequence

representation standard, beginning with a description

line (>) followed by the sequence.

1.3 FASTA as a Format:

Table 1 BLAST vs FASTA: A Comparison

Feature BLAST FASTA

Main Use

Rapid

database

searching

for local

sequence

similarity

Sensitive

identiﬁcation

of homology,

especially in less

similar sequences

Algorithm

Uses ﬁxed-

size words

and ungapped

extensions

Searches with

short k-tuples,

then extends for

alignment

Speed

Faster

for large

databases,

especially for

proteins

Can be slightly

slower, but more

sensitive in some

contexts

Output

Detailed

alignments,

E-values,

statistics

Detailed alignments,

various scoring

matrices

Typical

application

Routine

database

searches and

annotation

pipelines

Research-focused,

historical analyses,

special cases

FASTA: Sensitivity and Versatility

FASTA, developed before BLAST, employs a

hashing strategy and k-tuples (k-tups) to identify regions

of similarity, often yielding greater sensitivity for certain

types of searches, particularly nucleotide alignments.

It produces optimised local alignments and is able to

capture less obvious similarities between sequences,

though it can be slower than BLAST for very large

datasets.

Fig. 2 Pair-wise sequence alignment ﬁnds regions

of similarity between two biological sequences. By

introducing gaps and aligning identical or similar

residues in columns.

Image courtesy: https://blast.ncbi.nlm.nih.gov/Blast.cgi?CMD=Web&PAGE TYPE=Blas

tHome

Sequence alignment facilitates the comparison of

multiple sequences, distinguishing conserved regions,

substitutions, and indels (insertions or deletions), which

in turn helps interpret genetic information and analyse

molecular evolution, gene function, and structural

biology.

section How do we compare sequences?

Seq 1: CTGCACTA

Seq 2: CACTA

or C---ACTA

Scoring tries to approximate evolution: scores for

substitutions and for gaps (insertions/deletions)

Scores = sum of terms for substitutions and for

gaps (sequence as character string)

Simplest scoring: 1 for a match, 0 for no match,

-1 for a gap.

CTGCACTA Score =5

CACTA

CTGCACTA Socre =2

C---ACTA

Database Similarity Searching- BLAST and

FASTA

Database similarity searching is the computational

process of comparing a query biological sequence

against a large repository of known sequences to identify

and retrieve those with signiﬁcant similarity, suggesting

evolutionary, structural, or functional relationships.

It employs sequence alignment algorithms like

BLAST and FASTA, which use heuristics (words)

to rapidly detect local regions of similarity, enabling

eﬃcient annotation, classiﬁcation, and characterisation

of unknown sequences within extensive biological

databases. This method is fundamental for genome

annotation, phylogenetic analysis, and molecular

biology research, as it links novel sequences to known

biological information through statistically validated

alignments.

Central Role in Biosciences

Both BLAST and FASTA continue to underpin

critical workﬂows in the biosciences and bioinformatics,

powering public databases, web-based tools, and

research pipelines for a range of applications, from gene

1.3 FASTA as a Format:

annotation to evolutionary analysis and metagenomics.

Their enduring inﬂuence and integration into nearly

every molecular biology lab and bioinformatics

curriculum validate their description as cornerstones in

the ﬁeld.

This article accurately reﬂects the signiﬁcance of

BLAST and FASTA in both their historical development

and ongoing applications within the biosciences and

bioinformatics.

References

https://blast.ncbi.nlm.nih.gov/Blast.cgi

https://ncbiinsights.ncbi.nlm.nih.gov/2025/05/22

/faster-better-results-protein-blast/?utm source=ncbi

linkedin&utm medium=referral&utm campaign=cl

usterednr-default-2025052

https://pmc.ncbi.nlm.nih.gov/articles/PMC44157

https://omicstutorials.com/essential-tools-and-s

oftware-in-bioinformatics-blast-fasta-and-clustal/

About the Author

Geetha Paul is one of the directors of

airis4D. She leads the Biosciences Division. Her

research interests extends from Cell & Molecular

Biology to Environmental Sciences, Odonatology, and

Aquatic Biology.

Part IV

Computer Programming

Minimizing Synchronization Overhead in

Parallel Computing

by Ajay Vibhute

airis4D, Vol.3, No.10, 2025

www.airis4d.com

1.1 Introduction

In recent years, parallel computing has emerged

as a cornerstone of high-performance software design.

With the slowdown of Moore’s Law and the proliferation

of multi-core and many-core processors, software

developers have increasingly turned to parallelism as

the primary path toward improving execution speed

and responsiveness. Whether it’s scientiﬁc computing,

machine learning, graphics rendering, or real-time data

processing, the demand for scalable parallel systems is

greater than ever.

At the heart of parallel programming lies a central

tension: while dividing work across multiple processors

can signiﬁcantly accelerate computation, coordinating

the actions of these processors often introduces overhead

that can limit, or even negate, the beneﬁts of parallel

execution. This coordination—commonly referred

to as synchronization—is essential to maintaining

correctness when threads or processes interact with

shared data. However, the cost of synchronization

grows rapidly with the number of processors, especially

when access patterns are not carefully managed.

Traditional synchronization mechanisms, such

as mutexes, barriers, and critical sections, ensure

consistency but also force threads to wait, serialize

execution, or compete for shared resources. In high-

contention scenarios, this can result in performance

bottlenecks that dramatically reduce the overall

eﬃciency of the system. Even ﬁnely tuned

parallel algorithms may fail to scale as expected if

synchronization overhead is not minimized.

Understanding how to design parallel programs

that reduce or avoid unnecessary synchronization is

therefore a critical skill for developers working in high-

performance and scalable computing. This involves

more than just choosing the right tools—it requires a

deep understanding of how parallel architectures behave,

how memory is shared and accessed, and how threads

interact under the hood.

This article explores the various forms of

synchronization overhead encountered in parallel

computing and introduces practical strategies for

minimizing their impact. Through conceptual diagrams

and real-world examples, we’ll highlight common

pitfalls and demonstrate how techniques such as data

partitioning, lock-free programming, and asynchronous

execution can lead to more scalable, eﬃcient parallel

systems.

1.2 Why Synchronization Hurts

Performance

Parallel programming is fundamentally about

maximizing concurrency while preserving correctness.

To ensure that multiple threads or processes don’t

interfere with each other when accessing shared data,

synchronization mechanisms—such as locks, mutexes,

and barriers—are introduced. While these tools

are necessary, they also become a major source of

ineﬃciency in parallel systems.

1.3 Reducing Synchronization Overhead

Figure 1: Contention and Blocking: Only one thread

can access the critical section at a time. Others must

wait.

One of the primary performance costs arises

from contention. When several threads attempt to

access the same critical section, only one can proceed

at a time, forcing others to wait, ﬁgure 1. This

introduces serialization in what should be a concurrent

program. As the number of threads increases, the time

spent waiting often grows disproportionately, reducing

the overall speedup and, in extreme cases, causing

performance to degrade.

Synchronization can also trigger expensive context

switches. When threads block while waiting for locks,

the operating system may preempt them and switch

execution to other threads. While this ensures system

responsiveness, the frequent saving and restoring of

thread states adds latency and consumes valuable CPU

cycles, especially in high-contention scenarios.

Even in seemingly well-parallelized code,

performance can suﬀer due to false sharing—when

threads operate on diﬀerent variables that reside on

the same cache line. In such cases, writes from one

thread can invalidate the cache lines of others, leading to

increased memory traﬃc and degraded cache eﬃciency,

despite the absence of explicit data sharing.

Another challenge is barrier synchronization. In

many parallel algorithms, threads must wait at ﬁxed

synchronization points until all others reach the same

state. If some threads complete their tasks earlier,

they sit idle, waiting for the slowest thread. This

imbalance—often caused by varying workloads or

memory latency—reduces overall throughput and

wastes computational resources.

Underlying all these eﬀects is a limitation

described by Amdahl’s Law: the speedup of a parallel

program is bounded by the fraction of code that must

be executed sequentially. Every synchronized section

eﬀectively acts as serial code, creating bottlenecks

that scale poorly as more cores are added. Even a

small portion of synchronization can cap the theoretical

maximum performance.

Lastly, synchronization adds complexity to parallel

software design. It increases the risk of deadlocks,

priority inversion, and non-deterministic behavior,

making programs harder to develop, debug, and

maintain. These hidden costs—combined with

the direct performance penalties—make minimizing

synchronization not just an optimization, but a necessity

for building truly scalable parallel systems.

1.3 Reducing Synchronization

Overhead

Synchronization is often a necessary evil in parallel

programming, but its cost can be signiﬁcantly reduced

through careful design and strategy. By minimizing

contention, reducing the frequency and duration of

synchronization, and leveraging modern concurrency

techniques, developers can improve the scalability and

performance of their parallel applications. Below, we

explore ﬁve key approaches to reducing synchronization

overhead.

1.3.1 Avoid Shared State

One of the most eﬀective ways to reduce

synchronization is to avoid shared state whenever

possible. By designing algorithms and data structures

that allow threads to operate on independent, thread-

local data, the need for coordination through locks

or barriers diminishes,ﬁgure 2. This approach

minimizes contention and eliminates many sources

of synchronization-related delays.

Techniques such as data partitioning, functional

programming paradigms (immutable data), or

employing message passing instead of shared memory

can help reduce or even eliminate shared mutable state.

When threads don’t have to coordinate access to data,

they can run truly concurrently, boosting throughput

and scalability.

1.3 Reducing Synchronization Overhead

Figure 2: Thread-local storage: each thread operates

on private data, avoiding the need for locks.

Figure 3: Partitioned access to shared array: diﬀerent

threads operate on disjoint segments.

1.3.2 Partition Data for Independent Access

When data cannot be fully private, it can often

be partitioned so that each thread works on a separate

region. This avoids contention while still enabling use

of shared structures.

1.3.3 Use Lock-Free Techniques

When shared state cannot be avoided, lock-

free programming oﬀers an alternative to traditional

locking mechanisms. Lock-free algorithms use

atomic operations provided by modern CPUs—such

as compare-and-swap (CAS)—to ensure consistency

without putting threads to sleep or forcing them to wait.

Lock-free data structures like queues, stacks,

and counters can dramatically reduce blocking and

contention. Because threads can retry failed operations

without holding locks, the overall system tends to be

more responsive and scalable, especially under high

concurrency.

However, lock-free programming requires careful

design and understanding of memory models and

atomicity guarantees, as it can introduce subtle

correctness challenges such as the ABA problem.

Nevertheless, for many performance-critical sections,

it is a powerful technique to reduce synchronization

overhead.

1.3.4 Minimize the Use of Barriers

Barriers ensure that all threads reach a

synchronization point before any continue, but they

can introduce signiﬁcant idle time. To minimize this

overhead, it is beneﬁcial to reduce the number of barrier

synchronizations in a program.

This can be achieved by restructuring algorithms

to increase asynchronous execution, allowing threads

to proceed independently as much as possible. For

example, techniques like pipelining or overlapping

communication with computation can help.

Additionally, replacing global barriers with more

ﬁne-grained synchronization mechanisms—such as

point-to-point signals or lock-free queues—can improve

resource utilization and reduce waiting times.

1.3.5 Delay or Merge Synchronization

In some cases, synchronization can be delayed or

combined to amortize its cost over multiple operations.

Instead of synchronizing after every small step, threads

can perform a batch of operations independently and

then synchronize once.

This batching approach reduces the frequency of

synchronization events and the associated overhead.

For example, instead of locking shared data for every

update, threads can accumulate updates in local buﬀers

and apply them collectively at synchronization points.

Merging synchronization operations can also

improve cache locality and reduce contention hotspots

by spreading updates over time, thus smoothing out

bursts of synchronization activity.

1.3.6 Fine-Grained Synchronization

Finally, when synchronization is unavoidable, ﬁne-

grained synchronization can help improve performance

compared to coarse-grained locking.

Instead of protecting large data structures or

entire algorithms with a single lock, breaking the

synchronization scope into smaller, more localized

locks allows multiple threads to proceed concurrently

when accessing diﬀerent parts of the data. This reduces

contention and increases parallelism.

1.4 Summary

However, ﬁne-grained synchronization increases

programming complexity and the risk of deadlocks or

other concurrency bugs. Careful design, thorough

testing, and the use of higher-level abstractions or

frameworks can help manage this complexity.

1.4 Summary

Synchronization is essential for correctness

in parallel programs but often limits scalability

and performance. By adopting strategies such

as avoiding shared state, employing lock-free

techniques, minimizing barrier usage, delaying or

merging synchronization events, and using ﬁne-grained

synchronization, developers can signiﬁcantly reduce

synchronization overhead. These approaches help

maximize concurrency, reduce contention, and improve

resource utilization, ultimately enabling parallel

applications to achieve higher speedup and better

scalability. Eﬀective management of synchronization

not only boosts performance but also simpliﬁes

debugging and maintenance, making it a cornerstone

of eﬃcient parallel computing design.

About the Author

Dr. Ajay Vibhute is currently working

at the National Radio Astronomy Observatory in

the USA. His research interests mainly involve

astronomical imaging techniques, transient detection,

machine learning, and computing using heterogeneous,

accelerated computer architectures.

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.