Cover page

This James Web Space Telescope image shows the gravitational lensing produced by the galaxy cluster SMACS

0723, which is at a distance of 4.6 billion light years from us. Image credit: NASA, ESA, CSA, and STScI

Light travels in straight lines, and that’s why we can see the images of objects in their proper proportion by

focusing them in our eyes. Assume that you look at an image formed on a cutglass vessel kept on the table in

your yard. The same trees and the sky would look distorted. Something similar happens when massive galaxies

curve and distort the space-time around them. In the image, distant galaxies appear distorted, as if they are all

elongated or curved. Albert Einstein predicted this in his General Theory of Relativity about a hundred years

ago. Fascinating science, isn’t it?

Managing Editor Chief Editor Editorial Board Correspondence

Ninan Sajeeth Philip Abraham Mulamootil K Babu Joseph The Chief Editor

Ajit K Kembhavi airis4D

Geetha Paul Thelliyoor - 689544

Arun Kumar Aniyan India

Jorunal Publisher Details

Publisher : airis4D, Thelliyoor 689544, India

Website : www.airis4d.com

Email : nsp@airis4d.com

Phone : +919497552476

Editorial

by Fr Dr Abraham Mulamoottil

airis4D, Vol.1, No.2, 2023

www.airis4d.com

Immersed Education and Learning

Introduction

The gulf between the academy and industry is the main topic which most industrialists and academicians

try to deliver in convocation lectures and education and industry policy discussions. Of late, ”innovation” and

”sustainability” have become the main subjects of debate for the industry and academia in the post-covid era. An

Industry without switching to continuous or permanent innovation seems to fail in success and gradually ceases

to exist. Global warming, shortage of natural resources, advancement of technologies, scarcity of suﬃcient

human resources, resistance to adopting and skilling for the future, etc., are a few issues that aﬀect innovation

and sustainability of the industry. Situations in education and the end of learning institutions are dramatically

changing. The new sources of knowledge and access to it unprecedentedly revolutionised. The aim of education

to seek a job has slowly evolved towards focusing on entrepreneurship and start-ups. Thus, innovation and

sustainability are buzzwords, targets, missions, and aspirations for the industry and the academy.

Immersed Education

An Immersed learning ecosystem bridges the gap between industry and academia. Immersed education

or learning is familiar to many American universities in the context of Industry-Institute-Interaction discussion.

Its core aim is to innovate collaboratively with the young brains of academia and the professional industry to

sustain our shared future.

New Resources

Immersed learning is also essential in ﬁnding new resources for humanity and industry to survive. United

Nations proposed a triple bottom line (TBL) for many countries’ post-colonial development and economy. The

colonies were rich resources for the development of colonisers. It also made colonies become poor, and their

natural resources started depleting. Our planet has always been a rich resource for our survival and industry

growth. But contemporary global issues are threatening and alarming for ﬁnding new resources. Many countries

and institutions need vision and plan to ﬁnd necessary resources.

First, there should be a vision that can make our planet secure and safe for humanity. Secondly, rather

than sustainability, humanity should emphasise well-being. Thirdly, society should realise that creating wealth

and proﬁt is not a sin. Additionally, acquiring reasonable and responsible prosperity by individuals provides

self-esteem, and it will build a society of generous and responsible citizens.

This triple bottom line (TBL) of development may give way to the quadruple bottom line (QBL), adding

the word plenitude (Planet, People, Prosperity and Plenitude). The planet and people are looking for new

resources; otherwise, humanity and the earth cannot sustain themselves. The third industrial revolution, or

information revolution, give rise to a knowledge society/economy. Besides the old economy’s labour and capital

(land), knowledge has become the primary means of production. Virtual resources and money are vital for

industry. Human resources and skilling becomes essential for a country’s ability to create new resources. In

this context, protecting the planet, building people as responsible citizens, and earning proﬁt also need new

resources. Reinvesting in education and students is fundamental for society and, in particular, to industry.

Industry shall shift from maximising proﬁts, the usual mantra for success, to Invest in Immerced education for

innovation in the existing industry. Immersed learning is beneﬁcial, proﬁtable and productive for the industry

and academia.

Plenitude

Human ingenuity can create plenitude or abundance through science, technology, and new resources.

Technological advancements dramatically change the world. The scientiﬁc community can abundantly develop,

manage and protect resources for the world. Covid vaccines and their management are excellent examples of

micro and macro development. The world started working on new issues: ”water, water everywhere but no drop

to drink”, ”Sun is giving plenty of energy, more than human needs”, ”Recycle, Reduce, Reuse”, etc.

SPICE

The immersed learning methodology is part of the Triple I: Industry-Institute-Interaction, collaboration

program with M. G. University, Kottayam and the Central Travancore Chamber of Commerce and Industry.

airis4D is an implementing agency of the Central Chambers for this model. This model showcases and shares

possibilities with industry and academia. As an immersed calling, the project provides exposure and experience

for students and professors and at the same time industry will collaborate with research on advancements in

science and technology.

Showcasing (Sharing) Possibilities and Immersed Calling for Experience (SPICE) Programme intends:

1. to develop a new knowledge-based business ecosystem;

2. to showcase MSME products and services;

3. to identify new resources (natural, knowledge, and HR);

4. to pin down prospects and possibilities of the existing enterprises; and

5. to encourage new people, especially students willing to invest or set up start-ups.

SPICE Programme Showcases

1. Students as a Source of Ideas and Inspiration

2. Industry as a Meeting Point of Challenges

3. University as Strength and Knowledge

4. Entrepreneurs as the Growth of a Country and

5. The Central Travancore region as a Land of Opportunity and Resources.

The SPICE Programme is planned for an ”Immersed Calling for Experience” in and with the existing

industry and business enterprises (Immersed Learning or Education).

iii

In the context of India’s jobless growth, ”what we need is not mass production, but production by the

masses”, as Mahatma Gandhi envisioned. The MSME sector needs to modernise with the help of digital

technology, professional management, and a better scale of operations. The Indian MSME sector has many

hidden treasures waiting for this makeover. Take the case of the Indian/Kerala restaurants/cuisine run with

limited resources, which can be revived by branding to the world stage as a global business in scale and

sophistication. Taiwan’s model of small enterprise assembling units can be incorporated into the Kerala

family/SHG (Kudumbashree) as an opportunity space. Digitisation, partnership, branding, and recognition

with the help of the Government and the Corporates will create a win-win situation. The Government of India’s

new e-commerce platform, ONDC (Open Network for Digital Commerce), is an ambitious and strategic vision

that could unleash India’s entrepreneurial dividend. (C. Sarat Chandran, LSE, The Hindu Sep.14, 2022)

The SPICE Programme can provide hands-on training and immersed experience or learning in upcoming

areas of AI, Robotics, Automation, Nanotechnology, IoT, Wearable Medical Devices, Bio 3D printing, etc., both

for the students, entrepreneurs, and the MSMEs to upgrade their enterprises.

Contents

Editorial ii

I Artiﬁcial Intelligence and Machine Learning 1

1 Data Driven learning 2

1.1 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

1.2 5Vs of Big Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

1.3 Signiﬁcance of Data in Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

1.4 Types of Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

1.5 Generating data using GAN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

1.6 Data for ChatGPT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

1.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

2 Machines that can understand Human Languages 8

2.1 Lexical Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

2.2 Syntactic analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

2.3 Semantic Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

2.4 Disclosure Integration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

2.5 Pragmatic analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

3 AI over a Coﬀee 14

3.1 The dumb and powerful computers of the past . . . . . . . . . . . . . . . . . . . . . . . . . . 14

3.2 New-Age computers that can learn . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

3.3 A.I. in science and engineering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

3.4 Primitive learning algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

3.5 Artiﬁcial Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

II Astronomy and Astrophysics 18

1 Applications of Satellite Imaging 19

1.1 Use cases of Satellite imaging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

1.2 Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

2 Eclipsing Binaries Part- 2 23

2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

2.2 Classiﬁcation according to the physical characteristics of the components . . . . . . . . . . . 23

2.3 Classiﬁcation based on the degree of ﬁlling of inner Roche lobes . . . . . . . . . . . . . . . . 23

2.4 Some examples of Eclipsing binaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

CONTENTS

III Biosciences 27

1 An introduction to Molecular Biology 28

1.1 Central Dogma . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

1.2 How does the Gene expression connected to the Central Dogma of Molecular Biology? . . . . 30

IV Computer Programming 32

1 Remote Sensing and Artiﬁcial Intelligence in Agriculture 33

V Fiction 36

1 Curves and Curved Trajectories 37

Part I

Artiﬁcial Intelligence and Machine Learning

Data Driven learning

by Blesson George

airis4D, Vol.1, No.2, 2023

www.airis4d.com

According to The Economist, the world’s most precious resource in the last century was oil but now it is data.

The signiﬁcance of data in business and industry can be understood from the fact that 97 percent of organisations

worldwide utilise it to fuel their commercial opportunities and 76 percent use it as a vital component of their

business plans. Moreover, data has became a tradable commodity that can be purchased and sold. The primary

source of revenue for IT behemoths such as Google and Facebook is the data of their customers. The fourth

industrial revolution (Industry 4.0), commonly referred to as the ”Data Revolution,” involves integrating new

technologies into manufacturing production processes and operations, including Internet of Things (IoT), cloud

computing and analytics, artiﬁcial intelligence, and machine learning. The main product or outcome of this

revolution thus far has been the Big Data, or the vast amounts of information that are currently available. In

today’s world, data is the true wealth. According to the deliberations at the World Economic Forum, whoever

controls data will have inﬂuence over the world in the future. Consequently, data has become the most important

factor in the modern world. Nonetheless, the life of mankind is determined by the control of data. The data we

generate controls or inﬂuences our decision-making.

1.1 Data

Data may be described as a formalised representation of facts, concepts, or instructions that is appropriate

for communication, interpretation, or processing by a human or a computer. It includes the information obtained

via observations, measurements, study, or analysis. They may include numbers, names, images, videos, and

even descriptions of objects.

1.1.0.1 Big Data

Big data refers to bigger, more complicated data collections, particularly those derived from novel data

sources. These data sets are so extensive that conventional data processing software is incapable of handling

them. The Big Data is featured by diﬀerent Vs.

1.2 5Vs of Big Data

1.2.1 Volume

By 2025, it is anticipated that the volume of data created, consumed, duplicated, and stored would exceed

180 zettabytes (1,000,000,000,000,000,000,000 (10

) bytes). The total quantity of data produced and consumed

in 2020 was 64,2 zettabytes. In the past eleven years, the amount of data created, captured, duplicated, and

utilised on a global scale has increased by about 500%. The primary eﬀect is a substantial rise in data use from

1.2 trillion gigabytes to 59 trillion gigabytes.

1.2.2 Velocity

The transition to digital business is creating a tremendous increase in the volume of data produced, used,

and stored by enterprises. Additionally, it is accelerating the velocity of data, or the rate at which data is

changing. The fast generation and storage of vast quantities of data in computer systems is known as data

explosion. Businesses worldwide have already accumulated more data than they can handle, in large part

because of how easily information can be shared across linked devices.

Figure 1.1: Graph illustrating the rise of creation of worldwide data. The sudden rise in the data is referred to

as data explosion. Image courtesy: Kleiner Perkins

1.2.3 Variety

Variety refers to the many types of data that are available. Mostly data is found in two diﬀerent forms-

structured and unstructured. Structured data refers to information that is concise, factual, and well organised.

Structured data is simple to search and evaluate. There is a standard format for structured data and it is

comprehensible to machines. Whereas, unstructured data is data that lacks a preset structure or model. It

demands a large amount of storage space and is diﬃcult to secure. It cannot be presented in a data model or

1.3 Signiﬁcance of Data in Machine Learning

schema. Therefore, it is diﬃcult to manage, analyse, or search unstructured data. It exists in several formats,

including text, photos, audio and video ﬁles, etc. Most databases cannot analyse unstructured data because the

vast majority of data analytics databases are created for structured data and are not suited to handle unstructured

data. Working with unstructured data presents various obstacles such as the diﬃculty to ﬁnd pertinent and

quality data, additional processing etc.

In recent years, two other Vs have emerged: value and veracity. Data has inherent worth. However, it is

useless until its worth is revealed. Equally vital are the veracity and dependability of your data.

1.3 Signiﬁcance of Data in Machine Learning

Machine Learning can be ultimately deﬁned as the process of creating learned models. A model is a set

of algorithms and parameters that are trained on a dataset in order to make predictions or decisions about new

data. Training and prediction are two important steps in machine learning.

In machine learning, training a model involves using a dataset to adjust the model’s parameters so that it

can accurately predict the outputs for the training data. The process typically involves feeding the model a set

of input-output pairs, and adjusting the parameters so that the diﬀerence between the model’s predicted output

and the actual output is minimized.

Once the model is trained, it can be used for prediction. This process involves providing the model with

new input data and having the model return a predicted output. For example, if the model is trained on a dataset

of images and their corresponding labels (e.g. ”dog” or ”cat”), the model can then be used to predict the label

of a new, unseen image.

It’s important to note that a model’s performance on the training data is not necessarily indicative of its

performance on new, unseen data. To evaluate a model’s ability to generalize to new data, a separate dataset

called the validation set is used. The model is trained using the training set and then the performance is evaluated

using the validation set.

A suﬃcient quantity of high-quality data is required to obtain a successful ML model. ”Suﬃcient data”

refers to having enough data to train a model so that it can accurately make predictions or decisions about new,

unseen data. The amount of data required for a model to be considered suﬃciently trained can vary depending

on the complexity of the model and the variability of the data. A simple model, such as linear regression, may

require less data to be suﬃciently trained than a more complex model, such as a deep neural network. Similarly,

a dataset with low variability may require less data than a dataset with high variability. In general, the more data

a model is trained on, the better it will perform on new, unseen data. However, at a certain point, adding more

data will not signiﬁcantly improve the model’s performance. This is known as the ”law of diminishing returns”

for data. Additionally, having a diverse dataset is also important to make sure that the model generalizes well

to new, unseen data, and not just memorizing the training data.

1.4 Types of Data

Machine Learning is often divided into three categories based on the training data. Labeled, partially

labelled, and unlabeled data are termed to as supervised, semi-supervised, and unsupervised data, respectively.

In each of these methods, a machine detects statistical patterns or regularities within the data.

In supervised learning, data is typically divided into two parts: the feature set and the label. The feature set

represents the characteristics or attributes of the data samples, while the label represents the class or category

that the data sample belongs to.

1.5 Generating data using GAN

Figure 1.2: Data can often be labeled, unlabelled, or a combination of both. Based on the kind of data

labeling, machine learning algorithms may be divided into three basic categories: supervised, unsupervised, and

reinforcement learning. In supervised learning, the aim is also to discover an appropriate label for each data point,

whereas, in unsupervised learning, data points are categorized into several groups. In reinforcement learning,

training is based on rewarding desired and penalizing undesired behaviors. Image Courtesy: https:Viso.ai/Deep-

Learning/Deep-Learning-vs-Machine-Learning

For example, in a image classiﬁcation task, the feature set would be the pixel values of the image, and the

label would be the class of the object in the image (e.g. ”dog”, ”cat”, ”car”, etc.).

During the training phase, the supervised learning algorithm uses the feature set and the corresponding

labels to learn the relationship between the features and the classes. Then, during the testing phase, the algorithm

uses the learned relationship to predict the labels of new, unseen data samples based on their feature set. In

classiﬁcation tasks, we approximate a mapping between discrete input variables (X) and discrete output variables

(y). In contrast, the output in a regression tasks are continuous variables.

There are many diﬀerent types of data and labels depending on the task and the domain. Here are a few

examples:

Image classiﬁcation: The data is typically images, and the labels are the classes of objects in the images

(e.g. ”dog”, ”cat”, ”car”, etc.).

Sentiment analysis: The data is typically text, and the labels are the sentiment of the text (e.g. ”positive”,

”negative”, ”neutral”).

Speech recognition: The data is typically audio, and the labels are the transcribed speech (e.g. ”hello”,

”goodbye”, ”yes”, ”no”).

Object detection: The data is typically images or videos and the labels are the bounding boxes and class

of the objects in the images or videos.

Time series forecasting: The data is typically a sequence of values over time, and the labels are the future

values.

Anomaly Detection: The data is typically a sequence of values over time, and the labels are the indicator

of normal or abnormal values.

1.5 Generating data using GAN

Figure 1.3: Image demonstrating the diﬀerent types of labels for various image segmentation algorithms. The

image as a whole is learnt to identify the animals in the image recognition task (top-left), whereas the coordinates

of the objects inside the image are the target labels in the object detection technique (bottom-left). Each pixel

of the image is assigned a label (color) in the Semantic segmentation method (top-right), resulting in objects of

the same class with the same color. Instance segmentation (bottom-right), on the other hand, treats all objects

as unique and assigns them separate labels. Image Courtesy: https://www.reddit.com/r/learnmachinelearning/

comments/kt0hov/diﬀerence-in-image-classiﬁcation-semantic

1.5 Generating data using GAN

A Generative Adversarial Network (GAN) is a type of deep learning model that is used for generating new,

previously unseen data samples that are similar to a given training dataset.

A GAN consists of two main components: a generator and a discriminator. The generator is trained to

generate new data samples that are similar to the training data, while the discriminator is trained to distinguish

between the generated samples and the real training data.

The data used for GANs can be any type of data that can be modeled as a probability distribution, such as

images, videos, audio, text, etc. For example, a GAN can be trained on a dataset of images of faces and then

used to generate new, previously unseen images of faces that are similar to the training data. The generated

images can be used in a variety of applications, such as image synthesis, image-to-image translation, and data

augmentation.

1.6 Data for ChatGPT

ChatGPT is a variant of GPT-3, which is a large-scale language model developed by OpenAI. Since its

release, GPT-3 and ChatGPT have received signiﬁcant attention in the media and among researchers, developers,

and the general public. It is considered as one of the most powerful language models to date, and it has been

widely used in a variety of applications, such as natural language understanding, text generation, question

answering, and more.

The training data for GPT-3 includes a wide variety of sources such as web pages, books, articles, and

more. The corpus of text used to train GPT-3 is estimated to be around 570GB of text, which includes a diverse

range of text styles, formats, and genres. The training data is also ﬁltered to remove any sensitive information

or personally identiﬁable information (PII) to protect user’s privacy.

1.7 Conclusion

The data is used to train the model to generate natural language text that is similar to the training data, and

the model is capable of understanding and responding to a wide variety of natural language inputs, including

questions, statements, and prompts.

1.7 Conclusion

Data is the foundation of machine learning and having high-quality data is crucial for training accurate

models. Without enough data, it can be diﬃcult to train a model that generalizes well to new data, and with

poor-quality data, it can be diﬃcult to train a model that makes accurate predictions.

There are several limitations in handling big data, including:

Storage: Storing and managing large amounts of data can be costly and challenging, especially when

dealing with structured and unstructured data.

Processing power: Processing and analyzing big data requires signiﬁcant computational resources, which

can be expensive and time-consuming.

Data quality: Ensuring the quality and accuracy of big data can be diﬃcult, as it often includes incomplete,

inconsistent, and duplicate information.

Data security: Protecting and securing big data from unauthorized access and breaches is a major concern,

especially when dealing with sensitive information.

Data privacy: Ensuring compliance with data privacy regulations, such as GDPR and CCPA, can be

diﬃcult when handling big data.

Data integration: Integrating and combining diﬀerent types of data from various sources can be challeng-

ing, and may require signiﬁcant eﬀort and specialized skills.

Scalability and Elasticity: As data grows, it’s important that the infrastructure should be able to grow

with it. This is diﬃcult to achieve with traditional infrastructure and can be costly.

Despite all of these challenges, emerging technologies such as quantum computing, cloud computing,

distributed computing, etc., as well as new breakthroughs in hardware technologies such as smart materials for

artiﬁcial intelligence, will aid in overcoming the problem.

References

1. The Economist ∥ The world’s most valuable resource

2. World Economic Forum Annual Meeting,2023

3. Statista — Worldwide Data Created

4. Medium.com- Data Science: The 5 V’s of Big Data

5. Amazon AWS-What is data labeling for machine learning?

6. Top 6 Big Data Challenges and Solutions to Overcome-Chandan Gaur

About the Author

Blesson George is currently working as Assistant Professor of Physics at CMS College Kottayam,

Kerala. His research interests include developing machine learning algorithms and application of machine

learning techniques in protein studies.

Machines that can understand Human

Languages

by Jinsu Ann Mathew

airis4D, Vol.1, No.2, 2023

www.airis4d.com

Phases of Natural Language Processing

Humans possess an extraordinary ability to perform complex tasks with ease. However, what comes

naturally to us can often prove to be a challenge for machines to learn. One of the most fundamental aspects

of human communication is the use of natural language, which includes both spoken and written forms.

Natural Language Processing (NLP), also known as text analytics, involves making natural language usable

for computational purposes. This is of great signiﬁcance in today’s world due to the vast amount of text

data generated globally.The process of Natural Language Processing (NLP) is divided into ﬁve main stages

or phases1.1. These phases start with basic word processing and continue with the identiﬁcation of complex

phrase meanings. This article will provide a brief overview of each phase and give examples of how they are

applied in information retrieval.

2.1 Lexical Analysis

Lexical analysis, also known as lexing or tokenization, is the ﬁrst phase of natural language processing

(NLP) in which raw text is processed into a sequence of meaningful elements called tokens. Tokens are usually

words, but they can also be punctuation marks, numbers, or other types of symbols that carry meaning in the

text. The goal of lexical analysis is to identify and extract the tokens from the input text and to provide a stream

of tokens that can be used as input for further analysis. This involves breaking the text into words, identifying

punctuation marks and symbols, and removing any irrelevant characters or whitespace. The process of lexical

analysis can be broken down into several steps:

Character Stream

This is the process of converting input text into a stream of characters.

input text —- ”This is an example of character stream.”

output —- [’T’, ’h’, ’i’, ’s’, ’ ’, ’i’, ’s’, ’ ’, ’a’, ’n’, ’ ’, ’e’, ’x’, ’a’, ’m’, ’p’, ’l’, ’e’, ’ ’, ’o’, ’f’, ’ ’, ’c’, ’h’, ’a’,

’r’, ’a’, ’c’, ’t’, ’e’, ’r’, ’ ’, ’s’, ’t’, ’r’, ’e’, ’a’, ’m’, ’.’]

2.2 Syntactic analysis

(image courtesy: https://www.analyticsvidhya.com/blog/2021/05/natural-language-processing-step-by-step-guide/)

Figure 2.1: Phases of Natural Language Processing

Tokenization

This is the process of breaking a string of text into individual words or tokens.

input text —- ’This is an example of tokenization.’

Output —- [’This’, ’is’, ’an’, ’example’, ’of’, ’tokenization.’]

Normalization

This is the process of converting text into a standard format to improve its consistency and to make it easier

to analyze. Usually words are normalized by converting them into lower case. This is an important step in NLP

as it helps to reduce the dimensionality of the data, by reducing the number of distinct words, and to improve

the consistency and accuracy of the analysis.

input text —- ’This is an Example of Normalization’

Output —- ’this is an example of normalization’

Stemming

This is the process of reducing words to their base form, typically by removing inﬂectional endings such

as -ed, -ing, -ly, etc. Stemming does not always produce a valid word, it is more of a heuristic process and may

not always produce the correct root word.

Suppose we have the following words: ”computation”, ”compute”, ”computer”, ”computing”, ”computed”

All these words will be stemmed to comput.

Lexical analysis is a crucial step in the NLP pipeline because it provides the basic building blocks for

further analysis.

2.2 Syntactic analysis

(image courtesy:https://www.researchgate.net/ﬁgure/3-Syntactic-analysis-for-the-white-cat-sat-under-the-table ﬁg2 271764570

Figure 2.2: Syntactic analysis for “the white cat sat under the table”

2.2 Syntactic analysis

Syntactic analysis, also known as parsing, is the second phase of natural language processing (NLP) in

which the structure of a sentence is analyzed and represented in a formal format such as a parse tree or a

dependency graph. The goal of syntactic analysis is to understand the grammatical structure of a sentence and

to identify the relationships between the words in the sentence. The syntactic analysis of the sentence ’ the

white cat sat under the table’ is shown in 1.2

The process of syntactic analysis can be broken down into several steps:

Part-of-speech tagging

This is the process of marking each word in a text with its corresponding grammatical category, such as

noun, verb, adjective, adverb, etc. Part-of-speech tagging is a common step in natural language processing

(NLP) and is used to analyze the grammatical structure of a sentence and to identify the relationships between

words.

Parsing

Parsing is the process of analyzing the grammatical structure of a sentence and representing it in a formal

format such as a parse tree or a dependency graph. The goal of parsing is to understand the syntactic structure

of a sentence and to identify the relationships between the words in the sentence.

Syntactic analysis is an important step in the NLP pipeline because it provides a deeper understanding

of the meaning of a sentence and allows for more advanced analysis such as semantic analysis and discourse

analysis.

2.3 Semantic Analysis

Figure 2.3: Example of Relationship Extraction

2.3 Semantic Analysis

Semantic analysis is the process of extracting meaning from text. It enables computers to comprehend and

interpret sentences, paragraphs, or entire documents by examining their grammatical structure and identifying

the connections between individual words within a speciﬁc context. The task of a semantic analyzer is to

evaluate the text for meaningfulness. While lexical analysis also deals with the meaning of words, it diﬀers from

semantic analysis in that it focuses on smaller units of text, such as individual words and phrases, while semantic

analysis looks at larger chunks of text, such as sentences and paragraphs. In other words, lexical analysis is

concerned with the individual meanings of words, while semantic analysis is concerned with how those words

relate to each other to convey meaning in a larger context. In order to understand the meaning of a sentence, the

following are the major processes involved in Semantic Analysis:

Word sense Disambiguation

Word sense disambiguation (WSD) is the process of determining the intended meaning of a word in context,

as words can have multiple meanings depending on the context in which they are used. WSD is an important

task in natural language processing (NLP) as it allows for a better understanding of the meaning of the text.

For example, Consider the sentence ”I am going to wind the clock” In this sentence, ”wind” could refer to the

movement of air or the act of tightening or loosening something by turning it. A dictionary-based approach

might look up the deﬁnition of ”wind” in a pre-existing dictionary and ﬁnd that it has multiple meanings,

including ”the movement of air” and ”the act of tightening or loosening something by turning it.” The context of

the sentence, ”I am going to wind the clock,” indicates that ”wind” is being used to refer to the act of tightening

or loosening something by turning it, not the movement of air. Thus, the ability of a machine to overcome the

ambiguity involved in identifying the meaning of a word based on its usage and context is called Word Sense

Disambiguation.

Relationship Extraction

Relationship extraction is the task of identifying and extracting relationships between entities mentioned

in a text. These relationships can be between people, organizations, locations, and other types of entities. As an

example consider the sentence, “ Isaac Newton is best known for developing laws of motion” . In this sentence,

the entities ‘Isaac Newton’ and ‘laws of motion’ are mentioned and relationship between them is developed1.3.

2.4 Disclosure Integration

In conclusion, semantic analysis is the process of drawing meaning from text. It is an important step in

natural language processing (NLP) that allows computers to understand and interpret sentences, paragraphs, or

whole documents.The end goal of semantic analysis is to extract the meaning of the text and to make natural

language usable for computational tasks. This enables various applications such as text summarization, question

answering, machine translation and more.

2.4 Disclosure Integration

Disclosure integration is a process in which information from various sources is brought together and

presented in a cohesive and organized manner. The goal of disclosure integration is to make the information more

easily accessible and understandable for the intended audience. It emphasizes the importance of understanding

the context and structure of communication. Actually disclosure integration uses the relationship between

preceding and succeeding statements to generate the meaning of a current statement. This approach is guided

by a set of predeﬁned rules and follows an organized methodology. For example, the sentence ”Tom met with

an accident” can suggest that Tom was the victim of an unfortunate event, but by adding ”because he was over

speeding” it implies that the accident was caused by his own actions. Without the context of him over speeding,

the sentence only states that he met with an accident and it could be caused by other factors or parties. This

illustrates how language context and structure can aﬀect the interpretation of a statement.

2.5 Pragmatic analysis

Pragmatic analysis is the examination of how language is used in real-world situations, taking into account

the practical and logical aspects of communication. This level of language processing involves utilizing real-

world knowledge and understanding how it shapes the meaning of the communication. It delves into the

contextual dimension of the text, uncovering deeper levels of meaning that require an extensive understanding

of the world and context. For example, consider the sentence ”I’m just kidding” In this sentence, the speaker is

indicating that the previous statement was not meant to be taken seriously, and is intended to be interpreted as

a joke. The meaning of this sentence is derived from the context in which it is used, such as the speaker’s tone

of voice and nonverbal cues, as well as the listener’s understanding of the conversation.

In conclusion, pragmatic analysis is a crucial aspect of natural language processing (NLP) that studies how

language is used in context. It enables computers to understand the intended meaning of sentences, beyond

their literal meanings, by taking into account the intentions of the speaker, the background knowledge of the

listener, and the social and cultural norms that inﬂuence communication. Pragmatic analysis can involve several

tasks such as speech act recognition, implicature, presupposition, deixis, and sarcasm and irony detection.

This approach is important for NLP applications such as sentiment analysis, text summarization, and dialogue

systems, as it allows for a deeper understanding of how language is used and provides insight into the social and

cultural factors that inﬂuence communication.

Natural Language Processing (NLP) is a complex ﬁeld that involves several stages or phases to analyze

and understand human language. These phases include lexical analysis, syntactic analysis, semantic analysis,

pragmatic analysis, discourse analysis, and others. Each phase is designed to extract speciﬁc information from

text and build upon the information gathered in previous stages. By using a combination of these phases, NLP

allows computers to understand, interpret, and generate human language. This is crucial for a wide range of

applications such as text summarization, sentiment analysis, question answering, machine translation and many

2.5 Pragmatic analysis

more. Understanding these diﬀerent phases of NLP is essential for anyone looking to work with or develop

natural language processing systems.

References

Natural Language Processing Step by Step Guide,Analytics Vidhya,Amruta Kadlaskar,May 2021

Natural Language Processing - Semantic Analysis,Tutorials Point

A Guide To The 5 NLP Phases,Algoscale, May 2022

Understanding Semantic Analysis Using Python — NLP,Daksh Trehan, Roberto Iriondo,Towards AI,May

2021

INTRODUCTION TO DIFFERENT LEVELS OF NATURAL LANGUAGE PROCESSING,Data science

prophet,Vrutti Tanna, March 24, 2021

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.

AI over a Coﬀee

by Linn Abraham

airis4D, Vol.1, No.2, 2023

www.airis4d.com

This article is a coﬀee table discussion between two friends, Annie and Marty. Annie is an inquisitive

learner who just met her long-time friend Marty, who is doing his PhD. in artiﬁcial intelligence. Annie is amused

by the sudden rise of Artiﬁcial Intelligence and wants to understand the underlying concepts or principles that

drive its development. She also wants to be abreast of the developments in the ﬁeld that have led to the current

state of aﬀairs. The conversation is divided into sections for the reader to follow along easily.

3.1 The dumb and powerful computers of the past

Annie: Hi Marty. Can you explain to me what this whole A.I. thing is? What is all the buzz about?

Marty: Ya sure. We are probably on the brink of another industrial revolution. The invention of the

steam engine is considered to have brought about the ﬁrst industrial revolution. The invention of electricity was

another big step that radically changed the nature of machines. The invention of the transistor marked the third

industrial revolution, or what we call the digital revolution. These transistors power the I.C.s or chips found in

most electronics nowadays, including modern computers. The new revolution we are talking about has to do

with the age of intelligent machines, and A.I., as you might know, stands for Artiﬁcial Intelligence.

Annie: Oh! intelligent machines, that’s what it is about. But I was under the impression that computers

were already pretty intelligent. I saw this movie recently, ”The Imitation Game,” where they talk about the

history of computers and how they were developed during the Second World War era to break the secret code

used by the Germans. Is that true?

Marty: Yes, it is very accurate. It is also true that the computers of yesteryears were indeed very capable.

They have helped astronauts land on the moon, enabled metrologists to predict the weather, and cosmologists to

simulate the universe’s evolution. As late as 1997, a chess-playing computer called Deep Blue was developed,

which beat the reigning world champion Gary Kasparov in his own game. But all of these were done using

brute-force techniques and not by intelligent machines.

3.2 New-Age computers that can learn

Annie: Oh yeah, I remember hearing about Deep Blue beating Kasparov in the news. So do you mean to

say that it was not an intelligent computer that did that?

Marty: Yes.

3.3 A.I. in science and engineering

Annie: So what are these intelligent machines capable of doing that the previous-generation computers

couldn’t?

Marty: Even the most sophisticated machines of the previous generation failed spectacularly at tasks that

even a ﬁve-year-old kid could do. These are things like image recognition, speech recognition, and natural

language processing. Today’s machines are considered intelligent because they are able to do a lot of such tasks

that were considered too hot to handle for the older generation of computers.

Annie: Oh burn! So how do the new-age computers shine where previous-generation computers failed?

Marty: Traditional computers were powerful because of two signiﬁcant things - brute strength and explicit

algorithms. The brute strength of the computer comes from its mind-boggling speed; the modern computer is

capable of executing about 5 billion instructions per second, and it’s able to crunch large numbers for hours on

end without tiring out or getting brain fatigued. An algorithm is something like that recipe you use for cooking

a Shakshouka. A set of rules or instructions given to the computer to solve a speciﬁc problem. When it is

written out in a language that the computer can understand, we call it computer code. With new-age computers,

however, we don’t need such algorithms. Instead, we have something called learning algorithms.

Annie: So why are learning algorithms important for creating intelligent machines?

Marty: If we only had computers that relied on explicit algorithms, that would mean that computers could

only solve problems that humans knew how to solve in the ﬁrst place. When it comes to intelligence, you see

that although human intelligence has a lot to do with evolution and our genes, one deﬁning characteristic of

intelligence is the ability to learn from experience. A child’s brain is somewhat of a clean slate that has just

the ability to learn from its environment, hard-wired into it. Everything else that makes it intelligent is learned

during the course of its life, either from its own experiences or from the experiences of others.

Annie: But even if they could achieve these tasks, wouldn’t that make these computers only as bright as a

ﬁve-year-old?

3.3 A.I. in science and engineering

Marty: You may be wrong about that. The ability to learn, paired with the brute strength of computers,

results in a deadly combination. Learning to do even the most mundane tasks that people do every day can spark

a revolution. The reason for all these fancy applications and the hype that A.I. has been generating recently has

to do with people trying to build learning capabilities in their respective domains. Self-driving cars, language

translators, spam detection, recommendation engines, etc., are all such examples. But the same techniques can

be applied in scientiﬁc research, leading to even more exciting applications.

Annie: Ohh. Using A.I. for research? That sounds exciting. What are the research problems that can be

tackled using A.I.?

Marty: Glad you asked. There are lots of such applications. For instance, in medical research, they are

used for cancer diagnosis. Using C.T. scans from both people identiﬁed with cancer and similar scans from

healthy people, an intelligent machine can be trained to identify patterns in the data that evade even the best

oncologists. A.I. is used for drug discovery to identify new drug candidates and predict how they will interact

with biological systems. A.I. systems have been used to analyze large amounts of genetic data to identify genetic

variants associated with speciﬁc diseases and predict an individual’s risk of developing a disease. A key part

of such research involves the 3D structure identiﬁcation of proteins from their 2D images, and A.I. is used

to do exactly that. In astrophysics, you might remember that the Nobel prize was given for the discovery of

gravitational waves.

3.4 Primitive learning algorithms

Annie: Yeah I remember that. Those were theoretically predicted by Albert Einstein but not observed

until recently, right?

Marty: Yes. And even there, A.I. was used to identify the true signals buried under a lot of noise. They

are also used to analyze large amounts of telescope data to study the properties of stars, galaxies, and other

celestial objects.

3.4 Primitive learning algorithms

Annie: Wow. All these sound exciting. So if these ”learning algorithms” are the workhorses behind all of

these things that A.I. is capable of achieving, what are they actually, and how do they work?

Marty: Instead of having domain-speciﬁc rules hard-coded into the machines, the learning algorithms

enable computers to make blunders, evaluate the cost of their errors by comparing them with true values, and

keep on iterating until the accuracy becomes suﬃciently high. In most of these cases, the computers try to

arrive at rules by ﬁnding patterns in the data shown to them. Remember those questions that often come up

in competitive exams where a number is omitted from a sequence of numbers, and we are asked to predict the

missing one?

Annie: Yup. Those are really hard at times.

Marty: Ya. But those are a piece of cake if you use learning algorithms to solve them. The curve ﬁtting

you learn in mathematics is an example of such a learning algorithm.

Annie: Isn’t curve ﬁtting what we do when you take observations on graph paper during a science

experiment and later try to ﬁt a curve that accommodates all these data points?

Marty: Exactly.

Annie: So are these the kind of algorithms that A.I. nowadays uses?

Marty: Actually, curve-ﬁtting is one of the simplest and most primitive learning algorithms. Many years

of research in this ﬁeld have led to better learning algorithms.

Annie: But what do you mean by a better algorithm?

Marty: Curve-ﬁtting is mostly used in cases where the form of the relationship between the variables is

known apriori. Most often than not, it is used in cases where the relationship is linear. Additionally, these

methods are sensitive to the presence of outliers or noise in the data.

3.5 Artiﬁcial Neural Networks

Annie: So what better algorithms have people come up with until now?

Marty: The state-of-the-art in artiﬁcial intelligence is what we call artiﬁcial neural networks or ANN. It is

a kind of bio-mimicry where the algorithm is derived from the working of the human brain, which is basically

a large network of neurons. Although our knowledge about the brain is quite limited, the general idea we have

is that the learning process in the brain is related to what we call neuroplasticity.

Annie: And what do you mean by neuroplasticity in our brains?

Marty: Our brain is a large collection of neurons. The term ”large” would be an oversimpliﬁcation;

typically, there are about 100 billion neurons with about 100 trillion synapses, which are the connections

between the neurons.

Annie: Wow! That’s such a mind-boggling number.

Marty: Yes it is. Each time a child sees an image, say that of a dog, some of these neurons ﬁre together.

The neurons that ﬁre together become well-connected. The connection strength between certain other neurons

3.5 Artiﬁcial Neural Networks

might decrease. In this way, the idea of a dog is stored in our brain in the strength of the synapses between

individual neurons.

Annie: Oh. So that is probably the reason why diﬀerent areas of the brain store diﬀerent memories and

have diﬀerent functions.

Marty: Yes. And as more and more data about dogs comes in through images, the brain can learn a very

strong pattern that will enable it to identify all kinds of dogs in the future, even those it hasn’t seen yet.

Annie: So why does my one-year-old nephew mistake the moon for a ball?

Marty: Yes. That can happen in the initial phases of development when the brain is slowly learning to

identify certain features that can help it to distinguish between objects. As more and more data ﬂows into the

brain, it can identify objects at a distance and quickly conclude that it’s more probable to be something in the

sky rather than a ball.

Annie: Oh, this conversation has been very interesting. When can we talk more about this?

Marty: Soon enough I guess.

Annie: Then it’s a bye for now. Bye, Marty.

Marty: Bye Annie.

About the Author

Linn Abraham is a researcher in Physics, specializing in A.I. applications to astronomy. He is

currently involved in the development of CNN based Computer Vision tools for classiﬁcations of astronomical

sources from PanSTARRS optical images. He has used data from a several large astronomical surveys including

SDSS, CRTS, ZTF and PanSTARRS for his research.

Part II

Astronomy and Astrophysics

Applications of Satellite Imaging

by Robin Jacob Roy

airis4D, Vol.1, No.2, 2023

www.airis4d.com

Satellite remote sensing is the scientiﬁc ﬁeld that combines the knowledge, analysis and correlation of

natural phenomena with measurements of electromagnetic energy that comes from or reﬂects oﬀ the Earth’s

surface or atmosphere. Sensors onboard the satellites take measurements of large number of areas on the earth’s

surface and present the data in the form of imagery. Satellite images play a crucial role in understanding and

observing the earth. Satellite images can be used to track the physical environment (water, air, land, vegetation).

It can also be used to measure, identify and track human activity.

Satellites are placed in orbit around the Earth, at various altitudes and angles, depending on the speciﬁc

mission of the satellite and they collect information about the earth using sensors or cameras mounted on them.

Satellite sensors/cameras measure the amount of reﬂected or emitted energy in a speciﬁc wavelength of light

along the electromagnetic spectrum to obtain information about Earth’s atmosphere, land or ocean. When the

photons strike the satellite sensors, they generate an electrical charge in the detector that is proportional to the

amount of incoming light. The sensor electronics read the value of the electrical charge and convert it to a

digital signal. Information from these sensors are turned into radio waves and is transmitted to antennas on the

ground. These radio signals are then processed and analyzed further to create the detailed maps and images

of the Earth’s surface. Figure 1.1 explains the various steps involved in the data capture and processing of the

Geostationary Operational Environmental Satellite (GOES), operated by the United States’ National Oceanic

and Atmospheric Administration (NOAA).

Figure 1.1: Process for translating satellite data into imagery.

1.1 Use cases of Satellite imaging

Figure 1.2: Deforestation over the Amazon Rain-

forests.(Source: NASA earthobservatory website)

Figure 1.3: A view of the eastern part of the Sun-

darbans in Bangladesh showing seasonally ﬂooded

river basins.(Source: World Bank Blogs)

1.1 Use cases of Satellite imaging

Satellite imagery has a wide range of use cases, from environmental monitoring and natural resource

management to urban planning and disaster response. One of the most important uses of satellite imagery is

environmental monitoring and natural resource management. Satellites can be used to monitor the health of

forests, wetlands, and other ecosystems, as well as track changes in land use and land cover. This information can

be used to identify areas that are at risk of degradation, and to plan and implement conservation and restoration

eﬀorts. In addition, satellite imagery can be used to monitor the health of crops, which is important for food

security and sustainable agriculture. Figure 1.2 shows the extent of deforestation happening on the Amazon

rain forests over a period of time.

Another important use of satellite imagery is urban planning and infrastructure management. Cities are

constantly growing and changing, and satellite imagery can be used to track these changes and plan for future

growth. For example, satellite imagery can be used to map and monitor urban expansion, track changes in land

use, and identify areas that are at risk of ﬂooding or landslides. Figure 1.3 shows the regions of the Sundarbans

delta which are at a risk of ﬂooding. In addition, satellite imagery can be used to monitor the condition of

infrastructure such as roads, bridges, and buildings, which is important for maintenance and repair.

Satellite imagery can also be used in disaster response and humanitarian aid. For example, satellites can

be used to map the extent of damage from natural disasters such as ﬂoods, earthquakes, and hurricanes. This

information can be used to plan and coordinate response eﬀorts, and to provide assistance to those in need. In

addition, satellite imagery can be used to track the movements of refugees and internally displaced persons,

which is important for providing humanitarian aid.

Satellite imagery is also used in security, intelligence and military operations. Satellites can be used

for reconnaissance and surveillance, providing information about the location and activities of military units,

weapons systems, and other strategic assets. It can also be used for target identiﬁcation and battle damage

assessment. Figure 1.4 shows the Chineese invasion at the Indian border of Arunanchal Pradesh. There are

many other use cases of Satellite imaging, including:

Weather forecasting: Satellites can be used to gather data on atmospheric conditions, such as temperature,

pressure, and wind patterns, which can be used to improve weather forecasting.

Transportation: satellite imagery can be used to track traﬃc and monitor the movement of vehicles on

roads.

1.2 Challenges

Figure 1.4: Chinese enclave constructed within Indian territory in Shi Yomi district of Arunachal.

Oil and gas exploration: Satellites can be used to detect oil spills and monitor oﬀshore drilling platforms.

Mining: Satellite imagery can be used to identify areas of mineral deposits and monitor mining operations.

Archaeology and anthropology: Satellite imagery can be used to identify ancient ruins and track the

movement of people and resources over time.

Overall, satellite imagery plays a vital role in many diﬀerent ﬁelds and has a wide range of use cases. It provides

valuable information for environmental monitoring, urban planning, disaster response, and security operations.

As technology continues to improve, the capabilities of satellite imagery will continue to expand, making it an

increasingly important tool for understanding and managing our world.

1.2 Challenges

There are several challenges associated with taking satellite imagery, including:

Weather conditions: Cloud cover and atmospheric conditions can limit the ability of satellites to capture

clear images. This can be particularly challenging in areas that are frequently cloudy or have high levels of

atmospheric moisture.

Resolution and image quality: Satellites are typically limited by the resolution and quality of their

cameras or sensors. This can make it diﬃcult to capture highly detailed images of small or distant objects.

Cost: Launching and operating satellites can be expensive, which can limit the number of images that can

be captured and the frequency with which images are updated.

Data storage and transmission: The large amount of data generated by satellite imaging can be challenging

to store and transmit, requiring high-capacity storage systems and reliable communications networks.

Privacy and security: Satellites can capture images of sensitive areas such as military bases, government

buildings, and private property, raising concerns about privacy and security.

Geo-location and Image registration: Satellites capture images at diﬀerent times, altitudes, and angles,

which can make it diﬃcult to accurately locate and compare diﬀerent images of the same area.

Interpreting and analyzing images: The images captured by satellite need to be interpreted and analyzed,

which can be a complex and time-consuming process, requiring specialized knowledge and expertise.

Despite these challenges, satellite imagery has become an essential tool for a wide range of applications,

from monitoring crop yields and natural disasters to detecting changes in land use and tracking the movement of

1.2 Challenges

wildlife. Advances in technology, such as high-resolution cameras, improved data storage and transmission, and

machine learning, are helping to overcome these challenges and unlock new possibilities for satellite imagery.

References:

Transforming energy to imagery

Fifty Years of Earth Observation Satellites, Andrew J. Tatem, Scott J. Goetz, Simon I. Hay, American

Scientist, Volume 96, Number 5, 2008, 390

Applications of remote sensing

Mapping the Amazon

Rapid ﬂood response using Satellites

Major Limitations of Satellite images, Firouz A. Al-Wassai, N.V. Kalyankar

About the Author

Robin is a researcher in Physics specializing in the applications of machine learning for remote

sensing. He is particularly interested in using computer vision to address challenges in the ﬁelds of biodiversity,

protein studies, and astronomy. He is currently working on classifying satellite images with Landsat and Sentinel

data.

Eclipsing Binaries Part- 2

by Sindhu G

airis4D, Vol.1, No.2, 2023

www.airis4d.com

2.1 Introduction

Eclipsing binaries are one type of variable stars. We know that eclipsing binaries are binary stars in which

the components of this system eclipse each other as seen from earth. In the previous article named ” Eclipsing

Binaries” we explained what eclipsing binaries are, and brieﬂy explained about the change in the intensity of

eclipsing binaries due to transit. In the previous article, we also discussed the three main types of eclipsing

binaries based on the shape of the light curves. There is one more important category. EP type stars also belong

to this category. EP type stars show eclipses by their planets. There are other two types of classiﬁcation of

eclipsing binaries other than the classiﬁcation based on the shape of the light curves. The ﬁrst classiﬁcation is

based on the physical characteristics of the components and the second one is based on the degree of ﬁlling of

inner Roche lobes. They are described below.

2.2 Classiﬁcation according to the physical characteristics of the components

Based on the physical characteristics of the components of eclipsing binaries, they are classiﬁed into

diﬀerent categories. In GS type of eclipsing binaries, one or both the components are giant or supergiant. One

component may also be a main sequence star. PN systems have, among their components, nuclei of planetary

nebulae. WD systems contain white dwarf components. WR eclipsing binaries contain Wolf -Rayet stars

among their components. Another important type is RS Canum Venaticorum type systems. The spectra of

these systems contain strong Ca II H and K emission lines of variable intensity. This indicates the increased

chromospheric activity of the solar type. These systems exhibit radio and x-ray emission. Light curves of some

systems exhibit quasi sine waves outside the eclipse.

2.3 Classiﬁcation based on the degree of ﬁlling of inner Roche lobes

Eclipsing binaries are also classiﬁed into diﬀerent classes based on the degree of ﬁlling of inner Roche

lobes. To classify the physical conﬁguration of a binary, we have to determine whether it is a detached, semi

detached or contact system. If both components of a binary system are inside their Roche Lobes, then that

system is a detached system. In that case both components are physically separated and also non-interacting. In

the case of semi detached systems one of the companions ﬁlls it with Roche Lobe. Matter will ﬂow through the

2.4 Some examples of Eclipsing binaries

Figure 2.1: Eclipsing Binaries (Image Courtesy: Steve Howell/Pete Marenfeld/NOAO )

Figure 2.2: Roche-Lobe(Image Courtesy - Cosmos)

inner Lagrangian point onto the other star. In the case of contact binaries, both the components ﬁll or overﬁll

their Roche Lobe. Their surfaces are in physical touch. These components share a common outer envelope.

AR types are detached systems. Both components of this system are subgiants, not ﬁlling their inner

equipotential surfaces. In DM systems both the components are main sequence stars. They do not ﬁll their

inner Roche lobes. They are detached systems. DS systems contain a subgiant as one of their components.

Subgiant doesn’t ﬁll its inner critical surface. They are also detached systems. DW systems are similar to W

UMa systems in physical properties. But the components are not in contact as in the case of W UMa systems.

KE systems are contact systems of early spectral type (O-A). Both components of this system are close in

size to their inner critical surfaces. KW types are contact systems of the W UMa type. They have ellipsoidal

components of spectral type F0 - K. Main sequence stars are the primary components. Secondary components

lie below and to the left of the main sequence in the (MV, B-V) diagram. SD systems are semi detached systems.

In these systems, the surface of the less massive component is close to its inner Roche lobe. There are also the

combination of these three classiﬁcation systems.

2.4 Some examples of Eclipsing binaries

2.4.1 RZ Cassiopeia

It is visible with binoculars. Primary eclipses are 1.5 magnitudes in depth. Secondary eclipses are only 0.1

magnitude in depth. It belongs to the class EA+DSCT. Period is 1.1952503 days. It is an active semi-detached

Algol system. It shows complex features in its light curve. A pulsating star of the δ Scuti type is one of the

components of these systems.

2.4.2 U Cephei

U Cephei is an Algol type eclipsing binary. Brightness range is 6.7-9.3. Period of observation 2.493 days.

Eclipses last for approximately 9 hours. Primary is a main sequence star and secondary is a giant star.

2.4.3 RT Andromedae

RT Andromedae is an eclipsing binary with a long observation record. It belongs to the RS CVn binaries.

It consists of two stars. Their mass is about 1.1 solar mass and 0.8 solar mass. Flaring and spot activity is high.

It is due to the magnetic eﬀect which was caused by rapid rotation of these systems. Apparent visual magnitude

is 9.83 at minimum brightness and 8.97 at maximum brightness. Period is 0.6289216 days. It consists of a

G-type main sequence star and a K-type main sequence star.

2.4.4 TU Bootis

It is a short period system. Period is about 0.324 days. It is classiﬁed as a W Uma type eclipsing binary.

2.4.5 UU Lyncis

It is a barely detached EA type system. Components are of 1.4 and 0.6 solar mass. Each component is

critically close to ﬁlling their respective Roche lobes.

2.4.6 MY Cygni

A well-detached EA type system consisting of two nearly identical stars. Mass is about 1.8 solar mass.

Period is about four days.

2.4.7 KR Persei

A detached system consisting of nearly equal components. Spectral type is F5V. Period is almost exactly

one day. This system consists of two mid-F main-sequence stars.

2.4.8 RU Eridanis

The RU Eridanis system has a conﬁguration that is nearly overcontact. Here each component is either

ﬁlling or close to ﬁlling its Roche - lobe. They are short period systems with a period of about 0.63 days.

2.4 Some examples of Eclipsing binaries

2.4.9 YY Ceti

YY Ceti is a semi-detached Algol binary. Orbital period is approximately 19 hr (0.79 days). Both the

components are gravitationally distorted and that the cooler, less - massive secondary has ﬁlled its Roche lobe.

References

OBSERVATIONS AND MODELS OF ECLIPSING BINARY SYSTEMS,Jeﬀrey L. Coughlin

Eclipsing Binary Observing Guide,British Astronomical Association

Binary star, Wikipedia

GCVS variability types, Samus N.N., Kazarovets E.V., Durlevich O.V., Kireeva N.N., Pastukhova

E.N.,General Catalogue of Variable Stars: Version GCVS 5.1,Astronomy Reports, 2017, vol. 61, No. 1,

pp. 80-88 2017ARep...61...80S

The International Variable Star Index, AAVSO.

About the Author

Sindhu G is a research scholar in Physics doing research in Astronomy & Astrophysics. Her research

mainly focuses on classiﬁcation of variable stars using diﬀerent machine learning algorithms. She is also doing

the period prediction of diﬀerent types of variable stars, especially eclipsing binaries and on the study of optical

counterparts of x-ray binaries.

Part III

Biosciences

An introduction to Molecular Biology

by Geetha Paul

airis4D, Vol.1, No.2, 2023

www.airis4d.com

Cells are the fundamental building unit of living organisms. Cells contain organelles like the nucleus,

mitochondria and chloroplasts, endoplasmic reticulum, ribosomes, vacuoles etc. Of these, the nucleus is the

most important organelle because it carries DNA or deoxyribonucleic acid, which is the genetic material in all

living organisms. DNA carries the instructions for growth, development, functioning and reproduction. In this

article, we will explore how a living organism reads DNA information and uses it to synthesise the thousands

of diﬀerent proteins that make life possible. This is known as the Central Dogma in molecular biology.

1.1 Central Dogma

In molecular biology, the central dogma is the ﬂow of genetic information within a biological system.

DNA is found in every cell and is a read-only memory, meaning that it stays permanent with all the information

the living organism requires. This is why it is possible to reconstruct a living organism if its DNA information

can be recovered. Those who have seen the Jurassic Park movie might recollect this as the method they adopted

to bring back dinosaurs that went extinct millions of years ago. How is this possible? DNA is a code, and

several parts of it are masked from being used. The exposed regions of the DNA are called genes, and it is

the code in the genes that are used for the creation of proteins. The reading and the transfer of the information

are carried out by the RNA. The RNA that reads, decodes and transfers the information for protein synthesis

in the ribosome is called mRNA or messenger RNA. It is in these factories of ribosomes that protein synthesis

happens. This central Dogma of molecular biology was ﬁrst enunciated by Francis Crick in 1958 and redesigned

later in a Nature paper published in 1970.

Figure 1.1: Illustration of the central Dogma, the ﬂow of genetic information in a Biological system. The

data from the DNA is read (Transcription), the mRNA decodes it (Translation), and ﬁnally, the protein is

synthesised.(source: https://biologydictionary.net/central-dogma/)

1.1 Central Dogma

Let us look at the DNA in more detail. DNA, or Deoxyribonucleic acid, is a long-chain molecule that plays

a central role in life on earth. The information encoded in the strands of DNA controls the genetic makeup of

organisms. DNA is considered the pinnacle of Biology, and it is so powerful and incredible that scientists who

discovered it won the Nobel Prize.

DNA has an amazing double-helical structure, and it determines the characteristics of who we are. DNA

decides the colour of our eyes, the tone of our skin, the likelihood of developing certain diseases and even some

aspects of our personality. DNA is passed down from one generation to the next, and with it, all traits we see in

plants, people and animals. By doing research and studying DNA, we have cloned sheep, improved our crops,

made the discovery of new species, helped in investigations for determining the culprit of a crime and maybe,

if we desire so, like in Jurassic Park, can bring the dinosaurs back.

Is it not amazing to know that every creature, big or small, is created by the code encrypted in a molecular

chain that is only about 2.5 or 2.6 nanometers in size? A nanometer is 1/1000,000,000 of a metre!

Having understood what DNA is, let us lay out the basics of how DNA involves creating something that we

can see in real life. Like other processes inside our cells, it’s a complicated set of interrelated steps. Scientists

invented a term to describe the basic ﬂow of genetic information from DNA to our heritable traits. It’s called

the central dogma.

I shall try to explain it with a simple story that I found helpful in understanding it. Once, I made a trip to

my aunt. While there, she made me one of my favourite snacks (Protein): A ﬁsh sandwich! Obviously, I can get

ﬁsh sandwiches anywhere, but there’s something special about my aunt’s recipe. I really missed her sandwich,

so I wondered if I could learn to make it on my own. I asked her to give me the recipe, and she got out her giant

recipe book (DNA). She opened it to the right page (Gene expression) and handed me a pen and a recipe card

so I (mRNA) could copy the recipe down. Once I got home (Ribosome), I took out the card and made my own

Fish sandwich. I wasn’t using my aunt’s recipe book, but it didn’t matter. I still had the recipe, copied word for

word, and I made my breakfast taste just like aunts!

Figure 1.2: Represents the Protein synthesis in a cell connected with the cookbook, recipe & the ﬁsh sandwich.

The story is actually similar to the story of how DNA generates protein that makes life possible in all living

creatures, like animals, plants, and people. Just like my aunt’s recipe book, DNA is a set of instructions on how

to make something. That something, in the case of my story, was the Fish sandwich; but in the case of DNA,

it’s a protein. Protein is the product, the biological molecule that DNA provides instructions for.

Now, this story has an important ﬁrst part. I had to copy down only the essential part of my aunt’s recipe.

That was all I needed to prepare my favourite dish. In the same way, the mRNA had to copy only a very small

1.2 How does the Gene expression connected to the Central Dogma of Molecular Biology?

Figure 1.3: From the double stranded DNA,the genetic information is transcribed to mRNA by transcription and

from mRNA, the messages are decoded by the translation process to form the protein molecules.(Source:https:

//biologydictionary.net/central-dogma/)

part of the codebook which I call the DNA to be carried to the ribosome to synthesise the protein. In fact, the

DNA, like my home, exists at a diﬀerent place, relatively far from the ribosome where the protein is made. To

be more speciﬁc, the DNA ’lives’ inside the nucleus of a cell and proteins are made outside the nucleus, in the

ribosomes of the endoplasmic reticulum, seen in the cytoplasm. So, just like my recipe had to be copied, the

instructions in DNA have to be copied by mRNA and carried from the nucleus to the site where proteins are

made.

RNA is another kind of nucleic acid. It’s similar to DNA, but instead of being a double-stranded molecule,

it’s just a single strand of nucleotides. You can imagine it as one half of a ladder. RNA plays the role of my

recipe card by carrying the genetic code outside of the cell’s nucleus. Once in the cytoplasm, the instructions

for making proteins are read by the cell’s protein-building molecules.

Figure ?? illustrates the actual steps involved in the transfer of genetic information from the DNA by the

RNA to the ribosome’s protein-binding molecules.

The genetic information (base sequence) of a segment of DNA is ﬁrst copied to the RNA molecule. The

RNA molecules that have the encoded messages for the synthesis of proteins are called the mRNA, and the

process is called transcription. The mRNA molecules then leave the cell nucleus and enter the cytoplasm,

where they participate in protein synthesis by specifying the particular amino acids that make up individual

proteins, and the process is called translation.

1.2 How does the Gene expression connected to the Central Dogma of

Molecular Biology?

Gene expression is the message or the information encoded in the gene,when it is transformed to a function

or when the encoded information of a gene turns to a function. Genes encode proteins and proteins dictate

cell function. Therefore, the thousands of genes expressed in a particular cell determine what that cell can do.

Moreover, each step in the ﬂow of information from DNA to RNA to protein provides the cell with a potential

control point for self-regulating its functions by adjusting the amount and type of proteins it manufactures.

The central dogma shows how information is transferred from DNA to RNA to protein; when the cell

1.2 How does the Gene expression connected to the Central Dogma of Molecular Biology?

Figure 1.4: The Central Dogma Describes how a gene is ultimately expressed.(Source: https://socratic.org/

questions/how-is-gene-expression-related-to-the-central-dogma-of-molecular-biology)

receives a signal that a gene must be expressed, RNA Polymerase is recruited to the region of DNA where that

gene is located. It makes an RNA copy of that region of DNA, in a process called transcription. This RNA

is then transported out of the nucleus of the cell, and is translated into a protein by molecular machines called

ribosomes,where the proteins are synthesised.

References

https://doi.org/10.1371%2Fjournal.pbio.2003243

https://www.nature.com/articles/227561a0/

https://biologydictionary.net/central-dogma/

https://www.genome.gov/genetics-glossary/Central-Dogma

https://socratic.org/questions/how-is-gene-expression-related-to-the-central-dogma-of-molecular-biology

https://www.nature.com/scitable/topicpage/gene-expression-14121669/

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

Remote Sensing and Artiﬁcial Intelligence in

Agriculture

by Chandrashekharan C P

airis4D, Vol.1, No.2, 2023

www.airis4d.com

Water and air are indispensable for our sustenance. Nevertheless, life without agriculture is unimaginable.

Agriculture is our primary source of income. It plays a vital role in national revenue. Many major companies

in our country rely on agriculture for their raw material. Agricultural development is one of the most powerful

tools to end extreme poverty, boost shared prosperity and feed a projected 10 billion people by 2050.

However, agriculture is facing a signiﬁcant setback. There is increasing pressure from climate change,

biodiversity loss and soil erosion. Apart from this, lack of skilled labour, pests, diseases and change in growing

season pose their challenges. One-third of food produced globally is either lost or wasted. Addressing this is

critical to improving food and nutrition security and helping to meet climate goals, and reducing environmental

stress. There are more and more mouths to feed as the population is increasing at a fast pace. The article

demonstrates a personal story to explore modern technology like remote sensing and artiﬁcial intelligence as

promising solutions to these challenges.

Drones are a breakthrough in the agricultural sector by using remote sensing technology and precision

agriculture. Some typical agricultural applications for UAVs include pest control, plant health monitoring, soil

analysis and aerial survey.

Crops are susceptible to pathogens, fungi and insects and fast-spreading infections. These can be monitored

eﬃciently in real-time. Selective application of weedicides, fertilisers and micro-nutrients can also be made.

Artiﬁcial intelligence applications and implementation also are revolutionising the agricultural sector.

Various applications identify pests and diseases in crops with almost 98% accuracy. When waving a mobile

phone camera over a speciﬁc crop with a symptom, the nature of the problem will pop up on the image with

a bounding box around it. Best management practices can then be adopted based on the diagnosis. Similar

apps can be developed to estimate crops to detect weeds, deﬁciency of fertilisers and micro-nutrients, and pest

attacks. The interesting fact is that these apps can work even without the internet. Using such techniques, one

can detect diseases and plant growth from time to time and ensure ideal crop productivity. Another advantage

of these technologies is that production can be increased without consuming more land, reducing the impact of

climate change.

Being a farmer in Wayanad, the author has ﬁrsthand experience with the challenges farmers face today.

Keeping in pace with modern technology and having done a course in Drones for Agriculture and various

courses in AI, the author developed an interest in researching these technologies and applying them to the

plantation so that the future generation can manage the farm hassle-free and with interest.

Initially using a drone (DJI Mavic mini), around 200 images of 2.5 acres of a friend’s plantation were taken.

It was stitched into a single image using a photogrammetry tool. Vegetative indices like Excess Green(EG) and

NGRDI indicate the health and growth of vegetation. The vegetation index is the spectral calculation done using

the spectral bands of each pixel of the drone image. Drones with Near InfraRed (NIR) cameras can calculate

biomass measurements and plant stress and detect living vegetation.

Figure 1.1: Stitched image of the plantation.

Figure 1.2: NGDRI map.

Figure 1.3: Excess Green(EG) map.

In a following study, using a dji mavic mini camera, a complete project on detecting areca nut and coconut

seedlings in a mixed plantation were carried out.

The images were taken on a micro SD card using a drone ﬂying above the plantation at around 30 meters.

The images were then transferred to a laptop and labelled accordingly for training. The model training of the

images was done using YoLo v8 in google colab for 100 epochs.

Figure 1.4: Precision-Recall Curve.

The above model can be improved to detect various diseases in areca nut coconut and similar crops and

count diseased and healthy plants.

Figure 1.5: Detection of coconut and areca nut trees.

As millions of people from rural areas migrate to cities yearly, these modern technologies can inspire our

future generation to remain in agriculture.

About the Author

Chandrashekharan C P is an engineer by training but took over farming as his career. He is taking

mentorship at airis4D and has been instrumental in developing various application software using machine

learning.

Part V

Fiction

Curves and Curved Trajectories

by Ninan Sajeeth Philip

airis4D, Vol.1, No.2, 2023

www.airis4d.com

This article is a thought experiment on a virtual world with imaginary creatures capable of doing ﬁctitious

activities.

Episode 3

Characters:

1. Dot: a zero-dimensional creature

2. Angel the Light: a one-dimensional Angel

3. Mittu: a two-dimensional ant

4. Albert: a 3D boy.

Albert is an extraordinary young boy with inﬁnite energy, says his teachers. He is found to be doing

something all the time. His games are often a puzzle for his parents, and even his teachers often ﬁnd it diﬃcult

to answer his questions. He is a good boy, once his teacher said, except his interests are anchored to the wrong

goals. Not just his teachers, Albert hardly has any friends. He doesn’t care. He is in his world and plays alone

almost all the time in his imaginative universe.

Albert’s father is a craftsman. Though not well educated and concerned about what others spoke about his

son, he always invested some time in Albert, even during his busy schedule. Spinning tops were his masterpiece.

He would make a variety of them and sell them like hot cakes in the streets to win bread for the family. He

would also have something special for Albert every time, a spinning top with sophisticated craftsmanship. Since

everyone else at home blames the father for spoiling the child with his silly toys, exchanging gifts is a secret

between the father and the son. Albert would thus hide the toy and wait for the opportunity to vanish in solitude

to play with the fascinating toy his father had brought.

This time his father has gifted him a wobbling top that has small pellet holes and a dozen led pellets which

can be rearranged to create diﬀerent wobbling patterns as the top spins. Albert would play for days changing the

pellet combinations and observing the magical changes in the wobble, spin and stability of the top. He would

return home, hiding his toys in his secret drawer in the basement and none appreciated his craze for toys. His

mind would always be playing with those toys, and he would occasionally twist and turn like them, unknowingly

laughing and clapping his hands. His mother would cry and run to light candles at the altar in the church to

rescue her demon-possessed son, while the father and son would winkle their eyes. His father knew that those

were times when Albert would be crafting his mental picture for a new order of pellets that would make his toy

perform magic as it spun.

As usual, that afternoon after school and the evening snacks at home, Albert ran down with his toy to the

basement. The spinning wheel he had set on last day was still spinning with the tiny box at its edge. Albert sat

down with his new spinner and carefully stopped the spinning wheel with the tiny box.

Oh again! He could hear cries coming from the box. It was Light and Dot crying out on experiencing the

unknown hurting eﬀect on them. What happened? Albert’s looked in. He saw Mittu and asked why they were

crying. Mittu clariﬁed that they were experiencing some strange feelings when Albert stopped the wheel. What

feelings? asked Albert.

I think the eﬀect of stopping the spinning wheel, a consequence of the change in their state of motion,

explained Mittu.

Oh, really? But how can they experience it? They don’t have any mass. . . Albert got confused.

That’s the point, said Mittu. Light is a one-dimensional creature and hence cannot visualise curved paths.

So for him, it is not possible to see any change of state, and the experience gives a strange demonic feeling. For

Dot, it is even worse as he can’t even see what Light is able to do. Because I know that you have stopped the

spinning wheel, I realise it is a change in my state and can understand the Physics involved.

That’s interesting, said Albert. I wonder how you might feel on my wobbling spinner, he added.

What’s that? Asked all three. Instead of answering, Albert detached and tied the box to the edge of his

wobbling spinner. With a few attempts with the lead shots to balance the weight of the box, he let the spinner

wobble as it went around.

This time, all three, including Mittu, experienced a strange feeling.

Oh my! Mittu cried out. What have you done? Why I am having strange feelings

What do you see? Asked Albert.

I see that I am going around as before, but I have this strange new feeling that was not there before. I feel

like being pulled down and pushed out of myself. As if my world has started behaving strangely. He said.

You are also moving along a third axis, clariﬁed Albert.

Third axis? There is no third axis! Exclaimed Mittu.

Yes, there is! said, Albert.

A third axis? Cried out Light. I can’t imagine the second axis Mittu was telling me a while ago. He told

me to extrapolate my straight-line movement from every dot it makes to a new axis orthogonal. Absurd, but

seemed to make sense logically to explain the strange feelings I had. But now, for the same feeling that I have

now, why do you want me to imagine a third axis? Light objected.

No, no, this is diﬀerent. The second axis is still here, and this is something strange, said Mittu.

Alright, friends! Albert interfered. Mittu, did you tell Light to imagine lines from every dot on his line?

He asked Mittu.

Yes, I did. Said, Mittu.

Cool! Exclaimed Albert. He continued. Now imagine a dot orthogonal to every point on your surface.That

would produce a new surface like the one you already have.

Wow! But that’s just a multiverse. Like what I experience when I cross a wormhole. That does not explain

a new dimension. I am sure that there can’t be anything beyond two dimensions! Objected Mittu.

Wait, Mittu, please! Said, Albert. Imagine lines drawn instead of a single dot orthogonal to every dot on

your surface, insisted Albert.

That will all form lines on these two dimensions I already have. There can’t be anything orthogonal,

objected Mittu.

Apply the same logic you told Light to generalise his one dimension, suggested Albert. It might be diﬃcult

but mathematically feasible.

Yeah, that’s right. Mathematics always helps us to penetrate the darkness of our mental inability, Mittu

agreed. So, you are telling me that this torture i experience is due to my travel through the new dimension?

Why do all of we then experience it?

Good point, said, Albert. Every new dimension brings you a similar experience, but looking more

intensively helps you see the diﬀerence. They are not identical. The jerk in one dimension is not identical to

the centripetal force in two dimensions. Some are unidirectional, some have polarity, and some are universal,

concluded Albert.

You seem to have an answer for everything. Mittu shared his appreciation for Albert.

Not really, said Albert. I am trying to understand the mystic powers in my three-dimensional world. It

seems that as we move up the ladder of dimensions, we discriminate many things and get even more confused.

Albert expressed his concern. For Dot, everything is time-like. Light also experiences but realises the cause

for what Dot experiences. Yet he encounters an additional entity that puzzles him. But for you, Mittu, you

also experience everything that both Light and Dot experience but come across a third entity that is similar but

diﬀerent from the other two. I am wondering how higher dimensions might inﬂuence my world!

That’s a great thought, supported Mittu. Maybe you can help me demystify some of the challenging puzzles

like the many worlds and wormholes in my world, said Mittu.

Oh sure, Albert assured. The wormhole is just a slot I have put on this wheel to set the lead shots. It is like

a break in a line. When you asked Light to imagine a surface as lines attached to every dot in a line, you should

think of how the surface would look if there is a break, no dots at some part of the line. It will create a blank

region. Like Light may be able to turn around at the break of the line and walk back along the same line while

thinking that he is moving forward, your multiverse is just the same surface but seen as inverted!

However, the third dimension I told you about is formed by adding dots from all the dots on your surface

and extending them to form new lines and surfaces. That creates an additional surface distinct from the one you

are familiar with! Though not realised by you, it is the same world!! There is nothing like many worlds. You

are just moving to a diﬀerent surface in the world.

Oh, it’s too complex, said Mittu. I need time to think about it.

It is simple. The complexity is only because of the limitation of our brain to visualise something that has

a higher dimension, comforted Albert.

“So, for Dot, the world is a point. For Light, it is a line, and for me, a surface and what’s it for you?” asked

Mittu.

“It’s a cube or a sphere”, said Albert. “ A cube if I extrapolate it over a surface and a sphere if I assume

that there is no preferred direction and that I can be equally far from the edges of every point in my world”, he

clariﬁed.

“If I make such a consideration, my world should be circular!” said Mittu.

“Yes, indeed”, aﬃrmed Albert. But Light and Dot did not understand what those mathematicians were

talking about. Their sleepy eyes, too, wobbled with the spinning top.

“Did you say that Light had a strange feeling while on the spinning wheel?” asked Albert.

“Yes”, aﬃrmed both Mittu and Light.

“That’s fascinating news”, exclaimed Albert. “I could understand if a massive object experiences a force

when it moves along a curved path. But if you could experience it, I may need to change my entire concept of

mass!” He said.

“Oh, yes, I got your point”, said Mittu. “A massless object can’t experience centripetal or centrifugal

force!” he clariﬁed.

“Exactly!” aﬃrmed Albert. “What he experienced was the consequence of the curved trajectory of his

space or rather space-time!”

“Oh my God!” said Mittu. “What is space-time”?

“Oh, it is simply considering space and time together”, said Albert.

“No, I did not understand.” Mittu raised his objection. “I know what space is, but for me, time is an abstract

quantity. How can one consider them together?”

“Correct”, said Albert. “Your deﬁnition of time is based on observed variance in space. You deﬁne time

as the interval between two identically repeated events. It could be the movement of a pendulum or the interval

between two consecutive passes of the wheel about a ﬁxed point. Nevertheless, your experience can be precisely

stated by specifying the space and time coordinates. So when you say that the sun rises at 6 am, you have

speciﬁed the location, which is your spatial coordinates on the planet and the time on your clock. Unless you

specify both, the sentence makes no sense.”

“Okay,” said Mittu, “so you are telling me that though time by itself does not mean anything, it is required

to precisely specify events and observations”

“I did not say that time does not mean anything”, clariﬁed Albert. “I only mean to say that time and space

have independent roles when we consider objective reality.”

“All right”, agreed Mittu. ”What is that you are getting at?” he asked.

“Light was always travelling along his straight-line universe, agreed?” asked Albert.

“No, but his box was on the spinning wheel”, objected Mittu.

“That’s for you. For him, there is no second dimension!” clariﬁed Albert. “So the strange experience he

had was due to the curving of his space-time, something that you and I can perceive better than him”, Albert

clariﬁed.

“Oh, my!”, exclaimed Mittu.

“More interestingly, the experience he has is the same irrespective of his position on the line. Otherwords,

for him this experience is universal.”, said Albert. “While an up and down linear movement of his linear

dimension along the second dimension axis may diﬀerer in polarity depending on the direction of the movement,

the curved trajectory will result in unipolar, universal experience.”

“Oh, my! I need a break!”, said Mittu while he dipped his face into a drop of water on the table and sucked

it in fully.

[Will continue..]

About the Author

Professor Ninan Sajeeth Philip is a Visiting Professor at the Inter-University Centre for Astronomy

and Astrophysics (IUCAA), Pune. He is also an Adjunct Professor of AI in Applied Medical Sciences [BCMCH,

Thiruvalla] and a Senior Advisor for the Pune Knowledge Cluster (PKC). He is the Dean and Director of airis4D

and has a teaching experience of 33+ years in Physics. His area of specialisation is AI and ML.

About airis4D

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

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

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

Vision

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

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

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

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

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

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

abundant environment and ecosystem for the planet and the people.

Mission Statement

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

commitment and dedication towards humanity and the ecosystem.

Dream

To promote the unlimited human potential to dream the impossible.

Design

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

Develop

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

and the planet.

Deploy

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

Campus

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

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

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

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

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

dedicated optical ﬁbre internet connectivity 24×7.

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

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

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

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

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