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Dr. Debi Prasad Mishra
Director, NITTTR Kolkata
director@nitttrkol.ac.in

Editor-in-Chief

Dr. Subrata Mondal
Associate Professor, NITTTR Kolkata
subratamondal@nitttrkol.ac.in

EditorDr. Indrajit Saha
Assistant Professor, NITTTR Kolkata
indrajit@nitttrkol.ac.in

Sub-Editor

Dr. Sailendra Nath Mandal
Professor, NITTTR Kolkata
snmandal@nitttrkol.ac.in

MemberDr. Chandan Chakraborty
Professor, NITTTR Kolkata
chandan@nitttrkol.ac.in

Member

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herein are correct or sufficient to support the conclusion or that the experimental design or
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Journal ofEngineering,
Science &Education

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Editorial
Namaskar. Inaugural issue of the Journal of Engineering Science & Education(JESE) has been published on 11th January 2024, the 60th Foundation Day of theInstitute. The objective of JESE is to provide academic and industry with aplatform for publication of research papers, review papers and communication
letters allowing dissemination of knowledge in basic science, all discipline ofengineering, pedagogy, education technology, patent review and ancientIndian science & technology. In this issue, results of scientific and experimental
in the fields of material science, image processing and ensemble learning are
included. Further, couple of articles in the field of Indian knowledge systems
are presented in this inaugural issue of this journal.
All papers submitted to the JESE gone through per-reviewed process.
Moreover, all aspects of submission, resubmission and notifications have beenhandled electronically. We are grateful to all authors for contribution of theirworks and reviewers for their efficient in valuable assistance in the formationof this issue of the “Journal of Engineering Science & Education”. A special
thanks given to Shri. Utpal Chakraborty, NITTTR Kolkata for compiling articles
in a book form for this issue.
On behalf of all our editorial team and contributors, as the Editor-in-Chief of
JESE wishing a great pleasure to publish this journal for academic interest andwish you all fruitful reading. Constructive suggestions and feedback from ourreaders are solicited for improving the overall quality of this journal.

Prof. Debi Prasad Mishra
Editor-in-Chief
Journal of Engineering, Science & Education

Journal of Engineering, Science & Education, Vol. 1, Issue 1, 2024

Saha ,
Exploring Kidney Indrajit
Nimisha Ghosh ,Cancer using Somnath Rakshit
Transcriptomic Data aDepartment of Computer Science and
a,c,*

b,c

a,c

Engineering, National Institute of TechnicalTeachers’ Training and Research, Kolkata,West Bengal, India
bDepartment of Computer Science andInformation Technology, Institute ofTechnical Education and Research, Siksha‘O’ Anusandhan (Deemed to be University),
Bhubaneswar, Odisha, India
cEqually contributed∗Corresponding author, email:indrajit@nitttrkol.ac.in

Abstract
Kidney renal clear cell carcinoma is a common malignancy of kidney and has the highest
mortality rate in all genitourinary cancers. Regardless of the advancement in medical scienceand cancer research, the incidents and mortality of this disease have risen in the last two
decades. The delayed detection is one of the major causes behind this cancer. The identificationof molecular biomarkers using omics technology can help in better prognosis and treatment ofkidney cancer. Due to the recent advancements of Next Generation Sequencing techniques, theavailability of high-throughput RNA-Seq data gives a new dimension in cancer studies. This
fact motivated us to find not only the potential miRNA as biomarker for kidney cancer but also
to find the association between miRNAs and their target mRNAs. In this regard, ensemble ofdecision trees is used in a systematic way to perform the classification and regression tasks
separately for ranking of miRNAs and finding their association with mRNAs. The classification
task is performed using AdaBoost integrated ensemble of decision trees in order to rank themiRNAs while regression analysis is performed using LSBoost integrated ensemble of decision
trees to find their association with target mRNAs. Here survival of the kidney cancer patientsas alive or dead is used as class label of the expression data of miRNAs to perform theclassification task. The results of top ten miRNAs along with their top ten targets are considered
for biological insight. In this regard, expression analysis of miRNAs and mRNAs using t-test
along with miRNA-Gene-TFand PPI networks are analysed. Moreover, KEGG pathway andGO enrichment analysis have been conducted, which show significant cancer pathways
including Renal Cell Carcinoma for the selected miRNAs and some important pathways involvedin cancer development. The datasets, code and supplementary materials of this work areprovided online1.
Keywords: Ensemble learning; Kidney cancer; miRNA; mRNA; Next generation sequencing

1

http://www.nitttrkol.ac.in/indrajit/projects/mirna-mrna-kidneycancer/

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Journal of Engineering, Science & Education, Vol. 1, Issue 1, 2024

1.

Introduction

Kidney renal clear cell carcinoma (KIRC) is the most common kidney cancer having highmortality all over the world2. In addition to often arising as globular masses that occasionallyprotrude beyond the usual contour of the kidney, KIRC can originate anywhere in the renal
cortex. They can, however, occasionally infiltrate widely or grow inward. Usually consistingof soft bright yellow parenchyma with patches of grayish edematous stroma, haemorrhage,
necrosis, and cysts, the cut surface is varied. The cysts are filled with either haemorrhage or aclear, straw-colored fluid [1]. It has become a widespread malignancy in the last two decades.
The reason behind this is mainly poor prognosis and late detection. KIRC, if not metastasized,
is curable by surgery. This suggests the necessity of looking at this problem from clinical andcomputational perspectives and to identify biomarkers related to this cancertype. Some recent
studies have proposed that miRNAs can act as potential biomarkers for KIRC [2].
MicroRNAs (miRNAs) are small (19-22 nucleotide long) and non-coding RNAs thatnegatively control the gene expression. miRNAs bind with the complementary sequences of
the target mRNAs in 3’- untranslated region (UTR) or even in other regions of those mRNAs
and result in RNA splicing. It is evidenced that miRNAs are involved in various cellularfunctions such as development of cells, cell differentiation and metabolism [3]. It has been
experimentally found that due to both tumor suppression and oncogenesis nature of miRNAs,
they show divergent expression patternsand mutation in various cancer types. The selection ofproper miRNAs is, therefore, important to provide a better prognosis and treatment of KIRC.
In this regard, nowadays high-throughput sequencing is the primary choice to measure
expression levels, i.e., RNA-Seq. In such cases, no prior knowledge of the reference orsequence of interest is needed. It also enables for a wide range of applications [4]. RNA- Seq
technology has been widely used for identification of biomarkers in KIRC. Moreover, forunderstanding the underlying cause of disease it is always suggested to identify the causative
biomarkers. As already described, miRNAsand mRNAs are two such important biomarkers
involved in biological processes as well as diseases. Understanding their relation with respect
to a disease may help in developing efficient diagnostic and prognostic tools. In this regard,
Wotschofsky et al. [5] studied the role of miRNAs and mRNAs in KIRC. In [6], Khatibi et al.
have used feature selection methods based on filter and graph algorithm to identify miRNAs
and mRNAs for renal cell carcinoma subtypes which includes KIRC, KIRP and KICH. In thisregard, the authors have identified 77 mRNAs and 73 miRNAs to differentiate among such
subtypes with 92% and 95% accuracies respectively. Tensor decomposition (TD)-based
unsupervised learning was used in [7] to analyse mRNA and miRNA expression profiles of
KIRC genes. Their experimental results showed that out of all the genes related to cancer
pathways, 23 were highly correlated with the survival of KIRC patients. However, finding theassociation between miRNAs and mRNAs by ranking them using statistical learningtechniques is important to shortlist the miRNAs and their targets (mRNAs) for clinical trials.
To address the above fact, in the current work, Next Generation Sequencing (NGS) [8] provided
miRNA and mRNA expression data with clinical detail, such as survival of patients of KIRC
are used. The patients are classified into alive or dead based on their survival status in order todetermine responsible miRNAs for KIRC. In this regard, AdaBoost integrated ensemble ofdecision trees [9] is used to classify and rank the features (miRNAs) for further analysis. The
target mRNAs are identified for the top ranking miRNAs by performing regression analysis
using LSBoost integrated decision trees [9]. Finally, their roles in various important biological
2 https://www.cancer.org/cancer/kidney-cancer/detection-diagnosis-staging

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functions and networks are demonstrated.

2.
2.1.

Materials and Method
Brief Description of Ensemble Learning

Adaboost is an ensemble classifier, whose output combines the result of multiple classifiers. It
combines multiple weak classifiers to form a strong classifier. The short trees contain one
decision for classification, these are called decision stumps. Adaboost can be thought as
adaptive since the subsequent weak learners are tweaked to match the instances that were
wrongly classified by previous learners. The final tree can be used to obtain the importance ofthefeatures (miRNAs) by getting the values of Gini index. On the other hand, regression
analysis is a statistical tool to estimate the relation between variables. It helps in understanding
how the dependent variable changes with a change in one or more of independent variables.
Least square is a standard method used in regression analysis for approximating the solution of
overdetermined systems. This idea is used in LSBoost integrated ensemble of decision trees
algorithm.A prediction model is produced in a structure of an ensemble of weak prediction
models, mostly decision trees. The model builds in a stepwise pattern and in each step, a newlearner is fitted by ensemble by considering the difference between the actual response andaggregated prediction of all the learners considered earlier by allowing ensemble fits tooptimize mean-squared error. Similarly, like previous case, Gini index values can be used torank the features (miRNAs) using the final decision tree.
2.2.

Data Preparation

The NGS provided high-throughput miRNA and mRNA expression data (in the form of RNASeq) with clinical information are collected from TCGA. The miRNA expression data contains
544 miRNAs and 17,942 mRNAs for 326 patients affected by KIRC whereas the clinicalinformation contain age, status (alive/dead), days to follow-up and gender of the patients. The
expression of the miRNA and mRNA is generated using Illumina high sequencing techniquein the form of reads per million count and later normalized into log2 scale. miRNAs andmRNAs with more than 60% zeroes are discarded from both the expression datasets prior to
the experiment. The statistics of the dataset is provided in Table 1.
Table 1: Statistics of the dataset
Status2.3.

No. of patients

Average Age

No. of days tolast follow-up

Male

Female

Alive

152

72

58.17

1631.47

Dead

72

30

64.61

1053.75

MethodThe flowchart of the work is shown in Figure 1. In this regard, AdaBoost and LSBoost
integrated ensemble of decision trees are applied separately for classification and regression
respectively. First, the AdaBoost integrated ensemble of decision trees is used to rank themiRNAs using Gini index value of the decision trees. For this purpose, the expression valuesof the miRNAs along with their class label as survival of the patients (alive/dead) are used. This

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Journal of Engineering, Science & Education, Vol. 1, Issue 1, 2024

gives a score associated with each miRNA in order to rank them. Thereafter, miRTarBase isused to find the target mRNAs with respect to these miRNAs. As a result, the mRNAs are
obtained for each of the miRNA. Next, in order to find the association between miRNA andmRNAs, LSBoost integrated ensemble of decision trees is used to perform regression analysis.
After the regression analysis, the target mRNAs are ranked based on their corresponding
estimated value in terms of Gini index. Here, higher value indicates stronger association of themiRNA-mRNA pair that are involved in the survival of patient. Note that -log10 of such value
is computed to report in the results section for readability where lower score indicates higher
association. The validation of the pairs is shown in the last level of the flowchart. Finally, thepairs are validated using expression analysis, miRNA-Gene-TF network, PPI network, KEGG
pathway and GO enrichment analysis.

Figure 1: Steps of the proposed method to rank the miRNA-mRNA pairs
3.
3.1.

Results and Discussion
Experimental Testbed

The experiment is carried out by using MATLAB 2018a while Pandas 0.23 and Numpy 1.14
are used in Python 3.6.5 while for computation, an intel i7 processor with 8 GB RAM and 8
cores are used.
3.2.

Results and Validation

AdaBoost integrated ensemble of decision trees is used for the classification in order to rank
the miRNAs. As a result, some miRNAs are identified and ranked based on the value of log10(Gini index) and named as ‘Score’. From such list, top ten miRNAs, viz. hsa-miR-1291,
hsa-miR-92a-3p, hsa-miR-187-3p, hsa-miR-3615, hsa-miR- 148a-5p, hsa-miR-149-5p, hsamiR-939-5p, hsa-miR-590-3p, hsa-miR-937-3p and hsa-miR-130a-3p are considered forfurther study while the rest are provided in the supplementary. Thereafter, the target mRNAs ofthese ten miRNAs are identified using miRTarBase database. The expression value of these4

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Journal of Engineering, Science & Education, Vol. 1, Issue 1, 2024

target mRNAs is used for the regression analysis and depending on the score, the mRNAs areranked. Higher rank of the mRNAs signifies stronger association between miRNA and mRNA.
Such results of miRNA-mRNA association are reported in Table 2 with the score and PubMed
ID. Note that all the ten miRNAs have at least ten targets except hsa-miR-1291, hsa-miR-3615
and hsa-miR-590-3p which have eight, nine and seven targets respectively. Thus, 94 uniquemRNAs are obtained to make pairs and their evidence is also found in literature. In order tovisualize their change in expression based on survival status of the patients, expression analysis
has been performed on the miRNA-mRNA pairs using boxplots as shown in Figure 2.
Moreover, a two sample t-test has been performed on the expression of miRNA-mRNA pairs
to see their statistical significance. As a result, it is found that hsa-miR-92a-3p and BCL2L11,
hsa-miR-149-5p and BBC3, hsa-miR-187-3p and S100A4 and hsa-miR-590-3p and ITPRIPL1
are having significant association with p-values 7.06e-282, 5.69e-189, 1.82e-274 and 1.53e125 respectively and reported in Figure 2 also.

Figure 2: Boxplot of the top miRNA-mRNA pairs in order to show the change in expression
The top ten miRNAs and their 94 targets are used to identify the Transcription Factors (TFs)
using TransmiR and TRRUST databases in order to prepare the miRNA-Gene-TF network. It
is shown in Figure 3 (a), where orange, violet and blue colours represent miRNA, mRNA and
TF respectively. The TFs that are associated with 94 mRNAs for the ten miRNAs are foundusing TransmiR database. Such TFs are used to prepare the PPI network using STRING
database and shown in Figure 3 (b). A bar plot showing the degree of the top ten nodes is also
shown in the same figure. It is seen that well known kidney cancer related TFs like MYC, ESR1,
CREB1, IL6, etc. as found in literature [10,11, 12] are also seen in the PPI network as a nodewith higher degree. Thereafter, all the obtained mRNAs that are associated with top ten
miRNAs are considered to identify their significance in KEGG pathways and Biological
Process (Gene Ontology). In this regard, Enrichr Tool is used. Different cancer pathways arefound which are shown in Figure 4 (a).

CCDC115

E2F

USF

E2F

FOXA2

S100A4
BCL6
LZIC

NR1H3
ZNF12
DYRK2

BLC AP

CENPA

MMGT1

RUNX3

PTE

hsa-miR-187-3p

E2F

MAPK1

USF1
CRMP1

MRO

hsa-miR-130a- 3p
ABCF 2

BT BD 3

ZNF800

CCNB1IP1

DNMT1

CLSTN 1MYC

RPS27A

SMAD 4AKT 2

PLEKHA1

SOX4

SLC 34A2

DNMT

NR1H

ESR1
SP1

KHSR PMX2

hsa-miR-939-5p
IL6

ABC A1

RPL41

DUSP5
ATP5G3

SEPT8

UNC13Ahsa-miR-92a-3p

ABCC 5

QSOX2

NFKB1

H3F3C

SP1

REL
E2F

APC 2

hsa-miR-148a- 5p

SYN GR1RELA

FOXN 2

CALR

IL

CDCA
4

TMF1
CREB1

TLR

MY

EGR 1

MY

NFKB

NACC1
DCAF8
FOXM 1

MON1B

CAMK2N2

MY

TP5

TP53

GATA

E2F 1

GAT A2TNF

CBFA2T 3

hsa-miR-937-3p

FGF2
GDF15

EGR

GIT 1
CPO

CREB

HDAC
GATA

ZBTB2
PPAR
SPRY1

ATF3

FOXQ1

ATF

BBC 3
HIF1A

PEX6
GLU L

hsa-miR-149-5p

WNT4

POLQ

SER PINH 1

BAN K1hsa-miR-3615
CD300E

POU5F
HIF1

SLC 2A1

hsa-miR-590-3p
MYD88

RPS8

NKX2-

TEAD

PT GER 2NONOPDLIM3

MBTD 1HNRNPF
ZNF682PEX13TLX

ITPRIPL1

PFKF B3
GOLGA8A

PLA2G
7

ILF
TLX
YAP

SLC 12A7

GTPB
P2

(a)

(b)

Figure 3: (a) miRNA-Gene-TF network by considering top ten miRNAs with their 94 targets,(b) PPI Network of the TFs that are associated with the mRNAs of the selected miRNAs anddegree of the top ten nodes in the PPI Network are shown in bar

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Journal of Engineering, Science & Education, Vol. 1, Issue 1, 2024

Table 2: Top ten miRNAs obtained through our approach and their target mRNAs
miRNA

mRNA
GTPBP2GOLGA8B
FAM83G
SLC12A7
hsa-miR-1291 PFKFB3
HIST1H4H
GOLGA8A
SLC2A1FGF2PTEN
BCL2L11
H3F3C
hsa-miR-92a-3p DUSP5DNAJB9TMF1
PLEKHA1
FOXN2
SOX4
BCL6
CRMP1
DYRK2
CCDC115hsa-miR-187-3p LZICCCNB1IP1
S100A4
MRO

Score
3.945
4.016
4.068
4.079
4.148
4.155
4.185
4.229
4.105
4.149
4.179
4.288
4.341
4.341
4.377
4.392
4.485
4.542
2.497
3.083
3.152
3.203
3.274
3.277
3.424
3.486

PubMed ID
21572407
23446348
23592263
22291592
23592263
23446348
23592263
23889809
23622248
24026406
21883694
23592263
22473208
22473208
23622248
23592263
22473208
22473208
26845350
27864146
22012620
23446348
23592263
23446348
27544906
21572407

miRNA

mRNA
FOXM1
SERPINH1
ZBTB2
BBC3
hsa-miR-149-5p MTHFRPTGER2
IL6
MYD88
GIT1
NONO
UNC13AQSOX2CLSTN1SYNGR1hsa-miR-939-5p MSN
ABCC5
SLC34A2ICOSLGSEPT8ABCF2PLA2G7
EIF4A2ITPRIPL1
PEX13
has-miR-590-3p MBTD1BANK1HNRNPF
-

Score
2.945
3.515
3.579
3.601
3.610
3.618
3.667
3.696
3.744
3.801
4.292
4.319
4.412
4.459
4.530
4.564
4.565
4.619
4.633
4.708
3.467
3.887
3.946
3.988
3.992
4.025
4.081
-

PubMed ID
23762558
21572407
23144691
23873935
28045918
2591655025916550
24375488
24608434
23622248
23592263
23592263
23592263
23592263
20371350
26701625
28114937
20371350
22012620
24398324
23824327
23446348
23446348
22012620
21572407
20371350
26701625
-

CENPA
FOXA2
POLQ
RPS8
ACSM2ATAB1
hsa-miR-3615 PDLIM3SPRY1ZNF682
CD300E
WNT4
CALR
CDCA4
SH3GLB1
ABCA1
hsa-miR-148a-5pKHSRP
ATP5G3
APC2
MX2
RPL41
AKT2

3.516
3.614
3.439
3.889
3.946
3.979
4.023
4.034
4.052
4.070
4.141
3.602
3.665
3.869
3.891
3.922
3.926
3.962
4.034
4.089
4.187

23446348
28098868
23824327
23622248
24906430
23824327
23824327
23622248
23824327
24906430
23824327
23446348
24398324
22100165
26437365
23446348
23592263
23592263
23824327
20371350
27878305

DCAF8
CAMK2N2
GDF15
GLUL
MON1B
CBFA2T3
CPOX
FOXQ1
NACC1
PEX6
RUNX3
RPS27A
MMGT1
ZNF12
has-miR-130a-3pBLCAP
PPARG
BTBD3
SMAD4
TNF
ZNF800

4.063
4.099
4.238
4.240
4.299
4.317
4.369
4.490
4.578
4.817
3.693
3.771
3.809
3.869
3.956
3.983
3.993
4.037
4.104
4.140

23622248
19536157
26701625
23622248
27418678
23313552
23622248
23592263
26701625
23592263
22846564
23592263
23592263
24398324
23592263
21135128
23592263
23418453
24885472
23592263

Figure 4: Bar plot of the significant (a) KEGG pathways and (b) GO Enrichment Biological
Process terms based on -log10(p-value)

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This analysis signifies that these mRNAs actively play a significant role in KIRC by affecting
pathways like Pathways in cancer, FoxO signaling pathway, Toll-like receptor signaling
pathway, HIF-1 signaling pathway and TNF signaling pathway. Moreover, the Gene Ontology
terms for Biological Process are also obtained. This is shown in Figure 4 (b). Some important
terms are reported like Cytokine-mediated signaling pathway, Positive regulation ofprogrammed celldeath, Activation of MAPK activity, Negative regulation of MAPK cascade,Regulation of chemokine biosynthetic process, Regulation of interleukin-6 production, Positive
regulation of MAP kinase activity, Regulation of cytokine production, Wnt signaling pathway
and Interleukin-6-mediated signaling pathway. Therefore, the findings suggest that selected
miRNA-mRNAs pairs are statistically and biologically relevant in etiology of kidney cancer.
4.

Conclusion

In the current work, miRNAs with significant role in KIRC have been identified and they havebeen ranked based on patient status using AdaBoost. Also, the target mRNAs of these miRNAs
have been identified and ranked through LSBoost by performing regression analysis. The
obtained pairs show ample evidence in the existing literature regarding their dominant role in
KIRC. Moreover, during the biological analysis, these mRNAs are found to be present in KIRC
pathways which shows the importance of the selected miRNA-mRNA pairs in KIRC. As ascope of further research, similar study can be conducted to rank and select important miRNAmRNA pairs in other cancer types.
References
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J. N. Eble, L. Cheng, Chapter 30 - renal neoplasia, in: Modern Surgical Pathology (Second
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Meyer, Integrated microrna and mrna signature associated with the transition from thelocally confined to the metastasized clear cell renal cell carcinoma exemplified by mir-1465p, PLOS One 11 (2) (2016) e0148746.
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Artificial Intelligence approaches, Scientific Reports, 12 (2022), 16393.
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National Academy of Sciences 112 (21) (2015) 6539–6544.
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renal cell carcinoma using gene expression profiling analysis, Oncology letters 12 (1)(2016) 73–78.
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Optimization of density
and hardness of Jhulan Kumar and Subrata Mondal*of Mechanical Engineeringgraphite-copper Department
National Institute of Technical Teachers’
Teacher Training and Research, Kolkata,,
composites by using West Bengal, India
Corresponding author, email:
Taguchi method subratamondal@nitttrkol.ac.in*

Abstract
Copper is extensively used for various industrial applications due its high electrical andthermal conductivity, however, poor mechanical properties hindered its applications. As areinforcement material, graphite reduces weight of composite at the same time enhances themechanical property of the composites for advanced material applications. This paper presents
optimization of hardness and density of graphite-copper composites by using Taguchi’s
optimization methodology. Analysis of variance is used to study the influence of compaction
pressure, reinforcement concentration and sintering temperature on hardness and sintereddensity of composite materials. Measured data were analysed with the help of MINITAB15
software. Signal –to – noise ratio was used to analysed effect of reinforcement concentration,compaction pressure and sintering temperature on sintered density and hardness properties.
Results indicates that reinforcement concentration in the composite is the most notableparameter on sintered density, while, sintering temperature has the most significant influenceon hardness properties of composite.
Keywords: Composite materials; hardness; optimization; powder metallurgy; sintered density.
1.

Introduction

Composites materials are artificially produced multiphase materials. These materials areproduced in order to utilize the advantages of various properties of bulk material such as metal(or metal alloy) or ceramics (or ceramic alloy) or polymers (or polymer blend) and minor
portion of organic materials (e.g. Kevlar, cellulosic fibers etc.) or inorganic materials (e.g.
silicon carbide, various carbon material, boron etc.) in a tailor made material. The composites
materials are manufactured from at least two materials in such a manner that individualcomponents retain their identity while bonded together in the resultant material. The constituent
materials are not soluble in each other and no other chemical reaction takes place. The major
portion material is called as matrix domain while minor portion is the reinforcement. The
strength of the composite is generally in between matrix and reinforced material in case ofproper distribution and dispersion of reinforcement material in the matrix phase with goodbonding between the various phases.
Matrix phase bonds reinforcements and helps to transfers load from one phase to another phaseand vice versa. In general matrix is soft in nature as compared with the reinforcement phase.
On the other hand, reinforcing phase improves mechanical and other physical properties ofresultant composite materials. Generally, reinforcement is harder, stronger and stiffer thanmatrix [1, 2]. The geometry of the reinforcement particles is one of the major parameters for

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determining the performance of the composite materials. Properties of composites materials
depends on the shape, size, and dimensions of the reinforcement particles. The reinforcing
phases could be fibers, particles, flakes types etc. and size could be macro, micro, nano etc.
Composite materials prepared with particle like reinforcements has almost equal dimension in
all directions. The shape of the reinforcing particles may be spherical, cubical, cylindrical, plate
like and may have regular or irregular geometry.
Metal matrox composite, polymer matrix composite and ceramic matrix composites are various
classifications of composite materials based on the matrix phase. In MMC, Al, Cu, Mg, Ti etc.
are more commonly used as matrix phase. These composites can operate at high temperature ascompared to the PMC. Metal matrix composites have several conveniences over the pure metals
such as better specific strength, lighter, higher modulus, improved properties at elevatedtemperatures, improved wear resistance properties and lower coefficients of thermal expansion.
However, their toughness is inferior to monolithic metals. Copper is widely used for manyindustrial applications such as heating element, motor vehicle radiator, air conditioner,
electrical generator, electronic goods etc. Some of the drawback of copper are high cost, softand ductile etc. and these drawbacks could be improved by reinforcing appropriate reinforcing
phase in the matrix domain.
Yang et al. presents copper-carbon composite fabricated by using molding and sintering
method. Bonding between carbon particles and matrix domain has been improved by theincorporation of pitch coke. Excellent interfacial bonding between reinforcements and matrix
phases enhances the strength of the copper composite material. The micro-hardness ofcomposites has been improved with increase in the content of pitch coke [3]. Power presents
copper-carbon composite prepared by using powder metallurgy in different weight fraction ofreinforcement material. The hardness of copper-graphite composite decreases with the weightpercentage of graphite particulates [4]. Graphite reinforced copper matrix composites
preparation by employing casting process has been reported in Ref [5]. The hardness of the
prepared composite samples increases with the concentration of graphite. Microstructure study
exhibits presence of copper and along with some copper oxide formation. Detailed
microstructure analysis revealed excellent interfacial interaction between copper matrix andgraphite reinforcement. Density of the composite materials has been increased with increase in
the content of graphite by weight due to the formation of more compact structure.
Hardness property of copper (Cu) matrix composite material is an important parameter which
may influence other properties viz. wear, compressive strength etc. of the resultant composite
materials. Therefore, there is need for the optimization of process parameters for to obtain
possible highest hardness and lowest density response. The Taguchi method design with three
process parameters such as graphite (Gr) concentration, compaction pressure and sintering
temperature are studied in the current paper to optimize Gr-Cu composite’s sintered density andhardness behaviour. In order to maximize sintered density and hardness property, theexperimental results are analysed to reveal the best possible process parameters combinations.
2.

Experimental

2.1

Materials

Copper based metal matrix composites have unique properties and it is used widely in the
manufacturing industries. Copper exhibits good ductility, electrical conductivity and thermal
conductivity properties. While, graphite possesses excellent physical and mechanical10

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properties. Therefore, copper has been selected as the matrix domain and graphite as areinforcement domain to fabricate the composite materials. Properties of raw materials aredescribed elsewhere [6].
2.2

Fabrication of Composites

Graphite reinforced composite materials are fabricated by using powder metallurgy route.
Whole process has been depicted schematically in Figure 1. The detail fabrication process has
been described elsewhere [6]. Copper matrix was reinforced with three different concentrationof graphite, such as 5, 10 and 15 wt/wt%. Samples were compacted by universal testing machine(UTM) at three compaction pressure viz. 509.29, 572.55 and 636.61 MPa. Compacted samples
were sintered at three different temperature, such as 900, 950 and 1000 °C.

Figure 1: Schematic representation of the copper graphite composite fabrication process
2.3

Measurement of Density and Hardness

Sintered density of composite samples were measured by gravimetric method. The parameters
required to measure the density are diameter (d) and length (l) of specimens that have beenmeasured three times in each samples by digital Vernier calliper, and mass of each samples has
been measured by digital weighing machine. Hardness is the property of a material to resists
permanent indentation. Hardness measurement for each sample has been done in a Rockwell
hardness tester. Detailed process for the measurement of sintered density and hardness aredescribed elsewehre [6].
2.4

Optimization using Taguchi method

This method is used for the experimental design and analysis of experiments. The method runs
are organized as per design of experiments based on the orthogonal arrays. In orthogonal arrays,
the number of experiments runs is reduced and every process parameter is analyzed by keeping
other parameter as constant. Influence of other parameters and significant parameters are
decided on the basis of statistical analysis of the experimental response. Signal to Noise ratio(S/N) can be used to analyze the experimental data, which could be helpful for the designers.
S/N ratio is a log function and this is used to calculate the deviation between the desired value
and experimental value. This function can give the optimum value. The optimal setting is theprocess parameter combination, which gives the highest S/N ratio. The selection of appropriate
orthogonal array depends on the degree of freedom of the variables. The three level variables
have two degrees of freedom. The combination of the design of experiment with the11

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optimization of control parameters to get the best result is achieved in the Taguchi methods.
Table 1 shows the L-9 orthogonal array way of solving the problem in Taguchi method.
The optimization of experimental design is performed using Taguchi method and used to create
a set of newly designed experiments by MINITAB software17. In this design Taguchi L9 (3*3)
Orthogonal Array is designed to calculate S/N ratio, where the factor is 3 and number ofexperiment runs are 9.
Table 1: L9 orthogonal array with design factors
Sample No.
Concentration ofSintering Temperature
Compaction Pressure
graphite (wt/wt%)
(°C)
(MPa)
1.
5
900
509.292.
5
950572.553.
5
1000
636.614.
10900
572.555.
10
950636.616.
10
1000
509.297.
15900
636.618.
15950509.299.
15
1000
572.55
As per the Taguchi’s method, the S/N ratio is calculated as the lower the better (LB) and thehigher the better (HB). Relevant equation is as follows:[
L : = -10 log[ ( ) ∑:= -10 log ( ) ∑]]In this optimization process, the main aim is to maximize hardness and density. Therefore, thelarger is the better type signal to noise ratio is applied to get the best result for sintering process.
:[= -10 log ( ) ∑]where, = S/N ratio
n= number of repetition of experimenty= observed response value

represents the achieved S/N ratio calculated from observation table. denotes the total no ofexperiments, y denotes the value of response for ith number of the experiment.
From the experimental finding all the observation such as sintered density and hardness can beobtained, afterwards, S/N ratios are calculated. Different graphs for analysis is drawn by using
MINITAB software (version-17). Process parameter combinations can be predicted by using
S/N ratio and ANOVA.
3.

Results and Discussion

3.1

Main effect plots for sintered density

Sintered densities of composites with different concentration of graphite is tabulated in Table
2. Sintered density of different graphite containing specimens compacted at 636.61 MPapressure, and sintered at three different temperature are reported in our earlier publication [6].
Experimental results revealed that density of the composite could be lowered by increasing the12

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graphite contents due to the lower density of graphite in comparison with the copper. Moreover,
if the quantity of graphite was reinforced in composite too much, the graphite cluster could beformed, and organization of graphite agglomeration is also one of the factors leading to lower
the density of resultant composite materials. The measured densities are always much lower
than the theoretical densities. The porosity of composites increases by increasing graphite
content and this may affect other properties of composites such as hardness, and othermechanical properties of materials.
The effect plots in Figure 2 indicate that the sintered density is significantly influenced bycomposition, sintering temperature and compaction pressure. This graph indicates that as thecompaction pressure and sintering temperature increases, the sintered density also increases.
However, the effect of composition is most significant compared to sintering temperature andcompaction pressure on the sintered density of the samples. The changes in sintered density ismore when weight percentage of graphite is increases. Therefore, the composition ofcomposites strongly affects the sintered density. Vairamuthu et al. reported that the sintering
temperature and compaction pressure strongly influence density of the aluminium matrix
composites reinforced with ceramic nanoparticles [7]. Figure 3 presents the residual plot for
S/N ration of sintered density, the relevant data are tabulated in Table 2. Table 3 shows theanalysis of variation data.

Figure 2: Main effects plots for S/N ratio of sintered density.
Table 2: Experimental result and corresponding S/N ratio for sintered density.
Composition
Compaction
Sintering
Sintered
S/N Ratio(wt.% ofPressure
TemperatureDensitygraphite)
(MPa)
(°C)
(g/cc)
5509.29900
6.478
16.22885
572.55950
6.576
16.35925
636.61
1000
7.184
17.127310572.55900
6.324
16.019810
636.61950
6.415
16.143910509.291000
6.412
16.139915
636.61900
6.910
16.789615
509.29950
5.462
14.747015
572.551000
5.521
14.8404

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Figure 3: Residual plots for S/N ratio of sintered density.
Table 3: Analysis of variance for means (density)
Analysis of Variance for SN ratios
SourceDF Seq SS
Adj SS
Composition of graphite 2
1.87231.8723Sintering Temperature2
0.5331
0.5331Compaction Pressure
2
1.86201.8620Residual Error
20.74630.7463Total
85.0136
S = 0.6108 R-Sq = 85.1% R-Sq(adj) = 40.5%
3.2

Adj MS
0.93610.2666
0.93100.3731

F
2.51
0.71
2.50

P
0.285
0.583
0.286

Main effect plots for average hardness

Hardness of composites with different concentration of graphite is tabulated in Table 4. Sintereddensity of different graphite containing specimens compacted at 636.61 MPa pressure, andsintered at three different temperatures are reported in our earlier publication [6]. Denser
samples will have higher hardness values. Composite materials sintered at 900 and 1000 °C,
the hardness of copper-graphite composites has been improved with increasing graphite
concentration up to certain graphite concentration. Increase of graphite weight concentration
up to 10%, hardness of composite materials increases, afterwards, decreases with reinforcement
concertation. Maximum hardness value reported about 35 percent increase over that of the purecopper. The hardness of composite with higher graphite concentration decreases due to theclustering of graphite and formation of porosity in the composite materials. The hardness ofcomposite materials depends on concentration of graphite together with porosity of thematerials. Composite with higher density has improved hardness property as compared withless denser material. Optimization of processing parameters is very important to tailor theproperties of composite materials [8].

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The effect plots in Figure 4 indicates that the hardness is significantly influenced by sintering
temperature and compaction pressure. Further, the hardness is more affected by composition.
These graphs indicates that as the sintering temperature and compaction pressure increase,hardness is also increases. It is clearly observed that the composition of graphite strongly affects
average hardness, and the maximum hardness is found at 10 % wt. graphite. Earlier research
reported that the compaction pressure and sintering temperature strongly influence hardness ofthe ceramic particles reinforced aluminium matrix composites [7]. In another paper in earlier
research, it has been reported that higher compaction pressure, sintering temperature andreinforcement weight percentage were resolved as suitable parameters to obtain maximum
hardness [9]. Research by Srinivas et al. revealed that Taguchi optimization of mechanicalcharacteristics of composite materials fabricated by powder metallurgy are highly relied upon
the sintering temperature and slightly on sintering time and compacting pressure [10].
Figure 5 presents residual S/N ratio of hardness, the relevant data are presented in Table 4 andthe analysis of variation of hardness data are tabulated in Table 5.
Table 4: Experimental result and corresponding S/N Ratio for average hardness.
Composition (wt.%
SinteringCompaction
Average
S/Nof graphite)
Temperature
Pressure (MPa)
Hardness
Ratio(°C)(Rockwell)5509.29900
6436.12365
572.55950
6836.65025
636.61
1000
88
38.889710
572.55900
75
37.501210636.61950
7437.384610
509.291000
88
38.889715
636.61900
5334.485515
509.299506135.706615
572.551000
7637.6163

Figure 4: Main effects plots for S/N ratio of average hardness (Rockwell)

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Figure 5: Residual plots for S/N ratio of hardness (Rockwell).
Table 5: Analysis of variance for means (hardness)
Analysis of Variance for Means
SourceDF
Composition of graphite 2
Sintering Temperature
2
Compaction Pressure2
Residual Error
2
Total
8

Seq SS
377.56680.226.22
38.89
1102.89

Adj SS
377.556680.222
6.222
38.889Adj MS
188.778
340.111
3.111
19.444

F
9.71
17.49
0.16

P
0.093
0.054
0.862

S = 4.410 R-Sq = 96.5% R-Sq(adj) = 85.9%

4.

Conclusions

Sintered density and hardness properties of graphite reinforced copper matrix composites has
been investigated using a Taguchi orthogonal array design with different reinforcemnet
concentration, compacted pressure and sintering temperature. The effect of composition is mostsignificant compared to compaction pressure and sintering temperature on the sintered densityof the specimens. Further, the hardness is more affected by the compaction pressure andsintering temperature. Experimental analysis revealed that concentration of graphite strongly
affects average hardness, and the maximum hardness is found at 10 % wt. graphite.
References[1] Goudah, G., et al. “Microstructural studies of sintered carbon nanotubes reinforced copper
matrix composite”, Journal of Engineering Science and Technology, 2010, 5 (3), pp 272 –
283.

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[2] Zhen, J. M., et al. “Influence of graphite content on the dry sliding behavior of nickel alloy
matrix solid lubricant composites”, Tribology International, 2017, 114 (October), pp 322328.
[3] Yang, H. J. et al. “Effect of the ratio of graphite/pitch coke on the mechanical andtribological properties of copper–carbon composites”, Wear, 2010, 268 (11-12), pp 1337–
1341.
[4] Powar, R. U. “Powder metallurgical processing and characterization of graphite particulate
reinforced copper matrix composite”, International Journal of Research Science &Management, 2015, 2 (6), pp 54-57.
[5] Gupta, S & Singh, G. “Fabrication of copper graphite composite material & its mechanicalproperties”, International Journal of Advance Research and Innovation Ideas in Education,
2017, 3 (3), pp 347-362.
[6] Kumar, J & Mondal, S. “Microstructure and properties of graphite-reinforced copper
matrix composites”, Journal of the Brazilian Society of Mechanical Sciences andEngineering, 2018, 40, Article number: 196.
[7] Vairamuthu, J. et al. “Optimization of powder metallurgy parameters of TiC- and B4Creinforced aluminium composites by Taguchi method”, Transactions of the CanadianSociety for Mechanical Engineering, 2021, 45 (2), pp 249-261.
[8] Kucuk et al. “Optimization by Using Taguchi Method of the Production of MagnesiumMatrix Carbide Reinforced Composites by Powder Metallurgy Method”, Metals, 2017, 7(9), Art No. 352.
[9] Meignanamoorthy et al. “Evaluation on Powder Metallurgy Process Parameters of BallMilled AA8079-B4C Nanostructured Composites via Taguchi Grey Relational Analysis”,
Journal of Nanomaterials, 2022, Art No. 7484053.
[10] Srinivas et al. “Microstructural, mechanical and tribological characterization on the Al
based functionally graded material fabricated powder metallurgy”, Materials Research
Express, 2020, 7 (2), Art No. 026513.

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Journal of Engineering, Science & Education, Vol. 1, Issue 1, 2024
Asifuzzaman Laskera,
Mridul Ghoshb,
Sk Md Obaidullaha,
Teresa Goncalvesc,
Kaushik Royd andChandan Chakrabortye*

Enhanced Deep
Learning U-Net Model
for Chest X-ray Image
Dept. of Computer Science andEngineering,
Segmentation 700160, IndiaAliah University, KolkataabDept. of Computer Science, Shyampur
Siddheswari Mahavidyalaya, Howrah711312, Indiac
Computer Science Department and
ALGORITMI Center, University of́Evora, PortugaldDept. of Computer Science, West Bengal
State University, Barasat, Pincode700126, Indiae
Dept.of Computer Science and Engg.,
National Institute of Technical Teachers’
Training and Research, Kolkata-700106,
India*Corresponding author, email:chandan@nitttrkol.ac.in

Abstract
Modern healthcare uses chest X-ray images as an affordable medical imaging diagnostic
resource. Lung segmentation is a fundamental feature of chest X-rays (CXRs) for screening andidentifying lung conditions. It is difficult to segment the lungs on several patients' CXRs because
they are opacified. The purpose of this paper is to resolve this issue by using a division formulabased on U-Net. Our recommended design was developed by combining MobileNetV2 andInceptionResNetV2 pre-trained models. The segmentation performance of this architecture canbe improved by using a stacked framework based on pre-trained models. In the analysis
involving three widely-recognized datasets for lung segmentation, our method improved theaccuracy by 2.52% in respect to dice coefficient and 2.37 % IoU over the traditional U-Net
model.
Keywords: Supervised localization, U-Net, Semantic Segmentation, Chest X-rays
1.

Introduction

Radiographic diagnostics are performed all over the world on a daily basis, using a variety ofimaging modalities. Among all imaging modalities, CXRs imaging is the most prevalent andeconomically viable method for diagnosing pulmonary conditions [1]. Lung segmentation is
one of the most significant aspects of clinical support applications that evaluate respiratory
disorders. By separating lung areas from adjacent tissue, lung segmentation indicates lung edges
and areas. According to the published scientific article, deep Convolutional Neural Network(CNN) methods are used for both categorization and segmentation of diseases. A U-shaped
framework has been designed with balanced encoding and decoding modules for capturing
enhanced image information. U-Net-based frameworks were employed in this study to segment
X-ray images based on upsampling and downsampling mechanisms. These architectures are

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Journal of Engineering, Science & Education, Vol. 1, Issue 1, 2024very popular for medical image segmentation. According to the published scientific article [2],deep CNN methods are used for both categorization and segmentation of diseases. In the CNN
approach, features are used for classification and the evaluation score reaches a considerable
height. Considering the large amount of data required for the training process, pre-trainedarchitectures were used to optimize CPU power and processing time. This allowed for increased
efficiency in data processing while still achieving satisfactory results.
There are several factors that constrain image segmentation, including brightness, shade, noise,and object variations, making it a tedious and time-consuming part of biomedical technology.
In segmentation, pixels are divided into distinct areas based on their features in the surrounding
regions. In order to address this issue, researchers developed an automatic segmentation
framework to identify infected lung regions [4-6]. The use of intelligent methods helps improvethe detection and severity of many diseases at an early stage. It is quite difficult to employ
several standards label and tag datasets in order to obtain the required algorithmic output forcomputer-based medical image segmentation. The process of pixel-based segmentation in CXR
images, specifically for lung regions, frequently results in noise, making it challenging to obtainaccurate annotations. The annotation techniques of individual annotators may lead to
substantially different results between them, and significant interobserver disagreement may
also exist. Several studies have applied a human-in-the-loop technique to minimize theannotation burden [7]. The annotations for algorithmically generated labels were manuallychecked and corrected by annotators in order to improve deep learning-based training and build
a more reliable segmentation system. The effectiveness of deep learning over conventional
methods has been documented in the literature [8]-[11]. To accomplish this, our study utilized
standard U-Net architecture for segmenting the lung region from CXR images.
The contribution of the proposed work is listed below:
1. The proposed framework introduces an innovative stacked architecture that seamlessly
integrates MobileNetV2 and InceptionResNetV2 with U-Net, significantly enhancing
the accuracy of lung segmentation in chest X-rays.
2. A novel meta-learner component is developed, optimizing the fusion of features frompre-trained networks, resulting in a robust model that outperforms traditional U-Net
frameworks.
3. The study contributes a standardized pre-processing protocol for chest X-rays, ensuringconsistency across datasets and improving the model's training efficiency andgeneralizability.
2.

Earlier Works

Commonly, imaging techniques such as X-ray, CT scans, and MRIs are utilized for diagnosing
respiratory diseases. The method of segmentation usually incorporates criteria based on thetreatment approach or the illness’s progression stage, which is predominantly medical in nature.
Regardless of the type of disease, patients receive additional supportive care alongside theprimary treatment. Diagnoses are often enhanced by segmenting multiple organs and providing
essential data to healthcare providers. A range of CNN models, including Fully-CNN, U-Net,
and Mask R-CNN, as well as their various integrations, have been applied to the task ofsegmentation.
Lasker et al [12] suggested a computationally effective stacked ensemble architecture using a
CNN network and three previously trained neural networks. The performance scores of three

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datasets are 97.28%, 96.50%, and 97.41%, respectively, and they show effectiveness of thearchitecture performed in those datasets, Ghose et al. [13] proposed an ensemble architecture
for text/non-text image separation and represents the increase in performance of prediction
score. Lasker et al. [3, 14] prepared a stacked ensemble architecture for CXR image
segmentation depending on U-Net models. In U-Net models, it uses three DL architectures as
an encoder module: MobileNetV2, InceptionResNetV2, and EfficientNetB0. In a study, thesuggested framework was compared to the traditional U-Net model using the three available
lung segmentation datasets.
Chatterjee et al. [15] developed a classification method based on unequally distributed classes
to identify COVID-19 disorder using a different autoencoder (VAE). They carried out in-depth
experimental analysis, and the results showed considerable advancements in COVID-19
detection. Lasker et al. [16] designed an efficient deep learning framework to extract deep
features from photos. These features were then used to categories the images using various
methods of machine learning. The highest accuracy rate achieved by the MLP-basedclassification approach is 96.81%. Gayathri et al [17] provided a technique that investigatedthorough computer-aided diagnosis utilizing the CNN, autoencoder-based approach to identify
disorders.
3.

Architecture design

Our suggested system is built on a combination of MobileNetV2 and InceptionResNetV2,
integrated with a U-Net structure. This U-Net design, which is tailored for splitting medical
images into distinct sections, uses a layered CNN approach. It works in three main stages:
initially reducing the image size, then refining the features at a reduced scale, and finallyenlarging the image to its original size with improved segmentation.
The fusion of MobileNetV2 and InceptionResNetV2 in our model is a strategic choice aimed
at harnessing MobileNetV2's lightweight structure and efficient depthwise separable
convolutions for high-quality, channel-specific feature extraction from lung images.
Concurrently, InceptionResNetV2's sophisticated design merges inception modules withresidual connections, enhancing the model's capability to discern the complex variability inmedical imagery while preventing the vanishing gradient issue. This synergy allows our
integrated model to deliver a comprehensive segmentation of chest X-ray images, capturing an
extensive array of features that range from the minutely detailed to the highly abstract, thereby
bolstering the precision and dependability of lung segmentation, all the while optimizing
computational resource use.
Within our model’s structure, the U-Net’s encoding component is integrated with two distinct
pre-trained neural networks, forming a tandem system that benefits from a meta-learning
process for prediction tasks. This meta-learning module comprises three dense layers with 32,
64, and 128 units respectively, and includes a dropout rate of 0.4 to avoid overfitting. Following
the last dense layer, we introduced a batch normalization layer to stabilize learning. Finalizing
this meta-learner's design required several iterations. We constructed a comprehensive system
by individually creating U-Nets with MobileNetV2 and InceptionResNetV2 bases, and thenamalgamating these models. To simplify the model, we adjusted MobileNetV2’s alphaparameter to 0.45. Moreover, we established a skip connection from the activation layer toenhance feature transmission. Different filter sizes 16, 32, 64, and 128 were used in the decoder
part to maintain consistency with the encoder's skip connections. The complete structure of our
proposed model is depicted in Figure 1.

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To evaluate the model performance we used two readily accessible datasets [19] namely
Shenzhen Hospital (SH), and Montgomery County(MC) in this research. In the datasets of MC,
and SH, there are 138, and 662 original X-ray images and matching masks, respectively. There
are two different resolutions of images in these databases: 4892 x 4020 and 3000 x 3000. To
ensure uniformity of the input, all training images have been reduced to 256 X 256 resolutions
to boost training performance.

Figure 1: Proposed U-Net-based stack framework for lungs X-ray image segmentation
4.

Evaluation protocols and results

4.1

Evaluation metrices for segmentation

To assess the accuracy of the segmentation, we employ the Dice Coefficient (DC) and theIntersection over Union (IoU) as key statistical measures. The computation of both DC and IoU
involves considering the total area of the image (A) alongside the area identified by thesegmentation process (S). Within the segmented area, we distinguish between the actual
segment (St), which is the ground truth, and the segment predicted by our model (Sp).
2.

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2.

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4.2

Training Regime

To enhance the performance of our model, we fine-tuned it by adjusting several
hyperparameters. These included the batch size, the learning rate, and the number of training
cycles, set at 16, 0.0001, and 50, respectively. When we compare this to the U-Net model, ourmodel has 8.14%, and 0.10% fewer parameters than the U-Net combined with MobileNetV2and U-Net combined with InceptionResNetV2 configurations. As for the processing speed, ourmodel achieved inference times of 183 milliseconds for U-Net with MobileNetV2, 141
milliseconds for U-Net with InceptionResNetV2, and 149 milliseconds for the dual
configuration. The impact of these hyperparameter adjustments is illustrated by four distinct
learning curves, which are presented in Figure 2.

Figure 2: The training curve for the U-Net with a two-stack model.
4.3

Results

To assess the effectiveness of our segmentation process, we used five different measures for acomprehensive comparison across multiple model architectures. Details of these comparisonsare provided in Table 1. The initial results are from the standalone U-Net model, followed byoutputs when combined with MobileNetV2 and InceptionResNetV2 in a stacked arrangement.
On datasets MC and SH, the combined framework improved the DC and IoU scores by over
2% compared to the standalone U-Net. Additionally, when examining three separate
evaluations, it was observed that the U-Net's DC, IoU, and Recall metrics showed enhancement
when it was integrated with the MobileNetV2 and InceptionResNetV2 models that had beenpreviously trained. These improvements are also detailed in the corresponding sections of ourreport.
Table 1. Comparative outcomes using U-Net combined with different state-of-the-art
architectures.
Loss
Dice Coeff IoU
RecallPrecision
U-Net
0.02590.95830.93760.86940.9876
U-Net | MobV2
0.0246
0.96730.95190.89480.9985
U-Net | IncepV2
0.0202
0.97120.96020.9101
0.9980U-Net | MobV2 | IncepV2
0.01020.98320.9616
0.9356
0.9986
Note: MobileNetV2(MobV2), InceptionResNetV2(IncepV2)

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After analysing the suggested model, the CXR image, original data, binary mask, andsegmented lungs are shown in Figure 3. Third column (Binary Mask) indicates that the
proposed systems work well with validation data.

Figure 3: The initial chest X-ray (CXR) images alongside the corresponding Ground truth,
Binary Mask and segmented lungs.
5.

Discussion and Conclusion

This study successfully demonstrated the potential of a stacked architecture combining U-Net
with MobileNetV2 and InceptionResNetV2 for lung segmentation in chest X-rays. However, it
is crucial to acknowledge that the methodology carries inherent limitations. For instance, themodel's reliance on a diverse yet finite set of public datasets could influence its generalizability
across varied clinical scenarios and demographic profiles. Moreover, while the stacked
framework has shown promising results, its computational demands may pose challenges inresource-constrained environments.
Our research has yielded a novel lung segmentation framework that leverages the strengths of
U-Net in conjunction with the advanced feature extraction capabilities of MobileNetV2 andInceptionResNetV2. This stacked approach has demonstrated enhanced performance overindividual models, as evidenced by the remarkable results obtained using multiple public
datasets. The integration of these pre-trained networks into a unified architecture not only
boosts segmentation accuracy but also paves the way for cost-effective and scalable solutions
in medical imaging diagnostics. Despite these challenges, the findings from our research
provide a strong foundation for future studies, encouraging the exploration of othersegmentation models like DeepLab, SegNet, and Mask R-CNN. Our work not only contributes
to the advancement of medical imaging technology but also opens new avenues for researchthat could lead to more sophisticated and nuanced approaches in biomedical imaging.
References
1. Mukherjee, Himadri, Priyanka Sreerama, Ankita Dhar, Sk Md Obaidullah, Kaushik Roy,
Mufti Mahmud, and K. C. Santosh. “Automatic lung health screening using respiratory
sounds.” Journal of Medical Systems 45 (2021): 1-9.

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2. Lasker, Asifuzzaman, Sk Md Obaidullah, Chandan Chakraborty, and Kaushik Roy.
“Application of machine learning and deep learning techniques for Covid-19 screening
using radiological imaging: a comprehensive review.” SN computer science 4, no. 1(2022): 65.
3. Lasker, Asifuzzaman, Mridul Ghosh, Sk Md Obaidullah, Chandan Chakraborty, andKaushik Roy. “Deep features for covid-19 detection: performance evaluation on multiple
classifiers.” In International Conference on Computational Intelligence in Pattern
Recognition, pp. 313-325. Singapore: Springer Nature Singapore, 2022.
4. Mukherjee, Himadri, Bubai Das, Sahana Das, Ankita Dhar, Sk Md Obaidullah, K. C.
Santosh, Santanu Phadikar, and Kaushik Roy. “Deep Neural Network for Pneumonia
Detection Using Chest X-Rays.” In Computer Vision and Image Processing: 5thInternational Conference, CVIP 2020, Prayagraj, India, December 4-6, 2020, Revised
Selected Papers, Part I 5, pp. 78-87. Springer Singapore, 2021.
5. Saad, Mohd Nizam, Zurina Muda, Noraidah Sahari Ashaari, and Hamzaini Abdul Hamid.
“Image segmentation for lung region in chest X-ray images using edge detection andmorphology.” In 2014 IEEE international conference on control system, computing andengineering (ICCSCE 2014), pp. 46-51. IEEE, 2014.
6. Abedalla, Ayat, Malak Abdullah, Mahmoud Al-Ayyoub, and Elhadj Benkhelifa. “Chest Xray pneumothorax segmentation using U-Net with EfficientNet and ResNet architectures.”
PeerJ Computer Science 7 (2021): e607.
7. Shan, Fei, Yaozong Gao, Jun Wang, Weiya Shi, Nannan Shi, Miaofei Han, Zhong Xue,
Dinggang Shen, and Yuxin Shi. “Lung infection quantification of COVID-19 in CT images
with deep learning.” arXiv preprint arXiv:2003.04655 (2020).
8. Ghosh, Mridul, Sayan Saha Roy, Himadri Mukherjee, Sk Md Obaidullah, K. C. Santosh,
and Kaushik Roy. “Understanding movie poster: transfer-deep learning approach forgraphic-rich text recognition.” The Visual Computer (2022): 1-20.
9. Ghosh, Mridul, Gourab Baidya, Himadri Mukherjee, Sk Md Obaidullah, and Kaushik Roy.
“A deep learning-based approach to single/mixed script-type identification.” Advanced
Computing and Systems for Security: Volume 13 (2022): 121-132.
10. Ghosh, Mridul, Somnath Chatterjee, Himadri Mukherjee, Shibaprasad Sen, and Sk Md
Obaidullah. “Text/non-text scene image classification using deep ensemble network.” In
Proceedings of International Conference on Advanced Computing Applications: ICACA
2021, pp. 561-570. Springer Singapore, 2022.
11. Ghosh, Mridul, Himadri Mukherjee, Sk Md Obaidullah, K. C. Santosh, Nibaran Das, andKaushik Roy. “LWSINet: A deep learning-based approach towards video script
identification.” Multimedia Tools and Applications 80, no. 19 (2021): 29095-29128.
12. Lasker, Asifuzzaman, Mridul Ghosh, Sk Md Obaidullah, Chandan Chakraborty, andKaushik Roy. “LWSNet-a novel deep-learning architecture to segregate Covid-19 andpneumonia from x-ray imagery.” Multimedia Tools and Applications (2022): 1-23.
13. Ghosh, Mridul, Somnath Chatterjee, Himadri Mukherjee, Shibaprasad Sen, and Sk Md
Obaidullah. “Text/Non- text Scene Image Classification Using Deep Ensemble Network.”
In Proceedings of International Conference on Advanced Computing Applications, pp.
561-570. Springer, Singapore, 2022.
14. Lasker, Asifuzzaman, Mridul Ghosh, Sk Md Obaidullah, Chandan Chakraborty, Teresa
Goncalves, and Kaushik Roy. “Ensemble Stack Architecture for Lungs Segmentation from
X-ray Images.” In International Conference on Intelligent Data Engineering andAutomated Learning, pp. 3-11. Springer, Cham, 2022.
15. Chatterjee, Sankhadeep, Soumyajit Maity, Mayukh Bhattacharjee, Soumen Banerjee, Asit
Kumar Das, and Weiping Ding. “Variational Autoencoder Based Imbalanced COVID-19
Detection Using Chest X-Ray Images.” New Generation Computing (2022): 1-36.

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16. Lasker, Asifuzzaman, Mridul Ghosh, Sk Md Obaidullah, Chandan Chakraborty, andKaushik Roy. “Deep Features for COVID-19 Detection: performance evaluation on
multiple classifiers.” In International Conference on Computational Intelligence in Pattern
Recognition, pp. 313-325. Springer, Singapore, 2022.
17. Gayathri, J. L., Bejoy Abraham, M. S. Sujarani, and Madhu S. Nair. “A computer-aided
diagnosis system for the classification of COVID-19 and non-COVID-19 pneumonia onchest X-ray images by integrating CNN with sparse autoencoder and feed forward neuralnetwork.” Computers in Biology and Medicine 141 (2022):105134.
18. Ronneberger, Olaf, Philipp Fischer, and Thomas Brox. “U-net: Convolutional networks forbiomedical image segmentation.” International Conference on Medical image computing
and computer-assisted intervention. Springer, Cham, 2015.
19. “Tuberculosis chest X-ray datasets.” National Libray of Medicine, 30 Mar. 2021,
lhncbc.nlm.nih.gov/LHC-downloads/dataset.html.

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Kinsuk Giri*of Computer Science &Remarkable Department
Engineering, National Institute ofTeachers’ Training andContributions by Jaina TechnicalResearch, Kolkata, West Bengal, India
Mathematicians in Debi Prasad Mishra
National Institute of Technical Teachers’
and Research, Kolkata, West
Ancient India Training
Bengal, India*Corresponding author, email:kinsuk@nitttrkol.ac.in

Abstract
We mostly consider on Jainism about their religious teachings, which indeed are very profound.
However, Jainism also has a rich tradition in Mathematics. The main focus of the present
article is to represent a brief outline on significant contributions of Jaina works on mathematics
during ancient India, which were made during from BC 6th century to the AD 5th century.
Keywords: Ancient India, Mathematics, Jainsm, Sulbasutras, Infinity.
1.

Introduction

The history of Indian civilization is segregated into three eras viz. ancient India (pre-hostoric –AD 500), medieval India (AD 500 – AD 1857) and modern India (AD 1857 - Present). The
first ever enough indirect evidences of application in mathematics are obtained during the
beginning of Indus Valley civilization (3000 BC) which lasts up to next 1000 years(Srinivasiengar, 1967). Aryans entered India after the Indus civilization collapsed around 2000
BC and founded the Vedic civilization. Fortunately, the first ever evidence related to Indianculture that includes mathematics is revealed during this Vedic civilization. The word ganita,
which literally means the science of calculation, first appears in Vedic era. Nevertheless, to bementioned that the word Ganita equivalent to the word mathematics in Indian context. During
the post-Vedic period, Buddhism and Jainism experienced a surge in popularity in India.
Jainism is not only religion but also a philosophy. Around 600 BC, Jainism was founded inIndia. It became the dominant religion in the Indian subcontinent and made significant
contributions to the development of Jaina mathematics. Important mathematical works during
Jaina period were developed and discovered from around 300 BC to 400 AD (Sykorov, 2006).
The chronology of the history of ancient Indian mathematics is not actually linear. Manyevidences show that the work of one group of people is proceeded by the work of another groupand so on. However, In Fig.1, a tentative chorology in context of ancient Indian mathematics isgiven for obtaining the overall idea. It is true but sad fact that the contributions of mathematics
during ancient Indian period are highly neglected by rest of the mathematics community. In
addition, ancient Indian contributions on mathematics and astronomy are unfortunately
underrated compare to the contributions made by Egyptians, Babylonians, and Arabians. It isrelevant to mention here that the mathematics at Ancient India was the highest in the world
until the beginning of 17th century.

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Figure 1: Schematic Diagram of Ancient Indian Mathematics Timeline2.

Significant Contributions of Jain Mathematics

Jainas of ancient India took keen interest in the study of mathematics as this was treated as anintegral part of their religion. A number of Jaina texts of mathematical importance have been
analysed by various groups while a number of scripts yet to be studied due to damage conditionsof the manuscripts. Tens of Prajnapti and Sutras (canonical literature) were revealed from time
to time. Few of these are Jambu Dwipa PrajnaptiSthananga Sutra, Surya Prajnapti,
Uttaradhyayana Sutra, Anuyoga Dwara Sutra and Bhagwati Sutra. Ganita-sara-samgraha which
was written by Mahavira (AD 850), written by the founder of Jaina religion, was himself amathematician.
2.1

Themes of Jain works in Mathematics

Jain mathematicians sought to provide evidence for these beliefs through rigorous study andlogical proof. According to Jainism, time and the universe are both infinite. They aimed todemonstrate the endless nature of time, life, and the universe using mathematical principles.
There were ten separate themes (Dutta, 1929) on the work of then Jain mathematicians. These
are:
i.
ii.
iii.
iv.
v.
vi.
vii.
viii.
ix.
x.

vyavahara (subjects of treatment)
parikarama (fundamental operation)
rajju (geometry)kalasavarna (fractions)rasi (heap, mensuration of solid bodies)varga (quadratic equations)yavat-tavat (simple equation)
ghana (cubic equations)vikalpa (permutations and combinations).
varga-varga (biquadratic equations)

The exact meaning of some of the above themes is not properly revealed. Although, few
controversies are there, still maximum themes of Jain mathematics are significant and27

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meaningful. In the light of all available texts and evidences, few of the above themes are
discussed in this article as below:
2.2

Measuring Huge Distance and Time

Prior to Jain's era, mathematics was primarily used for practical purposes such as trade,
astronomy, and religion, and was limited to dealing with small numbers. The concept of infinity
was not yet incorporated into existing mathematical methods. Jain mathematicians approachedthis problem by making a fundamental assumption: to reach the highest place, one must first
ascend to higher levels, and then continue to ascend even further. Only then can one reach vastdistances, including infinity. As a result, they began their work by attempting to find large
numbers using established units and concepts. The thinking process of Jains, as illustrated in
Figure 2, is based on the assumption that if their God travels one lakh Yojana (equivalent to ten
lakh kilometres) with in one second, subsequently the God will travel the said distance within
six months is called a Rajju. It is worth noting that this distance is equivalent to
15,552,000,000,000 kilometres, which is the distance travelled by light in 60 days (Dutta,
1929).

Figure. 2: Concept of Huge Numbers by Jains (Human image courtesy:
www.hermesstaffing.com)
Similarly, we can give another example that Jains thought at this era in connection with thediscovery of Pallya. According to their theory, a pot with a length, breadth, and depth of one
Yojana each is filled with a hair collected from a sheep at every century. The total time it takes
to fill the pot is referred to as one Pallya (Kaye, 1929). Assuming an average hair length of fivecentimetres and a volume of 0.0001 cubic centimetres, the pot's volume would be
1000000000000000000 cubic centimetres. Therefore, one Pallya would be equivalent to1,000,000,000,000,000,000,000,000 years. This is an incredibly long time!
2.3

Different types of Counting Proposed by Jains

Jainism employs following three types of numbers for counting (Hayashi, 1995):
 Sankhyat (countable) Asankhyat (countable but infinite) and Anant (endless and not countable)
To represent large numbers, Jain mathematicians developed a new number writing method
using index numbers, which involve raising small numbers to powers. The term 'Ardhacched'refers to the number of times a number can be divided by 2 without leaving a remainder. For
example, the Ardhacched of 524288 is 19, as this number can be divided by 2 nineteen times.
Similarly, 1162261467 can be written as 3 raised to the power of 19, making the Trikacched ofthis number 19. In other words, 2 raised to the power of 19 equals 524288. Correct formulas

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for both of the combinations and permutations are seen clearly in Jaina works. Few of theseformulas for combinations and permutations are given below.

Jains developed the method that helps to find the concept of combinations. This was termed bythem Meru Prastara. This involved forming an early version of Pascal's triangle using thefollowing formula.

Around 10th century AD, a the then commentator explained Meru Prastara in a nice way (Datta
and Singh, 1935). To begin, draw a square. Then, draw two squares below it, starting from themid position o of the bottom side. Repeat this process, drawing three squares below the previousrow, and so on. In the top square, write the number 1. Additionally, in each row, write thenumber 1 inside the first and last squares. For every other square, write the summation of thenumbers in the form of two squares above it that overlap it. In figure.3 the same been illustrated.
It is unfortunate that French mathematician Pascal discovered the same thing Jains Meru
Prastara and gave a nomenclature as Pascal’s Triangle. However, actually Pascal’s Triangle
was discovered by Jains before 2000 years ago of French mathematician Pascal.

Fig.3: The diagram Meru Prastara given by Jains

2.4

Discovery of the Idea of Infinite Number

The Jaina sages used extensive time and effort to solve problems involving large numbers, andeventually developed a method for approximating infinity through calculation. Their initialapproach was to take the largest number imaginable, denoted as A, and then square it to obtain
A squared. Take this as B. Now repeat the process to obtain B squared. Continue this pattern toapproach infinity.
Later, they gave systematic proposal which is given below. In Fig.4, we tried to visualize thesesteps.

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 Take a wide pot as the similar size of earth and count mustard seeds in it.
 Even when the pot is full, you still won't be able to reach the highest number.
 If you repeat the process with a pot that is one thousand times larger.
 It will still be smaller than the highest possible number.
 A symbol was needed to represent this quantity that does not stop.
Hence, consequently the concluded that “The term used to describe a number that is larger than
any number that can be imagined or calculated by the human mind is called ASEEM or
INFINTY”.

Figure.4: Concept of Infinity by Jains. The image of mustards with wooden spoon is takenfrom www.amazon.in and the globe image is taken from www.dreamstime.com
2.5

Mensuration Formulae given by Jains

There are several evidences in support of works on Mensuration by Jains. It is clearly seen at
the book Tattvartha Sutra (also called Tattvarthadhigama Sutra) a book written bu Umaswati(Sarasvati, 1979). Umaswati's work is the earliest comprehensive Jain philosophy text that is
accepted as authoritative by all four Jain traditions. It was the first Sanskrit language text on
Jain philosophy. Umaswati stated the following formulae for the mensuration of a circle in his
book (B. S Jain, 1987).


Circumference of a Circle © = √10 ∗Area of a Circle = ∗

∗d

∗ ,

If a = arc of a segment of a circle, c = its chord and h = its height, then


4∗




∗

√

6



√

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2.6

Values of π by Jains

In Greek Alphabet, the sixteenth letter Pi (π), is most widely known mathematical constant. It is
well known that it equals the ratio between circumference and diameter of any circle (π =C/D). The value of π had been reported in Jaina mathematics at the various number of researchworks (Gupta, 1986; Jha & Jha, 1990). In Sulabasutras, it was reported that the value of π as
18 * (3–2√2) = 3.088. It is also founda at Manava Sulabasutra that the value of π to be 28/5=
3.125. According to Baudhayayana Sulabasutra, the perimeter of a pit is three times its
diameter, which leads to an approximation of the value of π as 3. Moreover, the ancient Jaina
school of mathematics preferred the approximation π = √10.
3.

Concluding Remarks

The ancient Jain mathematicians from India were very sophisticated with respect to their
wisdom and knowledge. This is relevant to mention here that the Jain mathematicians had adeep understanding of the concept for defining an extra ordinary large number which is later
termed as infinity. Apart from that they also worked on mathematical progressions, power
indices etc. This article highlights the need for further research in an uncharted area. Jaina
mathematics, which is over two thousand years old, may provide insight into the foundationsof mathematics. Hence, we should reveal these in order to honour this rich mathematical
heritage.
References
1. B. Datta, The Bakhshali mathematics, The Bulletin of the Calcutta Mathematical Society,21, 160, 1929.
2. B. S. Jain, Contribution of Ancient Jaina Mathematicians, Deshbhushanji Maharaj
Abhinandan Granth, 1987
3. C. N. Srinivasiengar. The History of Ancient Indian Mathematics, World Press Private Ltd.,Calcutta, 1967
4. B. Datta, A. N. Singh, History of Hindu Mathematics (part I), Lahore: Molital Banarsidass,
1935
5. I. Sykorova, Ancient Indian Mathematics, WDS'06 Proceedings of Contributed Papers, PartI, 7–12, 2006
6. G. R. Kaye, The Bakhshali Manuscript, Archaeological Survey in India, New Imperial
Series XLIII, vol.I, Parts I-II, Central Publication Brand, Calcutta, 1927
7. R. C. Gupta, Madhavacandra's and other octagonal derivations of the Jaina value π = √10,
Indian J. Hist. Sci. 21 (2) (1986), 131-139.
8. Sarasvati. Geometry in Ancient and Medieval India. Indological Publ., Delhi, 1979
9. S. K. Jha and M. Jha, A study of the value of π known to ancient Hindu & Jaina
mathematicians, J. Bihar Math. Soc. 13 (1990), 38-44.
10. T. Hayashi, The Bakhshali Manuscript: an ancient Indian mathematical treatise, Groningen,
1995

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N. Anantha Krishna*of Mechanical Engineering,
The Alchemy of Wootz Department
MBU, erstwhile SreeVidhyanikethan
Steel Production In Engineering College, Tirupati, A.P
Prasad Mishra
Ancient India Debi
National Institute of Technical Teachers’
Training and Research, Kolkata, West
Bengal*Corresponding author, email:nanathakrishna@vidyanikethan.edu

Abstract
These days, the mystical alchemy of generating a 'damask' pattern on ultra-high carbon Wootz
has gathered much research attention. Anachronistic technologies emerged during theevolution of ancient Indian civilizations due to the constant development of ancient technicalmarvels. These advancements spurred society and cultures to adapt humanity to the history oftechnology. The history of Indian technology can be traced back to prehistoric periods, withdifferent thought-provoking pieces of evidence, one of which is transmutation. Ancient Indian
Alchemy advocated transmuting base metals into gold to synthesize metallic medicaments andelixirs that resolve many rampant and morbid diseases. As a result of the transmutation ofmultilevel structures of materials from atomic to coarse level, alchemy tries to create a distinct
material with specific attributes. In this work, the alchemy of Wootz developed in India, suchas pyro-technology, quenching, and detoxification procedures.
1.

Introduction

Technology that is now considered anachronistic arose alongside the growth of ancientcivilizations as a result of the ongoing development of ancient technical marvels. These
improvements encouraged nations and cultures to introduce humans to the development oftechnology. One such marvel is Wootz. Wootz was coined when European travellers from the17th century onwards came Anachronistic technologies arose with the advent of ancientcivilizations as a result of continuous advancement in technical marvels of antiquity across its
making, in southern India. Wootz is the Anglicization of ‘Ukku’, the Kannada word for steel.
Wootz was the Anglicization of ‘Ukku’, the Kannada word for steel. In this regard, comparable
to how Wootz was esteemed in ancient India, Bintie was a steel of high regard in ancient China.
It was manufactured in China or imported from India or Central Asia. Diverse opinions exist
regarding its composition; some contend that it was merely Indian Wootz steel, while others
assert that it was a locally produced steel of indigenous origin Also, it was discerned as a typeof iron that was produced in ancient China by a process called “direct reduction” [1]. Many
researchers found that India was burgeoning in producing colossal quantities of this worldfamous Wootz steel during those times, and the fame for this steel was mustering much attention[2]. The technology of making high carbon wootz steel by crucible processes consummated inSouthern parts of India is a preeminent pyro-technological and pyro-metallurgical achievement
investigated by several researchers [3]. A complex thermo-mechanical procedure involving
forging and annealing transforms them into magnificent steel cakes.[4]. In this regard, making
sword blades from wootz steel with an idyllic interior design is also an unparalleled feat.
Accordingly, few researchers attributed this as a feat of Quantum black smithy [5]. They laid

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sufficient evidence for such ingenious quantum-level marvels in a multi-scale materials
architecture [6]. In that domain, quantum design triumphed, employing the best materials,
expertly manipulating elements and compounds, and outperforming mediaeval alchemists'
fantasies. [7]. Accordingly, Seventeenth-century Damascus sabers revealing cementite
nanowires and carbon nanotubes stirred several speculations. The researchers investigated anddeliberated on vital mixes involving organic molecules, specific metal catalysts, diverse ores,
and unique components. [8]. Due to interfusion, heat cycling, and cyclic forging, thecomponents above eventually consolidate into planar arrays parallel to the forging plane,encouraging the growth of nanotubes and the formation of cementite nanowires and coarse
cementite particles. These changes gave the metal very significant mechanical properties. In
this connection, early Swiss metallurgist and professor Zschokke was fortunate enough toobtain a few wootz blades for the research. In his book, Manfred Sachse details a portion ofZschokke's findings on some of the mechanical properties such as hardness, bending toughness
etc and general composition of those samples [9]. While the historical methods of Wootz steel
production did not meet modern environmental requirements, certain aspects of the process
may be deemed environmentally beneficial.
The use of natural elements such as iron ore and carbon rich additions derived from organic
sources such as leaves or wood demonstrates a commitment to sustainability. Further, themethods involved in crafting Wootz steel were intricate and often carried out by skilled artisansin smaller quantities. The process required careful attention to detail and specific conditions forthe carbonization and quenching phases. As a result, while Wootz steel was highly prized for
its exceptional qualities, including strength and sharpness, its production volume was likely
limited. The demand for Wootz steel might have been met within certain regions or specialized
markets, but specific evidences on production in quantities that could fulfil large-scalerequirements, such as those seen in contemporary industrial steel production remain unclear,which can be given significant research attention. Because of the apparent similarities in their
origins, few researchers believe that specific recipes exist. These formulas derive from Indianalchemy. In antiquity, blacksmiths, alchemists, and astronomers all presented convincing proof.
The consummate inferences parallel present characterisation approaches using only naked-eye
observations on metals, fires, and phase transitions [3]. As a result, techniques and recipes basedon ancient Alchemy ideas are likely to be included.
2.

Pyro-technology in Ancient Indian Alchemy

Transmutation is an intriguing piece of evidence demonstrating the origins of Indian technology
in prehistoric times. Its primary goal is to transform a substance's dynamic potential byeliminating any inherent contaminants A 14th-century text, "Rasendra Sara Sangraha,"describes the macro analysis of metallic specimens and how to analyze them by their luster andform of the fractures. This shows the state of the art in alchemy in the 14th century. [10].
Metals in medicines date back to Sushruta (400 AD). However, its supremacy was significantly
propounded in the 8th century, particularly during the Tantric period of the 9th century. Indianalchemy (Rasavada) came to full speediness during the 10th-14th centuries. Over the time,
numerous inorganic materials piled into their Materia Medica, and many Pyro-metallurgicalprocedures were heaped into their texts [11].
Hence, Indian alchemy deals with the alchemical and biopharmaceutical transmutation ofminerals, metals, and herbs. It majorly encompasses the Purification procedures of materials as
mentioned above, and their calcination process. After serious pyro-technical procedures, metals
transform into inorganic compounds, mostly identifiable as simple oxides. It is unknown if any

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organometallic complexes form in them during this process. If one observes a zinc sample after
calcination, it consists of 87.2% zinc oxide and 0.6% potassium. This was not an easy procedure
as for zinc, and the texts usually prescribe 10-12 incinerations. With this, one can estimate
typical heat treatment procedures adopted by the ancients in transforming metal into the internal
administrable form [12].
Pyro-technology forms a strong background and core constituent in the incineration andcalcination. Blacksmiths were taught some of these procedures and used them to quench their
weapons, even in blood and urine [13]. Their quenching methods seem to take inspiration fromalchemical procedures like Nirvana (hardening) and Dravikarana (liquefication) [14]. Arrowquenching procedures and heat treatment of ancient surgical instruments are good evidence. B.
Prakash gave a detailed description of the construction of furnaces, classification of iron andsteel, design, composition, and process of marketing crucibles by referring to ancient Indianalchemical texts [15].
In the 17th Century, modern chemistry was emerging from alchemy, and fire science was in an
infant stage, during which German chemist J.J Becher (1635-82) and Stahl (1660-1734)
collectively proposed Phlogiston (in Greek Phlogiston is the fire of earth) theory. It emphasizes
that any flammable material contains Phlogiston, liberated as smoke, heat, and light, where
weight loss is another indication. They also proposed that all solid materials contain Phlogiston
in variable proportions, and as a result, they exhibit differentiation in burning. Lesviev Woodcock regards Gibbs's free energy as a phlogiston alternative, and prior to 1850, all scholars ofscience believed in caloric theory till the initiation of thermodynamics. Based on Joseph Black's
concept of Phlogiston's absolute Levity C.E Perrin summarised the framework as follows:
Phlogiston is ponderable, imponderable and weightless, and Phlogiston has absolute levity. The
Greek's concept of Phlogiston as the fire of earth, its escape from the body in a whirling motion,and its relation with air can also be found in ancient Vedic references as Agni (Fire God).The
34 names of Agni mentioned in the very old Sanskrit thesaurus Amarakosha more or less speaks
the same properties of Phlogiston. MM. Vineland DE Morveau laid some lines of thought
proposing that Phlogiston does not gravitate towards the earth's centre; instead, it rises, andhence an increase of weight in metallic calx is found. The alchemists believed that every
material contains (molten fluid and vitreous earth). The idea of the levity of Phlogiston seems
to originate from the ancient idea of the levity of fire. Aristotle also held this old view by Indianalchemists where matter/ substances were characterized by elements [16-18].
Matter/substances can be arranged from fixed to volatile, with fire at the top; Pythagorean's
fourth tetractys of simple bodies also lays the same material scale. They assigned the monad,
which is fire in the first tetractys, as the most volatile, followed by the duad, which is air (lessvolatile than fire), and finally water and earth as triads and tetrads (more fixed than thosementioned above). They proposed that elements be classified based on their tenuity and density.
According to Samkhya philosophy, the five elements (air, water, earth, fire, and void) areamong the nine categories that comprise a substance (the remaining four being time, space, self,
and mind). These elements, or panchabhutas, can comprehend objects in the natural world. This
proposal is also known in Greek philosophy. This was first proposed by Empedodes (492-432
BC), and it was later popularised by Aristotle (384-322 BC). Panchabhutas (integrating
ether/Akasha) was the Indian term for it. These five elements are also classified as three gunas:
sattva, rajas, and tamas. Other than Indian alchemy, their equivalent western alchemical terms
refer to them as three philosophical principles: salt, sulphur, and mercury, as explained in detailin Bhojas Works [19]. Few researchers look into the ancient classification of a substance basedon its taste and the dominant elements receding in it. Asthtangahridaya's Vagbhatta created 20

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attributes of a substance and their equivalent mapping to each element that form tags in a partwhole schema [20]. In other words, all materials used to manufacture wootz-type Damascus
steels can be classified as Sattva, Rajas, and Tamas; among them may be materials containing
flammable earth-Rajas or philosophical sulphur.
3.

The Alchemy of Wootz

Wootz steel was earlier produced in India by introducing carbon into the crucibles in wood ororganic additions. Table 1 indicates the presence of carbon content in percentage from different
wootz objects as notified by B. Prakash et al. [15]. Specific elements, such as iron and carbon
sources such as wood or leaves, are combined in the carbonization process of Wootz steel. In acontrolled atmosphere, the mixture is heated, allowing carbon to infuse into the iron. This
critical phase leads to Wootz steel's particular qualities, such as its strength and sharpness.
Specific elements, such as iron and carbon sources such as wood or leaves, are combined in the
carbonization process of Wootz steel. In a controlled atmosphere, the mixture is heated,allowing carbon to infuse into the iron.
In Wootz steel manufacture, the carbonization process is critical to producing the correctcomposition and properties. However, the first in-detail analysis of Wootz in the middle of the
19thCentury indicated 1.33% of combined carbon and 0.312% of uncombined carbon [21].
Also, this, along with metallic catalysts and organic additions, gave rise to nano carbon tubes
as speculated by few researchers: such inclusions and detoxification recipes much prevailed inAncient Indian Alchemy, which is discussed in the next session. The transformation of Indian
Wootz into a Damascus blade is depicted in Figure 1, and the idyllic pattern in Figure 2.
Table-1 Carbon composition in wootz steel objects (Source: B. Prakash [15])
Sl. No.

Object%C

Reference No.

1

Ingot

1.64 to 1.68

14 P.822

Wootz swords

1.33 to 1.87

14 P.82

3

Swords

1.1 to 1.8

14 P.824

Wootz (Srilanka Origin)

1.97

26 P.390

5

Wootz (Indian Origin)

1.64

26 P.390

6.

Wootz (Indian Origin-Mysore)

0.45-0.9

26P.390

Wootz was synthesized from the carbonization of wrought iron, heated in closed crucibles withspecific ingredients over the fire, and operated with bellows. The operation was repeated toremove excess carbon, and the entire process took several hours to complete. In some parts,
quenching and mystic quenchants were also employed. It was a highly pure iron with 1.5%
carbon. The cakes were shipped to several parts and forged to swords, which displayed an
idyllic pattern, as shown in Figure 3. Herein, the hypereutectoid level plays a significant role inexhibiting such luster due to the alignment of cementite particles during cooling. The uniqueinternal microstructure was replete with bands of small cementite particles along the band's
centre line.

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Figure 1: Manufacturing of blades from Wootz (Source: A.K Biswas [22])Figure 2: Unique banding patterns on Wootz (source: Wikipedia)

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Figure 3: Pyrotechnology-Crucible furnaces for wootz steel (Source: B. Prakash [15])The structure is a function of the heat treatment process but was essentially found in pearlite.
The bands were found to be aligned parallel to the plane of forging, and further manipulationof his angle produced a variety of intricate patterns. According to recent research, ultra-highcarbon steels have superplastic properties because the pro eutectoid cementite network
microstructure forming along the grain boundaries of earlier austenite may result in a finer
distribution of spheroidised cementite particles in a fine matrix. The two typical processes invogue are presented in Table 2.

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Table 2: Wootz steel manufacturing process
Sl. No.
1.

Process

Details
chargedRemarks

South Indian

Crucibles

withProcess

Wrought iron + 5-10 % ofwood charcoal (in % wt) of
Cassia Auriculata, and 1 to 2%of leaves of Calotropis gigantic(called avaram in Tamil), anda) Crucibles were covered with charcoal indome fashion.
b) Foot-operated drum bellows were vogue
in central India.
c) In large hand bellows, buffalo/goat skinblowers were involved.

the mouth was sealed with clay.

d) Typically, 20 to 25 crucibles under

The charge was covered with

1450oC to 1550oC for 6 hours.

one or two leaves of plants likee) Solidified wootz ingots reveal thick

Convolvus

laurifoliaorcementite plates in transformed

Asclepias gigantea. or Tandra

ledaburite and pearlite matrices.

tegga(in Telugu) with leavesf) Quenching was done in moist clay, dry

and rice husk. The crucible issand heap, and throwing water.

then sealed with a sun-driedclay lid or a ball of moist clay.
2.

Hyderabad

Large pine-shaped granite claya) Steel was produced by refining white

Process

crucibles with ground pieces ofcast iron with 2.5% carbon by theoil, rice husk, grog closed withchemical action of highly oxidizing

a ball of clay and lid.

fayalite slag.
b) During decarbonization, somedephosphorization was noted.
c) The furnace was a double chambered
furnace with ground level pit.
d) Molten metal was allowed to solidify inthe furnace.
e) Composition: Carbon 1.68%, Silicon
0.43%, Phosphorus 0.02% and Sulphur
0.2%.

4.

Purification and detoxification procedures in Indian Alchemy

C.S. Smith emphasizes that an overarching structure occurs due to hierarchy and anarchy ofsmaller parts at different levels of structures- structure, substructure, and superstructure.
Accordingly, two possible dissymmetrical interplays arise between the surfaces of part and38

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whole. Hence, all things are interactive – both up and down the scale of clumpings. Specifically,
the hierarchical alteration between a material's externally noticeable attributes/traits and aninternal structure responsible for it occurs at all levels and applies to all things [23]. Specificpyro-technological processes, quenching techniques, quenchant recipes, and detoxification or
purification procedures may enhance such possibilities. Pre-treatment of raw materials in
extractive metallurgy and preparation of metallic medicines was considered the most crucialpart of Indian Alchemy. It is known as "Samskara," and the ancient Indians believed that thisprocess transmutes a substance's latent attributes, which leads to the summation of new ones.
Numerous modes of such treatments are stated in Indian alchemical texts, such as Svedana(Boiling), Mandhana (churning), Mardana (grinding), Bhavana (impregnation), Nirvana(heating and quenching in prescribed mediums) etc. Some of them are shown in Figures 4(a)and 4(b) used for detoxification and particle size reduction. The Ancient quenching techniques
are believed to be developed out of mysticism, empirical experimentation and intervention ofsome typical quenchant recipes. A few pieces of historical evidence are shown in Table 3.

Figure 4: (a) Swedana Yantra and (b) Dola Yantra [24]Table 4 highlights some of the material treatments involved in Indian Alchemy. Each treatment
is a distinctive method for the specified raw material. It is believed that subtle changes inmaterials can be brought by some arduous purification and detoxification procedures thatradically transform the chosen material.
Table 3: shows significant Quenching indications in the Historical Period pre-Christian Era
India [25]Sl.No.

Time line

Quenching details

1.

528 BC2.

600-300 BC

3.

3rd Century BC

4.

2nd Century BC

At Ujjain Site, large varieties of offence equipment,slags, fluxes,
water jar-indicative of quenching
At Jajamau site, Quenched and tempered axe with laminatedstructure at the edge
At Pandu RajarDhibi, Sharp cutting edge sickle with a tempered
martensitic structure
At Ror, Kangrasite,Rapidly quenched ferrite and martensite plates on
Iron razor

L.A Chernozatonsk II et al., investigated the conditions for generating nanotube structures incavities innate in metal matrices. It is found that treatment modes and composition are
significant factors in the generation of the same. Few researchers studied the possible
mechanism involved in generating such tiny structures. It is found that metal particles enable
the ousting of carbon and hydrogen at cooling from melt into the discontinuities [25]. C.Y
Jagtap et. al. worked on purification and detoxification procedures w.s.r to Copper in Medival
Indian alchemical medicine; it elucidates several metallurgical phenomena like hydrogen

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embrittlement, nano crack forming, and bio-beneficiation, as innate aspects of the traditional
Indian Alchemical recipes- especially Nirvapavis-a-vis quenching [26-27]. Specific coatings
were applied on crucibles mentioned in Ancient Indian Alchemical Pyro-metallurgical works.
Few researchers studied thermogravimetric analysis of such coatings [28]. During suchprocesses, two-dimensional interfaces give a separate identity of things in three dimensions.
Also, inter-dimensional interaction gives the very essence of form, and uniformity of tiny
particles' relationship to their neighbor [6]. Further, the boundaries are the source of inchoation
for both strengths and weaknesses and provide a solid basis for any crystalline change's
inception. With the reduction in temperature, hydrocarbons decompose under the catalytic
influence of nanoform. Besides, nanotubes in pores are formed in carbon vapor at temperatures
below 12000C. The flow collides the micropore's surface in the breakneck cooling matrix, andbulker fragments are deposited first crack offshoots. Hence, nano entities take in particles of alesser mass, further becoming the nuclei of nanoarrays on which lighter particles are deposited
after supplementary cooling [29-30]. CNTs were synthesized involving pretreated plantmaterials – heat treatments in the air for partial carbonization – and a low-temperature cyclicoxidation process in the presence of air.
Table 4: Indian Rasavada material treatments
Sl.No.

5.

1.

Material
Treatment
Nirvapana

2.

Dhalana

MethodMaterial mediums

Heating and Dipping into
liquid Medium.

Acidic(Triphaladecoction),Alkaline(limewater),Oils and milk
Churbidaka(lime water),Ghee and
Milk

3.

Heating for melting the metalor semi-metal and dipping ina liquid medium
Urdhvapathana Sublimation and distillation

4.

Adhahpathana

Sublimation and distillation

5.
6.

Bharjana
Bhavana

FryingTirturation

7.
8.
9.
10.
11.

Virechana
Shoshana
Mardana
Sthapana
Svedhana

Cleansing and sifting
Shade dryedRubbing
Soaking in the liquid medium
Boiling or fomentation

Usually Seeds are used
Usually Seeds are used
Juices or Salts or Decoctions
Cows –Urine , Lemon Juice
Steam of water or milk,
Lemon Juice,

12.

Avapa

Special herbs like Devadali

13.

Shodhana

14.

MaranaKeeping metals in melted
media
Include one or all above
mentioned procedures
Incineration

Paste of Acidic medium like
Lemon juice
Paste of Acidic medium like
Lemon juice
Ghee
Garlic, ginger, salts, Lemon juice

All Materials including metals
and minerals
Metals and minarals including
Mardana materials

Conclusion

In conclusion, the transmutation of wootz steel as an art form with quantum-level forging
emerged in India with the nanoscience of nanotechnology or material science but drew
considerable attention as a novel material. Although many of the wootz-making recipes appear

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to be riddled with mysticism and speculative experimentation, a growing body of literature on
ancient Indian Alchemy, pyro technology, quenching, and other detoxification procedures
mentioned in it emphasizes the transmutation of the material hierarchy to achieve any novel
material. Depending on the alchemical selection and characterization of elements, organic
additives, and catalysts most likely created an idyllic miracle.
References
1.

W. L. Bintie: the wootz steel in ancient china, Indian Journal of History of Science, 44.3,
369-388, 2009.
2. S. Srinivasan and S. Ranganathan, India's Legendary wootzsteel : An Advanced Materialof the Ancient World, Tata Steel, 2004
3. O. D. Sherby and J. Wadsworth, Ancient Blacksmiths, the Iron Age, Damascus Steels andmodern metallurgy, Journal of Materials Proceeding Technology, 117, 347-353, 2001.
4. O. D. Sherby, and L. Wadsworth, Damacus Steels, Scientific American, p. 118, 1987
5. S. Srinivasan and S. Ranganathan, India's Legendry Wootz Steel: An advanced Material of
the Ancient World, Tata Steel
6. C. S. Smith, Structure, Substructure and Superstructure, Reviews of Modren Physics, 1965
7. G. B. Olson, Designing a New Material World, Pathways of Discovery, 288, 2000.
8. M. Reibold, P. Paufler et al., Discovery of Nanotubes in Ancient Damascus Steel, Physics
and Engineering of New Materials, 2009
9. J. Wadsworth, Archeometallurgy related to swords. Materials Characterization. 99:1-7,
2015
10. G. K. Bhatt, Rasender Sara Samgrah, Choukhambha Sanskrit Sansthan, Varanasi, 2007
11. S. C. Sankhyadhar, D.Sankhyadhar and R. Nighantu, Chaukambha Orientalia, Varanasi,
2010
12. R. C. Kapoor, Some observations on the metal-based preparations in the Indian Systems ofMedicine, Indian Journal of Traditional Knowledge 9,562-575, 2010
13. D. S. Mackenzie, History of Quenching, Advanced Materials and Process, 2015
14. Y. C. Jagtap, P.K. Prajapati, R. Patil and Y. S. Chowdary, Therapeutic uses of Tamra(Copper) bhasma – A review through BhaishajyaRatnavali, 3,128-135, 201415. B. Prakash, Ferrous Metallurgy in Ancient India, Metallurgy in India: A Retrospective, 5291, 2001
16. A. Rorsch, Climate science and the phlogiston theory, Energy & Environment · Vol. 18,
No. 3+4, 2007
17. M. Caamano, A Structural Analysis of the Phlogiston Case, 70:331–364, 200918. D. Allchin, J. Hutton and Phlogiston, Annals of Science, 51:6, 615-635, 2014
19. M. Roy, The Concept of Yantra in the Samarangana – Sutradhara of Bhoja, Indian Journalof History of Science 19, 118-124, 1984.
20. A. Balkrishna, Ashtanga Hridayam, Divya Prakashan, 2014.
21. P. Neogi, Iron in Ancient India, Prathibha Prakasan, 2007
22. A. K. Biswas, Minerals and Metals in Ancient India, D.K. Print World 1, 1996
23. C. S. Smith, A Search for Structure, MIT Press, Cambridge, 1981
24. P. Rây and P. C. Rāy. "History of chemistry in ancient and medieval India: incorporating
the History of Hindu chemistry." (No Title) (1956).
25. L. A. Chernozatonski, V. P. Val Chuk et al., Synthesis and Structure investigations of
Alloys with fullerene and nanotube inclusions, Pergamon, 35: 6, 749 – 753, 1997
26. C. Y. Jagtap, B. J. Patgiri and P. K. Prajapati, Purification and Detoxification procedures
for metal TAMRA in Medical Indian Medicine, Indian Journal of History and Science,
48.3,383-404,2013.

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Journal of Engineering, Science & Education, Vol. 1, Issue 1, 2024

27. S. Mitra, S. Gupta and P. K. Prajapati, Role of media in Shodhana process w.s.r to
metals/minerals, Indian Journal of Ancient Medicine and &Yoga, 2, 211-217, 2009
28. N. A. Krishna et. al., A Critical review on Ancient Alchemical Metal Treatment Proceduresin Rasagrandhas and Brhadvimanika Shastra, Materials Today: Proceedings, 2015
29. X. F. Xie, B. Goodell et. al., A method for producing carbon nanotubes directly from plantmaterials, Forest Products Journal, 59 (1): 26-28, 2016
30. J. Wadsworth, Archeometallurgy related to swords. Materials Characterization. 99:1-7,
2015

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Journal of Engineering, Science & Education, Vol. 1, Issue 1, 2024

Architectural Elegance
P Parmar*Through Time: Samirsinh
Department of Civil Engineering,
Desai University, Nadiad,
Unveiling the Unique Dharmasinh
Gujarat, Bharat
Construction Legacy of Debi Prasad Mishra
National Institute of Technical Teachers’
and Research, Kolkata, West
Hindu Temples in Training
Bengal, India
author, email:Ancient India *Corresponding
spp.cl@ddu.ac.in

Abstract
Each epoch possesses a discernible construction methodology, inherently unique andemblematic of the prevailing ideologies, cultural progress, artistic inclinations, andarchitectural prowess of its particular civilization. From a nuanced standpoint, Hindu Temples
stand out as embodiments of knowledge, artistic brilliance, architectural magnificence, andcultural legacy, symbolizing the pinnacle of building technology in the ancient Indian
subcontinent. The timeless principles and customs of Indian Temples go beyond historicalboundaries, echoing deeply in the present context, thereby sustaining the flexibility of
traditional Indian values and wielding a significant effect on the socio-economic tapestry ofsociety. This scholarly work delves into the intricacies of temple styles, design principles,
geometric configurations, structural systems, and the intricate construction technologies
intrinsic to Indian temples. The exposition meticulously unfolds the distinctive architectural
styles and elements inherent in Hindu Temples. Furthermore, a comprehensive elucidation of
the geometric design principles that underscored Indian temple construction is expounded upon
within this discourse. The paper meticulously delineates the construction technology,
commencing with the meticulous assembly of adept teams, through the stages of meticulous
planning, intricate carving, and culminating in the seamless assembly of individualcomponents.
Keywords: Ancient Indian Temple, Geometry, Geometrical Design Philosophy, Component
parts, Symbolism, Vaastu planning, Elements of temple architecture, Fractal geometry, Fractal
design.
1.

Introduction

Hindu temples, referred to as Mandirs in Hindi, find their etymological roots in the Sanskrit
word "Mandira." Despite variations in architectural styles across India, the fundamental
essence of the Indian temple remains consistent. The architectural concept of temples in India
took shape about 4500 years back, during the developed phase of the Indus-Saraswati orHarappan Civilization (2600-2000 BCE) [1]. Initially, these temples were outdoor shrines
located under consecrated trees and/or near water source structures. However, a significant shift
occurred during the Mahajana pada period (700 BCE), marking the emergence of the temple as

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a closed structure. The modest thatched huts in the villages of Northern India from 700 to 500
BCE served as initial prototypes for temple construction. [2]. The construction of multiple
stories temples gained momentum during the Sunga-Kusana era (200 BCE-200 CE) [3]. The
idea of dualism in Bhakti, which emphasizes the holy link between the deity and the devotee,
gained prominence after the fall of the Gupta Empire in 320 CE. [4]. This conceptual shift led
to the provision of two distinct spaces within the temple: the garbhagriha for the idol and the
Mandapa for the devotees. An early illustration of this arrangement can be observed in the
Sanchi Buddhist temple [5]. In the late fifth and early sixth centuries, during the Gupta period,
Indian temple architecture underwent a significant transformation, breaking away from thelimitation of vertical monumentality and introducing features like a second story and shikhara
or vimana [6]. Interestingly, no mortar was used in the construction of these temples. During
this time, temple iconography and architecture developed, with myths and gods being
meticulously carved into stone and becoming essential elements of the temple's construction.
[7]. Temple architecture developed in a unilineal form up until the fifth century, but by theseventh century, a regionalized style had taken hold.
The South Indian, or Dravida, style adopted a stepped Vimana that ended in a round ‘stupi’,
while the North Indian, or Nagara, style accepted a curved shikhara that terminated in a pointed
finial. [8]. Modern temple erection in North India has become further diverse, while South
India, Gujarat, and Orissa continue to adhere to traditional practices. Indian temples symbolize
both the macrocosm (universe) and the microcosm (inner space), aiming to establish aconnection between man and God [9]. Hinduism's central idea is that the temple, with its
architecture, ornamentation, and ceremonies, is a location where one might achieve ultimate
nirvana.
Indian temples promote the advancement of the arts and intellect as well. In the past, public
courts, hospitals, and schools were located within temple complexes. Spacious halls were also
used to recite epics like the Ramayana and the Mahabharata. [10]. The temples sustained
themselves through income generated from assigned cultivable land, influencing the economic
life of the community and providing livelihoods for many. This paper aims to offer acomprehensive overview of Indian temple style, design, geometry, structural systems, andconstruction technology [11]. It explains how Indian temples' resilience to seismic forces isderived from an exploration of the affiliation between structural solidity, symmetry, andproportion.
2.

Architectural Styles of ancient Indian temples

Over the course of centuries, the architectural styles of ancient Indian temples have undergone
a transformative evolution, influenced by a myriad of factors including geography, culture,
history, and regional nuances. These distinct styles represent the diverse and dynamic nature ofIndia's rich architectural heritage. The formation of the different architectural styles can be
attributed to the factors like, geographical diversity, cultural influences, historical changes,
artistic innovations, and availability of different construction materials. Table 1 depicts
different styles of architecture of ancient Indian temples and its specific characteristics over an
Indian map.

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Table 1: Architectural Styles of ancient Indian temples and its characteristics.
Sr. Architectural Region
Characteristics
Prominent Temples
No. Styleand Examples
1

Nagara Style

Primarily inNorth India

Towers with curvilinear spires Prominent in temples of(shikhara), square sanctum North India, especially(garbhagriha), and mandapa.
intheHimalayan
region.
Predominantly Towering gopurams (entrance Prominent in temples ofin South India towers), large and open Tamil
Nadu,
mandapas, axial alignment of Karnataka,
Andhrashrines,andelaborate Pradesh,andsculptural work.
Telangana.
Deccan region A fusion of Nagara and Prominent in temples ofDravidianstyles, Maharashtra and parts
characterized by a central of Karnataka.
tower with elements of bothsquare and circular plans.
Western India, Elaborate
andintricate Prominent in temples
especiallyarchitecture, often with domes likethe
DilwaraGujarat,andtoranas(arched Temples in Mount Abu.
Rajasthangateways).
AssociatedLarge and ornate structures, Prominent in temples ofwith theintricate carvings, and pillared the
Vijayanagara
Vijayanagara halls.
period, including the
Empire in
Virupaksha Temple inSouth India
Hampi.
Southern
Star-shaped
platforms, Prominent in temples
Karnataka
sculpted walls with intricate like the Chennakesava
detailing, and lathe-turned Temple in Belur.
pillars.
OdishaPyramidical towers (rekha Prominent in temples(Orissa)deula), sculptures depicting like the Lingaraja
stories from epics, and a focus Temple
in
on intricate carvings.
Bhubaneswar.
AssociatedFlat roof structures, carved Prominent in temples
with thepillars, and ornate sculptures. like the Badami Cave
ChalukyaTemples.
dynasty in
Karnataka
Tamil Nadu
Rock-cutarchitecture, Prominent in temples
monolithic structures, and like the Shore Temple
sculptural decorations.
in Mamallapuram.
Northwestern Influenced by Greco-Buddhist Prominent in early
regions ofart, featuring Greek and Buddhist monuments.
India
Roman architectural elements.

2

Dravidian
Style

3

Vesara Style

4

Maru-Gurjara
Style

5

Vijayanagara
Style

6

Hoysala Style

7

Kalinga Style

8

Chalukya
Style

9

Pallava Style

10

Gandhara
Style

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3.

Steps in Temple Construction

Temple construction in ancient India involved a meticulous process guided by religious,
architectural, and cultural principles. The steps in temple construction reflect the intricate
craftsmanship and spiritual significance associated with these sacred structures.
1. Site Selection: Choosing an auspicious location was paramount. Vaastu Shastra, anancient Indian architectural science, provided guidelines for selecting a site based on
factors like direction, topography, and energy flow.
2. Foundation Laying: The foundation, often symbolic of the temple's stability andendurance, was laid with precision. Rituals and prayers accompanied this crucial step.
3. Planning and Design: Temple architects, often influenced by regional styles, religious
beliefs, and royal patronage, created detailed plans encompassing layout, proportions,and decorative elements. The design incorporated spaces for sanctuaries, halls, andintricate sculptures.
4. Materials Selection: The choice of materials was crucial. Stone, particularly sandstone
and granite, was commonly used for its durability. Wood and metal were also employed.
The quality of materials influenced the temple's longevity.
5. Sculpture and Carving: Skilled artisans sculpted intricate reliefs and statues depicting
deities, mythological stories, and celestial beings. Each carving was imbued withspiritual symbolism and aesthetic finesse
6. Construction of Main Structure: The construction of the main temple structure
involved careful assembly of stones or bricks, following the architectural plan. Temples
often featured a central sanctum (garbhagriha), a hall (mandapa), and a tower (shikhara
or vimana).
7. Ornamentation: Elaborate ornamentation enhanced the visual appeal. This included
decorative motifs, friezes, and intricate carvings adorning both the interior and exterior
surfaces.
8. Installation of Deities: Once the main structure was complete, a consecration ceremony(prana pratishtha) took place to infuse spiritual energy into the temple. Images or idolsof deities were installed in the sanctum.
9. Ritual Consecration: The consecration involved elaborate rituals conducted by priests
to invoke divine blessings and sanctify the temple. This marked the formal
commencement of worship and religious activities.
Temple construction was not merely a physical endeavour but a spiritual and communal
undertaking. The careful adherence to religious principles, adherence to traditional
craftsmanship, and the infusion of cultural symbolism contributed to the creation of magnificent
and enduring structures that remain architectural wonders today.
The construction stages of ancient Indian temples are listed below as per the ancient
Sanskrit texts.

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Selection of Location and Soil Examination (Bhu Pariksha): This stage involves
meticulously examining and choosing a fertile land with suitable soil for theconstruction of the temple and the surrounding town.

ii.

Material Selection for the Image (Sila Pariksha): At this phase, the focus is on
selecting and examining the appropriate materials for creating the temple's sacredimage.

iii.

Crop Cultivation and Purification (Karshana): Prior to construction, a specific crop,
such as corn, is grown in the designated area. The harvested crop is then fed to cows,
signifying the readiness of the location for town or temple construction.

iv.

Ritual for Divine Favor (Vaastu Puja): This involves a ritual to appease Vaastu
Devata, seeking divine favor and blessings for the upcoming construction.

v.

Removal of Undesired Elements (Salyodhara): In this stage, any undesired elements,
such as bones, are carefully excavated from the site.

vi.

Laying the Foundation Stone (Adyestaka): The commencement of construction
begins with the laying of the foundation stone, symbolizing the initiation of the templebuilding process.

vii.

Foundation Ritual and Construction (Nirmana): Following the foundation stone, apurification ritual is performed by sprinkling water mixed with auspicious substances.
A pit is dug, filled with gems, minerals, seeds, and more, marking the actualcommencement of the temple's construction.

viii.

Placement of Top Stone (Murdhestaka Sthapana): This stage involves placement of
the top stone over architectural elements like the prakara and gopura. Before the apexes
are placed, special voids containing jewels, minerals, and seeds are formed.

ix.

Installation of Main Deity's Vessel (Garbhanyasa): A vessel made of five metals(pancaloha kalasa sthapana) is ceremoniously mounted at the location designated forthe main deity.

x.

Temple Construction Completion (Sthapana): The main deity is formally installed,
marking the completion of the temple construction.

xi.

Infusing Life into the Deity (Prana-Pratistha): In the final stage, the main deity isconsecrated and infused with divine life and essence.

4.

Components of Hindu Temple Architecture
The Hindu temples established a distinct architectural structure in the latter half of the 7th
century, and the fundamental elements in their original Sanskrit scripts are outlined asfollows:
The primary enclosure of the temple is known as ‘Vimana’, consisting of two parts [12]. The
upper section of the Vimana is termed Sikhara, while the lower part within the Vimana isreferred to as Garbhagriha (cella or internal chamber) (refer to Figure 1 (a) & (b)).
1. “Shikhara” denotes the spire or tower, shaped in a pyramidal and tapering form,
symbolizing the mythical "Meru" or the highest mountain peak.

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2. “Garbhagriha” is the womb chamber, the inmost section of any temple where the deity
exist in. It is mainly square in plan and is accessed through the eastern side.
3. “Pradakshina Patha” represents the auxiliary pathway for walking around, which
consists of an enclosed passageway outside the garbhagriha. Devotees walk around thedeity in a circular clockwise direction to offer their homage
4. “Mandapa” is situated in front of the garbhagriha, the pillared hall, serving as an
assembly point for pilgrims to chant, meditate, or observe the priests performing rituals.
5. In some instances, a “Natamandira” is provided, representing the hall for dance. In
early temple structures, the ‘mandapa’ was sometimes a separate and isolated structurefrom the sanctum.
6. “Antarala” designates the intermediate space connecting the main sanctum and thepillared hall of the temple premises.
7. “Ardhamandapa” refers to the front entrance at the main entry of the temple leading
to the main temple. Some additional essential basic elements specific to South Indian
temples include:
8. “Gopurams” are monumental sequence ofentrances to the temple premises.

pyramidal shaped domes and ornate

9. “Pitha” signifies the plinth height and type of the main temple.
10. Gateways distinctive to temples of North Indian regions are termed “Toranas.”

Figure 1: Typical Elements of Hindu temples. (Source:
https://en.wikipedia.org/wiki/Hindu_temple)

5.

Comparison between Architectural Styles

The distinct architectural designs of Hindu temples have undergone evolution driven bysignificant variations in geography, climate, culture, ethnicity, history, and language between
the northern plains and the southern peninsula of India. Primarily classified based on natural

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Journal of Engineering, Science & Education, Vol. 1, Issue 1, 2024features, Hindu temples can be broadly categorized into three main orders: the Nagara or'northern' style, the Dravidian or 'southern' style, and the Vesara or hybrid style found in the
Deccan region, serving as an intermediary between the other two. Additionally, peripheral
regions such as Waste Bengal, Kerala, and the States within Himalayan range give rise to uniqueand regional styles. Table 2 provides a comparative analysis of distinctive features between the
Nagara and Dravidian architectural styles.
Table 2: Contrasting the Architectural Styles of Nagara and Dravidian Temples(Reproduced from [13])Particulars

Nagara style architecture

Dravidian style architecture

Temple’s main spire(Shikhara Style)‘Sikhara’ above temple

The Vimana, possibly having multiple levels(talas), with its uppermost part referred to as
the shikhara

Mandapa spire(tower)Certainly

Not at all

Shape of the spire

Curvilinear above the sanctum, as
well as a straight-edged pyramidal
structure

Straight pyramidal or, at times, curvilinear
pyramidal, positioned over the shrine

Garbhagriha(Sanctum)

Ground storeyed or multi-storeyed

Usually, singular (though the Vimana may
consist of multiple storeys)

DesignMandapa, temple, and tower plans
primarily exhibit a Chaturasra (foursided) layout, with occasionalvariations such as ‘Ashtasra’,‘Vritta’, ‘Ayatasra’, ‘Ayata
Ashtasra’, ‘Ayata Vritta’, ‘Hasti
Prishta’, and ‘Dwayasra Vrita’.

similar, as well as Prana Vikara

Not a protruding feature

A distinctive feature, though not imperative;
post-10th century, frequently taller than the
Vimana. Multiple towers may be present on
all borders of the complex, functioning as
milestones for pilgrims.

Gopuram(monumental
entrance tower)

Other topographies

Sacred ponds, fewer pillared‘mandapas’ within the temple
Sacred ponds, numerous pillared ‘mandapas’
grounds (with a separate
within the temple grounds, prakara walls
dharmshala), ‘prakara’ walls are became prevalent, and the temple may featureuncommon, and the temple may havea single or multiple entrance.
a single or multiple entrance.

Foremost sub-styles

Latina, Phamsana, Sekhari, Valabhi

Tamil (upper and lower Dravidadesa),
Karnata, Andhra

Regions

Northern, western, and central
regions of the Indian subcontinent

southern parts of the Indian subcontinent,southeast Asia

Time line of enduring
stone-masonrymonuments

Late Kushana period, early Gupta:
basic architecture; 6th-10th century

Late Gupta period: elementary; 6th-10th
century: zenith

6. The Geometric Design of Hindu Temples
The Vastupurashamandala's geometry was designed to embody the concept of Purusha within
the abstract notion of the square as the highest geometric form [14]. In this geometricrepresentation, the square symbolizes the earth, while the circle embodies the cosmos,
symbolizing endlessness and infinitude (refer to Figure 2). The mandala itself is a square

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subdivided into minor squares, forming a grid that represents different areas dedicated torespective deities. A commonly employed mandala involves subdividing the square into 64 and81 squares [15]. Consequently, the Vastupurashamandala served as the foundational blueprint
for the ground floor plans of Hindu temples. The temple's basic shape was derived from thismandala, with the outermost ring of squares defining the thickness of the main shrine's walls.
The central squares were reserved for the most significant god, the inner loop of 12 squares
formed the garbhagriha's walls, and the remaining 16 to 28 squares created the "pradkshina
patha." These simple square divisions, through permutations and combinations, paved the way
for the creation of more intricate temple compounds.

Figure 2: The image of Vastupurushamandala with 64 blocks designated for diverse deities(Source: https://en.wikipedia.org/wiki/Hindu_temple)
7.

Fractal Characteristics in Temple Architecture:

The ancient Shani Mantra portrays the principles of the Sanatana Vedanta philosophy, as
outlined below:
This Purnam Shanti mantra (पूण शाि त मं ) is from ‘Isha Upanishad’ and is one of the foremost
principles of the Sanatana dharma.
ॐ पूणमदः पूणिमदं पूणा पुणमुद यतेपूण य पूणमादाय पूणमेवाविश यते ॥ॐ शाि तः शाि तः शाि तः ॥
The connotation of shloka is, “Supreme Being (Parmatma) is complete. It doesn’t depend on
any external agency for anything. Our Atman is a fragment of this infinite Supreme Being;
hence our Atman is also complete and has the same characteristics as those of Parmatma. Our
Atman is a replica of the Parmatma. The Supreme Being is infinite (अनंत). Hence, when a small
fragment is taken out of it, it still remains complete” [10].
In accordance with the ancient Indian Sankhya School of Philosophy, the cosmos is space-timeharmonic simulation [16]. This implies that the cosmos's structure reproduces itself in a selfsimilar manner at progressively smaller scales. Similar to a hologram, each piece of the universe
is self-contained, encapsulating all the information of the entire cosmos. The entire cosmos canbe envisioned within a microlevel cosmic entity referred to as Pinda-Brahmanda. This fractal

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architecture is evident in various parts of the temple, including the shikhara (both in plan andfront view), temple design, mandapa ceiling design, column design, and more (see Figure 3).

Figure 3: Fractal architecture of the shikhara. (Source: Trivedi K. Hindu temples: models ofa fractal universe [17])The image above was created using comparative elevations and plans of (a) the Shikhara of‘Adinatha’ temple (900 AD), (b) the Shikhara of ‘Paraswanatha’ temple (950 AD), and (c) the
Shikhara of ‘Kandariya Mahadev’ temple (1050 AD) [17]. With an escalation in geometriccomplexity, the count of self-similar designs concurrently rises, implicitly reflecting thepresence of fractal geometry.

Figure 4: Development of Garbh Griha in fractal sequence. (Source: Trivedi K. Hindu
temples: models of a fractal universe [17])The increasing intricacy of elements on both the interior and exterior walls of diverse
garbhagrihas is depicted in Figure 4. The inner wall showcases features such as (1) Bhadra,(2) Subhadra, and (3) Pratibhadra, while the exterior walls exhibit elements like (1) Arya,(2) Hastangula, (3) Bhagava, and (4) Samadala.

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Figure 5: Mandapa's fractal geometry in ascending order of fractal repetition. (Source:
Trivedi K.. Hindu temples: models of a fractal universe [17])Figure 5 illustrates the plans of eight kinds of Mandapas, as outlined in Shilpa texts: 1.
Vardharnan; 2. Swastika; 3. Garuda; 4. Suranandan; 5. Sarvatobhadra; 6. Kailash; 7.
Indranila; 8. Ratnasambhava. The external and internal wall geometry and elevation change,
becoming increasingly creative and complicated in geometry from stage 1 to stage 8 of themandapa geometry [18]. In some states, the design preserves symmetry concerning the x(horizontal) and y (vertical) axes.
Fractal architecture in temple design is a captivating exploration of fractal geometry principles,
introducing intricate and self-repeating patterns into the very fabric of temple structures. This
design concept, extending beyond mere aesthetics, weaves a tapestry of symbolic meaning into
various facets of temple architecture. Exhibiting self-similarity, where patterns repeat at
different scales within the structure, fractal architecture is discernible in the replication of
specific design elements or motifs throughout the entire temple. The incorporation of intricate
details at varying levels of magnification, facilitated by fractal geometry, manifests in temples
adorned with highly detailed carvings, patterns, and reliefs, creating visually rich environments[19]. Geometric patterns, such as fractal curves, recursive structures, and shapes, form thefoundation of this architectural approach, visible in the layout, pillars, and ornamentation of
temples. Mandala designs, rooted in geometric principles and characterized by radial symmetry,
represent a form of fractal architecture prevalent in temples, symbolizing the universe andembodying repetitive patterns.
Beyond aesthetic allure, the use of fractal geometry carries symbolic significance, portraying
cosmic order, interconnectedness, and spiritual concepts of infinity and eternity. Embracing
mathematical precision, fractal architecture demands meticulous planning, often employing
algorithms to ensure a harmonious and balanced temple structure [20]. Associated with sacredgeometry, fractal architecture aligns certain shapes like circles, squares, and triangles in a
repeated fractal manner, imbuing spiritual significance. The result is visual harmony, where therepetition of patterns and shapes contributes to a sense of order and coherence, enhancing theoverall aesthetic appeal [20]. This aesthetic complexity invites contemplation, encouraging
worshippers and visitors to delve into the depths of architectural details with a sense of wonder.
Culturally varied, fractal architecture in temples can take different forms across diverse culturesand historical periods, ranging from intricate complexity to simpler geometric patterns, eachladen with symbolic meanings [21]. In essence, fractal architecture in temple design unites
mathematical precision, symbolic depth, and visual richness to transcend mere functionality,
providing a profound and aesthetically pleasing experience for all who enter.

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8.

Physical human form and Temple Geometry

Conferring to ancient sacred scripture or agamas, the temple is considered a body. Therefore,
the construction of a temple must adhere to specific rules based on Tantra, Agama, and ShilpaŚastra. The ‘Kśetra’ (temple area) represents our ‘sthūla śarīra’ (gross body), the deity
symbolizes the ‘sūkshma śarīra’ (subtle body), and its ‘prāña’ represents our ‘kāraṇa śarīra’(causal body). The ‘prāña’ of the deity is considered analogous to ours. The temple structure,from feet to head, is comprised of six ‘ćakras’ (Figure 6). Each part of the temple, such as
Garbhagriham, Dhwajasthambham, etc., holds significance in Tantra Śastra.

Figure 6: Self-similarity is expressed in both the human body and temple geometry..(Source:
Sthapati G.Thirugnnanam[22])9.

Vinyasasutra (Layout & Orientation of Temple Complexes)

Hindu temple architecture is symmetrical and self-repeating, with its foundations in mythology,
cardinality, and mathematics. The Vastupurusamandala principles are followed in the layout of
the Manduka Hindu Temple floor plan, which is based on an 8x8 (64) grid. This 64-grid pattern
stands as the most sacred and widely used template for Hindu temples. At its center, marked bya vibrant saffron hue, the intersection of diagonals symbolizes the Purusha in Hindu philosophy.
In a Hindu temple, the axis is determined by the four cardinal directions, forming a perfectsquare within the available space. This square is considered divine, symbolizing perfection,knowledge, and human thought. Surrounding the square is the circular mandala, representing
earthly and human elements observed in everyday life. The relationship between the square andcircle is symbiotic. The square undergoes division into grids, as depicted in Figure 7, commonly
resulting in an 8x8 or 64-grid configuration within expansive temples. For ceremonial temple
superstructures, the prevalence of an 81-square grid is notable. These individual squares,
referred to as 'padas,' trace their origins back to the Vedic practices surrounding the fire altar,‘Agni’. The configuration along basic directions serves as an extension of Vedic rituals, with
Hindu temple manuals intricately detailing design plans that encompass square counts ranging
from 1 to 1024. The most basic plan, featuring a single pada, functions as a seat for meditation,

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yoga, or conducting Vedic fire rituals. The design incorporating 4 padas boasts a symbolic vitalcore, fostering a meditative ambiance. In the 9 pada configuration, a sacred-surrounded center
emerges, serving as the template for the smallest temple. While older temples may employ the
9 through 49 pada series, the 64-grid structure holds unparalleled sanctity in Hindu temple
architecture, referred to as Manduka, Bhekapada, or Ajira [23]. Respectively pada isconceptually linked to a representative element or deity, with the central squares of the 64-grid
specifically dedicated to the Brahman, known as Brahma padas. Ancient temple plans, like thatof Tanjavur, (figure-8) were meticulously divided into architectural elements to fulfill thesemathematical and symbolic calculations.

Figure 7: Distinctive Temple Plan (a)Shrine without porch (b)Shrine with front porch(c)Shrine with front Antarala and porch (d)Sarvatobhadra - shrine with entrances on all foursides (Source: Hardy, Adam. The Temple Architecture of India [24])Figure 8: Plan of Typical south indian Temple : Tanjavur (Source: Velmurugan C. andKalaivanan R., Existence of the golden ratio in Tanjavur Brihadeeshwarar temple [25] )10.

The Technological Advancements in the Construction of Ancient Hindu Temples

According to the references, the temple's construction materials comprised stone slabs, metal
plates, palm fronds, and timber parts. Dravidian and Nagara temple construction techniques
were substantially similar, culminating in temple construction [26]. Minor variances arose as aresult of factors such as the variety of construction materials, climate considerations, and theavailability of manual labor during the construction process.
The building of the temples began with the gathering of a knowledgeable group under the
direction of the chief architect, whose titles differed according to the location (sompuras in
the western region, mahapatras in the eastern region, and Sthpatis in the southern region).
The building team was divided into four different classes:

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Sthapati: The chief architect, well-versed in traditional knowledge, mathematics, andShilpa shastras.

ii.

Sutragrahin: Responsible for executing tasks allocated by the “Sthapati”.

iii.

Taksaka: extensively involved in the precise shaping and engraving of stones.

iv.

Vardhakin: The stonemason responsible for assembling all the individual components.

The sequence of temple construction is explained below;
1. Planning the Temple:
The construction of a temple, an extensive and lengthy process lasting years,
commenced with meticulous planning.



The initial phase involved the selection and inspection of the site, determining
the orientation and layout, and choosing materials. This process utilized theIndian Circle Method and an instrument known as "shanku yantra."

The orientation of the temple was significantly influenced by the nature of themain deity.



The chosen stone for construction had to exhibit specific qualities, including
even color, hardness, and a pleasing tactile feel.

2. Carving the Temple Parts:
The subsequent phase focused on the intricate carving of different temple
components. The takshaka guided sculptors and shilpis in carving according to
specified drawings.



Stone cutting and carving adhered to predetermined shapes, and rough joinery
was established during the cutting process.



Locally crafted tools such as hammers and chisels were regularly sharpened foruse.



Sketching was executed using charcoal or sharpened bamboo, and the polishingprocess involved the use of stone bars.

3. Assembling the Temple:

55



The final stage encompassed the assembly of temple parts, marking the actual
construction phase.

Mortise and tenon joints, a key joinery system involving a peg secure between
two mortises in diverse stones, were prominently used. This method prevented
movement due to lateral forces and was particularly employed between two
courses of masonry (Figure 9). Lap joints were also utilized during assembly.

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Figure 9: Stone joined by iron pegs fixed as “X” joint. (Source: Parmar S. P., Insights ofModhera Sun Temple [27])

Stone Wall Thickness: The typical thickness of stones employed for constructing walls
ranged between 800 mm to 1200 mm.

Column Structure: The five integral pieces of a column were two segments that madeup the base of the column, one segment that made up the shaft of the column, and two
portions that made up the capital.

Monolithic Construction: Both columns and beams were constructed as monolithic
structures, ensuring a seamless and cohesive architectural integrity.

Hindu temples were established across a myriad of locations, each characterized by its distinct
geography, topography, and meteorological conditions. As a result of this diversity, several
building styles and materials were incorporated. Here are some instances of these many temple
locations:Temples in Varied Locations: Mountain Temples: Notable examples include Mansa Devi and Vaishno Devi.
Cave Temples: Examples encompass Chandrabhaga and the Chalukya Ellora Caves.



Step Well Temples: Prominent instances are Mata Bhavani stepwell and Khodiyar Mata
stepwell, both situated in Ahmedabad, Gujarat.



Temples inside forests: Locations like “Kasaun” and “Kusama” are representative of
temples in forested areas.

River Bank and Seashore Temples: Iconic temples situated along riverbanks andseashores include Jagannath Puri, Somnath, Kashi, Prayag (Allahabad), Hardwar, andRameswaram, among others.

11.

Art and Cultural representation in Temples

Art in Ancient Hindu Temples:56



Diverse Art Forms: Ancient Hindu temples boast a rich array of artistic expressions,encompassing paintings, sculptures, symbolic icons, engravings, and a well-thought-out
spatial layout that integrates mathematical ideologies with the Hindu conceptualizationof time and cardinal points.

Categorization of Idols and Images: According to earliest Sanskrit scripts, idols andimages are classified in various forms. One method involves the dimensionality ofcompletion:

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

Chitra: Referring to fully formed, three-dimensional images.



Chitrardha: Describing pictures inscribed in partial relief.



Chitrabhasa: Representing 2D pictures, such as portraits on walls and fabric.

Discussion

Temple architecture serves as a testament to the profound advancements achieved in the realmof ancient Indian building sciences. This paper intricately explores a spectrum of facets
including styles, design geometry, philosophical underpinnings, construction technology, andthe intriguing concept of self-similarity between the human body and temple elements [28]. The
geometric intricacies of Indian temples, including the extent of work and the area they
encompass, are contingent upon multifarious factors such as location, budget constraints, andthe availability of specific building stones for construction. Across temporal and geographicaldimensions, the geometry of temples undergoes dynamic transformations, adapting to theavailable materials and evolving design paradigms. The prevalent use of fractal architecturetranscends various styles and regions of temple construction, persisting as a dominant practiceeven in contemporary times. The meticulous stone artwork adorning every facet of the temples
appears to be meticulously crafted with the purpose of preserving and transmitting information.
The shikhara's fractal geometry imparts an ambient aesthetic, achieving an exquisite
equilibrium between mass and symmetry. Vertical offsets, increasing in a zigzag fashion witheach side recess, contribute to a discernible vertical rhythm, while horizontal abutments andcarvings enhance the structural stability of the temple The temple's geometric configuration is
not merely an architectural feat; rather, it symbolically mirrors the human body, aligning withthe sequential awakening of the seven chakras of Kundalini Shakti from feet to head. The
thoughtful design, not only aesthetically pleasing but also resilient, was orchestrated to endure
moderate seismic activities, reinforcing the temples as not just structures but profound symbolic
representations of the human spiritual journey.
13.

Concluding Remarks










57

Temple architecture signifies profound advancements in ancient Indian construction
technology.
The paper explores elegances, geometrical design and styles, philosophicalunderpinnings, construction technology, and the concept of self-similarity with thehuman body.
Geometric intricacies depend on factors like location of site, budget, and available
building stones.
Temple geometry evolves across time and geography, adapting to materials and designchanges.
Fractal architecture is a prevailing practice in contemporary temple construction.
Stone artwork is meticulously crafted for the preservation and transmission of
information.
The shikhara's fractal geometry achieves an aesthetic balance between mass andsymmetry.
Vertical offsets in a zigzag fashion and horizontal abutments enhance structural
stability.
The temple's geometric configuration symbolically mirrors the human body and theawakening of chakras.

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

The thoughtful design is both aesthetically pleasing and resilient, crafted to endure
seismic activities, representing a profound spiritual journey.

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Journal of Engineering, Science & Education © 2024, NITTTR Kolkata

Journal of Engineering, Science & Education, Vol. 1, Issue 1, 2024

CONTENTS
Sl. No.

Topic

Page

1.

Exploring Kidney Cancer using Transcriptomic Data
Indrajit Saha, Nimisha Ghosh and Somnath Rakshit

1

2.

Optimization of density and hardness of graphite-copper
composites by using Taguchi method
Jhulan Kumar and Subrata Mondal

93.

Enhanced Deep Learning U-Net Model for Chest X-ray
Image Segmentation
Asifuzzaman Lasker, Mridul Ghosh, Sk Md Obaidullah,
Teresa Goncalves, Kaushik Roy and Chandan
Chakraborty
Remarkable Contributions by Jaina Mathematicians inAncient India
Kinsuk Giri and Debi Prasad Mishra

18

5.

The Alchemy of Wootz Steel Production in Ancient India
N. AnanthaKrishna and Debi Prasad Mishra

32

6.

Architectural Elegance Through Time: Unveiling the Unique
Construction Legacy of Hindu Temples in Ancient India
Samirsinh P Parmar and Debi Prasad Mishra

43

4.

Published by
National Institute of Technical Teachers’
Training and Research, Kolkata(Ministry of Education, Government of India)
Block-FC, Sector-III, Salt Lake City, Kolkata-700106

Journal of Engineering, Science & Education © 2024, NITTTR Kolkata

26