Subhash Chandra Panja

493 total citations
43 papers, 309 citations indexed

About

Subhash Chandra Panja is a scholar working on Astronomy and Astrophysics, Mechanical Engineering and Statistics, Probability and Uncertainty. According to data from OpenAlex, Subhash Chandra Panja has authored 43 papers receiving a total of 309 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Astronomy and Astrophysics, 12 papers in Mechanical Engineering and 9 papers in Statistics, Probability and Uncertainty. Recurrent topics in Subhash Chandra Panja's work include Solar and Space Plasma Dynamics (13 papers), Solar Radiation and Photovoltaics (7 papers) and Advanced Machining and Optimization Techniques (5 papers). Subhash Chandra Panja is often cited by papers focused on Solar and Space Plasma Dynamics (13 papers), Solar Radiation and Photovoltaics (7 papers) and Advanced Machining and Optimization Techniques (5 papers). Subhash Chandra Panja collaborates with scholars based in India, Nepal and Sweden. Subhash Chandra Panja's co-authors include Soumya Roy, Amrita Prasad, Dibyendu Das, Koushik Ghosh, Indrajit Mukherjee, Arindam Sarkar, Pradip Kumar Ray, Purushottam Sharma, Bijan Sarkar and Atri Sengupta and has published in prestigious journals such as Colloids and Surfaces A Physicochemical and Engineering Aspects, The International Journal of Advanced Manufacturing Technology and Solar Physics.

In The Last Decade

Subhash Chandra Panja

38 papers receiving 290 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Subhash Chandra Panja India 10 86 61 45 44 38 43 309
Annalisa Weigel United States 14 150 1.7× 12 0.2× 55 1.2× 39 0.9× 128 3.4× 53 526
Ivica Pavić Croatia 11 38 0.4× 26 0.4× 6 0.1× 29 0.7× 21 0.6× 59 449
Nikolaos S. Thomaidis Greece 9 54 0.6× 33 0.5× 66 1.5× 18 0.4× 13 0.3× 34 449
Ju Hong China 13 103 1.2× 17 0.3× 83 1.8× 5 0.1× 7 0.2× 58 555
Andreas M. Hein France 11 104 1.2× 92 1.5× 13 0.3× 14 0.3× 19 0.5× 68 451
Eduardo C. Garrido‐Merchán Spain 10 6 0.1× 156 2.6× 54 1.2× 31 0.7× 11 0.3× 21 460
Jianliang Zhou China 13 7 0.1× 17 0.3× 68 1.5× 11 0.3× 47 1.2× 43 400
Alejandro Escudero-Santana Spain 11 20 0.2× 14 0.2× 11 0.2× 10 0.2× 17 0.4× 47 326
K. Ishii United States 11 6 0.1× 43 0.7× 32 0.7× 121 2.8× 19 0.5× 25 413
David Brčić Croatia 13 18 0.2× 19 0.3× 4 0.1× 25 0.6× 5 0.1× 66 436

Countries citing papers authored by Subhash Chandra Panja

Since Specialization
Citations

This map shows the geographic impact of Subhash Chandra Panja's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Subhash Chandra Panja with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Subhash Chandra Panja more than expected).

Fields of papers citing papers by Subhash Chandra Panja

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Subhash Chandra Panja. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Subhash Chandra Panja. The network helps show where Subhash Chandra Panja may publish in the future.

Co-authorship network of co-authors of Subhash Chandra Panja

This figure shows the co-authorship network connecting the top 25 collaborators of Subhash Chandra Panja. A scholar is included among the top collaborators of Subhash Chandra Panja based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Subhash Chandra Panja. Subhash Chandra Panja is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
2.
Panja, Subhash Chandra, et al.. (2025). Synergistic integration of FFF and electroless plating towards a simplistic selective metallization process for electronic applications. Journal of Manufacturing Processes. 151. 576–594. 1 indexed citations
3.
Panja, Subhash Chandra, Somnath Mondal, Debarati Jana, et al.. (2025). Methanolic extract of Acorus calamus rhizome loaded nanostructured lipid carriers: Characterization and in vivo anti-allergic activity studies. Colloids and Surfaces A Physicochemical and Engineering Aspects. 725. 137580–137580.
4.
Naikan, V.N.A., et al.. (2024). Reliability analysis of cutting tools using transformed inverse Gaussian process-based wear modelling considering parameter dependence. Probabilistic Engineering Mechanics. 78. 103698–103698. 1 indexed citations
5.
Panja, Subhash Chandra, et al.. (2024). Analysis of mesostructural characteristics and their influence on tensile strength of ABS specimens manufactured through fused deposition modeling. The International Journal of Advanced Manufacturing Technology. 132(1-2). 349–363. 4 indexed citations
6.
Prasad, Amrita, et al.. (2023). An Improved Prediction of Solar Cycle 25 Using Deep Learning Based Neural Network. Solar Physics. 298(3). 9 indexed citations
7.
Roy, Soumya, et al.. (2021). Temporal variation of solar flare index during solar cycles 21 − 24. Research in Astronomy and Astrophysics. 21(3). 53–53. 2 indexed citations
8.
Roy, Soumya, et al.. (2020). Chaos and Periodicities in Solar Flare Index from Kandilli Observatory during 1976–2014. Research in Astronomy and Astrophysics. 20(7). 110–110. 6 indexed citations
9.
Roy, Soumya, et al.. (2020). An embedded system to measure ground-based solar irradiance signal. Measurement. 173. 108598–108598. 5 indexed citations
10.
Mukherjee, Indrajit, et al.. (2020). A synergistic Mahalanobis–Taguchi system and support vector regression based predictive multivariate manufacturing process quality control approach. Journal of Manufacturing Systems. 57. 323–337. 17 indexed citations
11.
Mukherjee, Indrajit, et al.. (2019). A synergic multivariate statistical process control framework for monitoring, diagnosis, and adjustment of multiple response abrasive machining processes. International Journal of Industrial and Systems Engineering. 33(3). 314–314. 2 indexed citations
12.
Prasad, Amrita, et al.. (2019). A Search for Periodicities in F10.7 Solar Radio Flux Data. 53(3). 240–240. 1 indexed citations
13.
Roy, Soumya, et al.. (2018). Scaling Analysis of the Flare Index Data from Kandilli Observatory. Proceedings of the International Astronomical Union. 13(S340). 161–162. 2 indexed citations
14.
Mukherjee, Indrajit, et al.. (2018). A CASE STUDY ON IMPLEMENTATION OF MAHALANOBIS-TAGUCHI SYSTEM FOR MULTIVARIATE MANUFACTURING PROCESS CONTROL. 43(1). 61–71. 1 indexed citations
15.
Panja, Subhash Chandra, et al.. (2015). Fault tree analysis of Rukhia gas turbine power plant. HKIE Transactions. 22(1). 32–56. 5 indexed citations
16.
17.
Bhattacharya, Gautam, et al.. (2013). A low free-parameter stochastic model of daily Forbush decrease indices. Journal of Atmospheric and Solar-Terrestrial Physics. 107. 30–35. 1 indexed citations
18.
Panja, Subhash Chandra, et al.. (2011). Expert-based FMEA of wind turbine system. SUNScholar (Stellenbosch University). 1582–1585. 10 indexed citations
19.
Panja, Subhash Chandra, et al.. (2011). Survey of maintenance policies for the Last 50 Years. International Journal of Software Engineering & Applications. 2(3). 130–148. 22 indexed citations
20.
Panja, Subhash Chandra & Pradip Kumar Ray. (2009). Failure Mode and Effect Analysis of Indian Railway Signalling System. International Journal of Performability Engineering. 5(2). 131. 1 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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