D. P. Mahapatra

1.4k total citations
10 papers, 81 citations indexed

About

D. P. Mahapatra is a scholar working on Modeling and Simulation, Atomic and Molecular Physics, and Optics and Infectious Diseases. According to data from OpenAlex, D. P. Mahapatra has authored 10 papers receiving a total of 81 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Modeling and Simulation, 4 papers in Atomic and Molecular Physics, and Optics and 3 papers in Infectious Diseases. Recurrent topics in D. P. Mahapatra's work include COVID-19 epidemiological studies (5 papers), SARS-CoV-2 and COVID-19 Research (3 papers) and COVID-19 Pandemic Impacts (2 papers). D. P. Mahapatra is often cited by papers focused on COVID-19 epidemiological studies (5 papers), SARS-CoV-2 and COVID-19 Research (3 papers) and COVID-19 Pandemic Impacts (2 papers). D. P. Mahapatra collaborates with scholars based in India, South Africa and United States. D. P. Mahapatra's co-authors include S. Triambak, Neelkamal Mallick, R. Sahoo, A. Chutjian, J. R. Machacek, Yuri Ralchenko, David Schultz, Richard Mawhorter, J. Simčič and M. O. A. El Ghazaly and has published in prestigious journals such as The Astrophysical Journal, Physical Review A and Physica A Statistical Mechanics and its Applications.

In The Last Decade

D. P. Mahapatra

8 papers receiving 79 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
D. P. Mahapatra India	5	33	24	16	13	10	10	81
M. Mezzetto Italy	11	10 0.3×	23 1.0×	5 0.3×	3 0.2×	24 2.4×	32	347
James Turtle United States	4	20 0.6×	26 1.1×	5 0.3×	8 0.6×		13	68
S. Maltezos Greece	4	12 0.4×	12 0.5×	6 0.4×	2 0.2×	11 1.1×	26	59
Vanessa Böhm United States	6	26 0.8×	11 0.5×	13 0.8×	17 1.3×		9	192
J. E. Midtbø Norway	7	14 0.4×	27 1.1×	3 0.2×	9 0.7×	49 4.9×	15	118
J. Dudouet France	7	35 1.1×	17 0.7×	10 0.6×	17 1.3×	84 8.4×	14	164
R. Spighi Italy	4	10 0.3×	4 0.2×	5 0.3×	3 0.2×	14 1.4×	12	69
D. Menasce Italy	7	10 0.3×	4 0.2×	5 0.3×	3 0.2×	29 2.9×	22	130
S. Simionatto Brazil	5	11 0.3×	16 0.7×	13 0.8×	2 0.2×	36 3.6×	11	69
T. R. Pollmann Germany	5	9 0.3×	30 1.3×	1 0.1×	3 0.2×	23 2.3×	9	87

Countries citing papers authored by D. P. Mahapatra

Since Specialization

Citations

This map shows the geographic impact of D. P. Mahapatra'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 D. P. Mahapatra with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites D. P. Mahapatra more than expected).

Fields of papers citing papers by D. P. Mahapatra

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by D. P. Mahapatra. 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 D. P. Mahapatra. The network helps show where D. P. Mahapatra may publish in the future.

Co-authorship network of co-authors of D. P. Mahapatra

This figure shows the co-authorship network connecting the top 25 collaborators of D. P. Mahapatra. A scholar is included among the top collaborators of D. P. Mahapatra 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 D. P. Mahapatra. D. P. Mahapatra is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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10 of 10 papers shown

Triambak, S., et al.. (2023). Plausible explanation for the third COVID-19 wave in India and its implications. Infectious Disease Modelling. 8(1). 183–191. 2 indexed citations

Mahapatra, D. P. & S. Triambak. (2022). Towards predicting COVID-19 infection waves: A random-walk Monte Carlo simulation approach. Chaos Solitons & Fractals. 156. 111785–111785. 12 indexed citations

Triambak, S., et al.. (2022). Plausible Explanation for the Third COVID-19 Wave in India and Its Implications. SSRN Electronic Journal.

Triambak, S. & D. P. Mahapatra. (2021). A random walk Monte Carlo simulation study of COVID-19-like infection spread. Physica A Statistical Mechanics and its Applications. 574. 126014–126014. 21 indexed citations

Triambak, S., D. P. Mahapatra, Neelkamal Mallick, & R. Sahoo. (2021). A new logistic growth model applied to COVID-19 fatality data. Epidemics. 37. 100515–100515. 18 indexed citations

Khuntia, A., Gayatri Sahu, R. Sahoo, D. P. Mahapatra, & N. Barik. (2019). Non-extensive statistics in free-electron metals and thermal effective mass. Physica A Statistical Mechanics and its Applications. 523. 852–857. 1 indexed citations

Chutjian, A., et al.. (2015). Electrostatic Storage Ring for Physics and Space Studies. Journal of Physics Conference Series. 635(3). 32099–32099.

Machacek, J. R., D. P. Mahapatra, David Schultz, et al.. (2015). SOLAR-WIND ION-DRIVEN X-RAY EMISSION FROM COMETARY AND PLANETARY ATMOSPHERES: MEASUREMENTS AND THEORETICAL PREDICTIONS OF CHARGE-EXCHANGE CROSS-SECTIONS AND EMISSION SPECTRA FOR O⁶⁺+ H₂O, CO, CO₂, CH₄, N₂, NO, N₂O, AND Ar. The Astrophysical Journal. 809(1). 75–75. 10 indexed citations

Mahapatra, D. P., et al.. (2014). SIZE DISTRIBUTION OF POSSIBLE DUST CARRIERS FOR THE EXTENDED RED EMISSION. The Astrophysical Journal. 790(2). 140–140. 3 indexed citations

10.

Machacek, J. R., D. P. Mahapatra, David Schultz, et al.. (2014). Measurement and calculation of absolute single- and double-charge-exchange cross sections forO6+ions at 1.17 and 2.33 keV/u impacting He andH2. Physical Review A. 90(5). 14 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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