Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
This map shows the geographic impact of R.E. Uhrig'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 R.E. Uhrig with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites R.E. Uhrig more than expected).
This network shows the impact of papers produced by R.E. Uhrig. 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 R.E. Uhrig. The network helps show where R.E. Uhrig may publish in the future.
Co-authorship network of co-authors of R.E. Uhrig
This figure shows the co-authorship network connecting the top 25 collaborators of R.E. Uhrig.
A scholar is included among the top collaborators of R.E. Uhrig 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 R.E. Uhrig. R.E. Uhrig 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.
Uhrig, R.E., et al.. (2008). Multi-Intelligent Agents: Potential Applications to Next Generation Nuclear Plants. Transactions American Geophysical Union. 98(1). 87–88.1 indexed citations
2.
Uhrig, R.E. & J. Wesley Hines. (2005). COMPUTATIONAL INTELLIGENCE IN NUCLEAR ENGINEERING. Nuclear Engineering and Technology. 37(2). 127–138.21 indexed citations
3.
Buckner, Mark A. & R.E. Uhrig. (2003). Learning from data with localized regression and differential evolution. PhDT.9 indexed citations
Hines, J. Wesley, et al.. (1997). Evaluation of instrument calibration monitoring using artificial neural networks. Transactions of the American Nuclear Society. 77.5 indexed citations
Miller, Laurence F., et al.. (1992). An application of neural networks and artificial intelligence for in-core fuel management. Transactions of the American Nuclear Society. 66(11). 1889–90.6 indexed citations
9.
Ikonomopoulos, A., Lefteri H. Tsoukalas, & R.E. Uhrig. (1992). Use of neural networks to monitor power plant components.7 indexed citations
10.
Ikonomopoulos, A., R.E. Uhrig, & Lefteri H. Tsoukalas. (1992). A methodology for performing virtual measurements in a nuclear reactor system. Transactions of the American Nuclear Society. 66.2 indexed citations
11.
Tsoukalas, Lefteri H., A. Ikonomopoulos, & R.E. Uhrig. (1992). Virtual measurements using neural networks and fuzzy logic.1 indexed citations
12.
Uhrig, R.E.. (1991). Potential application of neural networks to the operation of nuclear power plants. 32(1).33 indexed citations
13.
Uhrig, R.E., et al.. (1991). Vibration monitoring with artificial neural networks. STIN. 93. 21992.7 indexed citations
14.
Ikonomopoulos, A., et al.. (1991). Monitoring nuclear reactor systems using neural networks and fuzzy logic. University of North Texas Digital Library (University of North Texas).5 indexed citations
Uhrig, R.E., et al.. (1989). Use of neural networks for in-core fuel management. Transactions of the American Nuclear Society. 59.1 indexed citations
17.
Uhrig, R.E., et al.. (1988). Development of an expert system for signal validation. Transactions of the American Nuclear Society. 57.1 indexed citations
18.
Uhrig, R.E.. (1966). A Review by Field Test. Nuclear Applications. 2(3). 264–265.1 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.