{"id":347,"date":"2022-06-30T20:32:37","date_gmt":"2022-06-30T20:32:37","guid":{"rendered":"https:\/\/cse.unl.edu\/~rquinones\/?page_id=347"},"modified":"2026-01-30T12:42:54","modified_gmt":"2026-01-30T18:42:54","slug":"publications","status":"publish","type":"page","link":"https:\/\/cs.siue.edu\/~rquinon\/publications\/","title":{"rendered":"RESEARCH"},"content":{"rendered":"\n<h3><strong>Peer Reviewed Articles<\/strong><\/h3>\n\n\n\n<ul>\n<li><strong>Qui\u00f1ones R<\/strong>, Mu\u00f1oz-Arriola F, Das Choudhury S, Samal A. Introducing Concurrent Imaging and Unidimensional Analytics for Plant Stress Responses. Plants. 2026; 15(3):428. <a href=\"https:\/\/doi.org\/10.3390\/plants15030428\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/doi.org\/10.3390\/plants15030428<\/a><\/li>\n\n\n\n<li>Hirata Sanches, V.,\u00a0<strong>Qui\u00f1ones, R<\/strong>., Vivas, J., Guillaume, J. H., Iwanaga, T., Kwakkel, J. H., &#8230; &amp; Lade, S. J. (2025). Integrating diversity and agency into social-ecological resilience metrics. Ecology and society, 30(4).<\/li>\n\n\n\n<li><strong>Qui\u00f1ones, R<\/strong>., Banu, S. M., &amp; Gultepe, E. (2025). GCNet: A Deep Learning Framework for Enhanced Grape Cluster Segmentation and Yield Estimation Incorporating Occluded Grape Detection with a Correction Factor for Indoor Experimentation.\u00a0<em>Journal of Imaging<\/em>,\u00a0<em>11<\/em>(2), 34.\u00a0<a href=\"https:\/\/doi.org\/10.3390\/jimaging11020034\">https:\/\/doi.org\/10.3390\/jimaging11020034<\/a><\/li>\n\n\n\n<li><strong>Qui\u00f1ones, Rubi.&nbsp;<\/strong>&#8220;OSC-CO^ 2: Coattention and Cosegmentation Framework for Plant State Change with Multiple Features.&#8221; Frontiers in Plant Science 14: 1211409.<strong>&nbsp;<\/strong><a href=\"https:\/\/doi.org\/10.3389\/fpls.2023.1211409\">https:\/\/doi.org\/10.3389\/fpls.2023.1211409<\/a><\/li>\n\n\n\n<li>Benjamin Wingerter,&nbsp;<strong>Rubi Qui\u00f1ones<\/strong>, and Dominic Cristiano. August 2023. DA<sup>3<\/sup>-BES: Exploring Complex Adaptive Systems Using Dynamic Multi-Agent Models for Honey Bee Colony Environment Simulation. Teaching Issues and Experiments in Ecology, Vol. 19: Experiment #1.&nbsp;<a href=\"https:\/\/tiee.esa.org\/vol\/v19\/experiments\/wingerter\/abstract.html\">https:\/\/tiee.esa.org\/vol\/v19\/experiments\/wingerter\/abstract.html<\/a><\/li>\n\n\n\n<li>Cox, M., Harrison, H., Partelow, S., Curtis, S., Elser, S., Hammond Wagner, C., \u2026&nbsp;<strong>R. Qui\u00f1ones&nbsp;<\/strong>&amp; Whittaker, B. (2023). How academic podcasting can change academia and its relationship with society: A conversation and guide.&nbsp;<em>Frontiers in Communication<\/em>,&nbsp;<em>8<\/em>.<\/li>\n\n\n\n<li>F. E. Hogan, J. A. Fowler, C. D. Barnes, A. K. Ludwig , D. J. Cristiano, K., D. Morales,&nbsp;<strong>R. Qui\u00f1ones<\/strong>, D. Twidwell, J. M. Dauer. \u201cNew multimedia resources for ecological resilience education in modern university classrooms.\u201d Eco-Sphere: Eco-Education Track. October 2022.&nbsp;<a href=\"http:\/\/doi.org\/10.1002\/ecs2.4245\">http:\/\/doi.org\/10.1002\/ecs2.4245<\/a> <\/li>\n\n\n\n<li><strong>Qui\u00f1ones R<\/strong>, Munoz-Arriola F, Choudhury SD, Samal A (2021) Multi-feature data repository development and analytics for image cosegmentation in high-throughput plant phenotyping. PLoS ONE 16(9): e0257001. <a href=\"https:\/\/doi.org\/10.1371\/journal.pone.0257001\">https:\/\/doi.org\/10.1371\/journal.pone.0257001<\/a><\/li>\n\n\n\n<li>S. Gampa, <strong>R. Qui\u00f1ones<\/strong>. \u201cData Driven Techniques for Hyperspectral Image Analysis,\u201d CRC Press, Taylor and Francis, 2020. ISBN: 9781315177304<\/li>\n\n\n\n<li>Firestone, J.W., <strong>Qui\u00f1ones, R.<\/strong>, &amp; Duncan, B.A. (2019). Learning from Users: An Elicitation Study and Taxonomy for Communicating small Unmanned Aerial System States Through Gestures. In HRI 2019 \u2013 14th ACM\/IEEE International Conference on Human-Robot Interaction (pp. 163-171). [8673010] (ACM\/IEEE International Conference on Human-Robot Interaction; Vol. 2019-March). IEEE Computer society. <a href=\"https:\/\/doi.org\/10.1109\/HR.2019.8673010\">https:\/\/doi.org\/10.1109\/HR.2019.8673010<\/a><\/li>\n<\/ul>\n\n\n\n<h3><strong>Datasets<\/strong><\/h3>\n\n\n\n<ul>\n<li><strong>Qui\u00f1ones, R.<\/strong>, Munoz-Arriola, F., Das Choudhury, S., &amp; Samal, A. (2025). Unidimensional Phenotypes using Concurrent Imaging Collected Via a High-Throughput Imaging System [Data set]. Zenodo. <a href=\"https:\/\/doi.org\/10.5281\/zenodo.17400167\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/doi.org\/10.5281\/zenodo.17400167<\/a>\n<ul>\n<li>Please cite: <strong>Qui\u00f1ones R<\/strong>, Mu\u00f1oz-Arriola F, Das Choudhury S, Samal A. Introducing Concurrent Imaging and Unidimensional Analytics for Plant Stress Responses. Plants. 2026; 15(3):428.\u00a0<a href=\"https:\/\/doi.org\/10.3390\/plants15030428\">https:\/\/doi.org\/10.3390\/plants15030428<\/a><\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Qui\u00f1ones, R.<\/strong>, Samal, A., Das Choudhury, S., &amp; Munoz-Arriola, F. (2022). Cosegmentation for Plant Phenotyping+ (CosegPP+) Data Repository Collected Via a High-Throughput Imaging System [Data set]. Zenodo.\u00a0<a href=\"https:\/\/doi.org\/10.5281\/zenodo.6863013\">https:\/\/doi.org\/10.5281\/zenodo.6863013<\/a>\n<ul>\n<li>Please cite: <strong>Qui\u00f1ones, Rubi.&nbsp;<\/strong>&#8220;OSC-CO^ 2: Coattention and Cosegmentation Framework for Plant State Change with Multiple Features.&#8221; Frontiers in Plant Science 14: 1211409.<strong>&nbsp;<\/strong><a href=\"https:\/\/doi.org\/10.3389\/fpls.2023.1211409\">https:\/\/doi.org\/10.3389\/fpls.2023.1211409<\/a><\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Qui\u00f1ones, Rubi<\/strong>, Munoz Arriola, Francisco, Das Choudhury, Sruti, &amp; Samal. Ashok. (2021). Cosegmentation for Plant Phenotyping (CosegPP) Data Repository Collected Via a High-Throughput Imaging System [Data set]. In Multi-feature data repository development and analytics for image cosegmentation in high-throughput plant phenotyping. Zenodo. <a rel=\"noreferrer noopener\" href=\"https:\/\/doi.org\/10.5281\/zenodo.5117176\" target=\"_blank\">https:\/\/doi.org\/10.5281\/zenodo.5117176<\/a>\n<ul>\n<li>Please cite: <strong>Qui\u00f1ones R<\/strong>, Munoz-Arriola F, Choudhury SD, Samal A (2021) Multi-feature data repository development and analytics for image cosegmentation in high-throughput plant phenotyping. PLoS ONE 16(9): e0257001.<a href=\" https:\/\/doi.org\/10.1371\/journal.pone.0257001\"> https:\/\/doi.org\/10.1371\/journal.pone.0257001<\/a><\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\n<h3><strong>Software<\/strong><\/h3>\n\n\n\n<ul>\n<li>DA<sup>3<\/sup>-BES Bee Simulation Model:&nbsp;<a href=\"https:\/\/github.com\/benwingerter\/bee-simulation\/releases\">https:\/\/github.com\/benwingerter\/bee-simulation\/releases<\/a>\n<ul>\n<li>Please cite: Benjamin Wingerter,&nbsp;<strong>Rubi Qui\u00f1ones<\/strong>, and Dominic Cristiano. August 2023. DA<sup>3<\/sup>-BES: Exploring Complex Adaptive Systems Using Dynamic Multi-Agent Models for Honey Bee Colony Environment Simulation. Teaching Issues and Experiments in Ecology, Vol. 19: Experiment #1.&nbsp;<a href=\"https:\/\/tiee.esa.org\/vol\/v19\/experiments\/wingerter\/abstract.html\">https:\/\/tiee.esa.org\/vol\/v19\/experiments\/wingerter\/abstract.html<\/a><\/li>\n<\/ul>\n<\/li>\n\n\n\n<li>OSC-CO^2: <a rel=\"noreferrer noopener\" href=\"https:\/\/github.com\/rubiquinones\/OSC-CO2\" target=\"_blank\">https:\/\/github.com\/rubiquinones\/OSC-CO2<\/a>\n<ul>\n<li>Please cite: <strong>Qui\u00f1ones, Rubi.&nbsp;<\/strong>&#8220;OSC-CO^2: Coattention and Cosegmentation Framework for Plant State Change with Multiple Features.&#8221; Frontiers in Plant Science 14: 1211409.<strong>&nbsp;<\/strong><a href=\"https:\/\/doi.org\/10.3389\/fpls.2023.1211409\">https:\/\/doi.org\/10.3389\/fpls.2023.1211409<\/a><\/li>\n<\/ul>\n<\/li>\n\n\n\n<li>HyperRPheno: <a href=\"https:\/\/plantvision.unl.edu\/software\">https:\/\/plantvision.unl.edu\/software<\/a>\n<ul>\n<li>Please cite: S. Gampa, <strong>R, Qui\u00f1ones<\/strong>, &#8220;Data-Driven Techniques forPlant Phenotyping Using Hyperspectral Imagery&#8221;, Intelligent Image Analysis for Plant Phenotyping, CRC Press, Taylor and Francis Group, First Edition, 2020.<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\n<h3><strong>Grants<\/strong><\/h3>\n\n\n\n<ul>\n<li><strong>$40,000<\/strong> &#8211; NIH STRR Grant for &#8220;Customized Infant Breastfeeding Accessories for Breastfeeding Success&#8221;<\/li>\n\n\n\n<li><strong>$16,000<\/strong> &#8211; SIUE\u2019 Seed Grants for Transitional and Exploratory Projects (STEP) Grant for &#8220;A Multi-Agent Simulation to Understand the Environmental Influence on Plant Growth&#8221;<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>Peer Reviewed Articles Datasets Software Grants<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":[],"_links":{"self":[{"href":"https:\/\/cs.siue.edu\/~rquinon\/wp-json\/wp\/v2\/pages\/347"}],"collection":[{"href":"https:\/\/cs.siue.edu\/~rquinon\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/cs.siue.edu\/~rquinon\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/cs.siue.edu\/~rquinon\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/cs.siue.edu\/~rquinon\/wp-json\/wp\/v2\/comments?post=347"}],"version-history":[{"count":13,"href":"https:\/\/cs.siue.edu\/~rquinon\/wp-json\/wp\/v2\/pages\/347\/revisions"}],"predecessor-version":[{"id":539,"href":"https:\/\/cs.siue.edu\/~rquinon\/wp-json\/wp\/v2\/pages\/347\/revisions\/539"}],"wp:attachment":[{"href":"https:\/\/cs.siue.edu\/~rquinon\/wp-json\/wp\/v2\/media?parent=347"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}