Computational and Data Science, Ph.D.

Assistantships

Research and teaching assistantships, with stipends beginning at $20,100, are available on a competitive basis to full-time students in the COMS program. In addition to the stipend, the university also pays all tuition and most fees for assistantship holders. Non-Tennessee residents who are awarded a graduate assistantship are not required to pay out-of-state fees. To learn more about the types of graduate assistantships and to download an application, visit the Graduate Studies Assistantship page.

The College of Graduate Studies also awards a limited number of scholarships. For additional information and applications, visit the Graduate Studies Finance page.

In addition to assistantships and scholarships, MTSU’s Office of Financial Aid assists graduate students seeking other forms of financial support while in school.

Student Forms

• COMS Student Handbook
• COMS Degree Plan (.docx)
• COMS Degree Plan (.pdf)
• COMS Qualifying Exam Proposal
• COMS Qualifying Exam Results
• MTSU Graduate Student Forms
• Travel and Reimbursement Forms
• 25Live for Scheduling Rooms

Research in Computational and Data Science

Computation is now regarded as an equal and indispensable partner, along with theory and experiment, in the advance of scientific knowledge. Numerical simulation enables the study of complex systems and natural phenomena that would be too expensive or dangerous, or even impossible, to study by direct experimentation. The quest for increasing levels of detail and realism in such simulations requires enormous computational capacity, and has provided the impetus for dramatic breakthroughs in computer algorithms and architectures. Due to these advances, computational scientists can now solve large-scale problems that were once thought intractable.

Computational Science is in a rapidly growing multidisciplinary area with connections to the sciences, mathematics, and computer science. The program focuses on the development of problem-solving methodologies and robust tools for the solution of scientific problems.

The Computational and Data Science (COMS) program is a broad multidisciplinary area that encompasses applications in science, applied mathematics, numerical analysis, and computer science. Computer models and computer simulations have become an important part of the research repertoire, supplementing (and in some cases replacing) experimentation. Going from application area to computational results requires domain expertise, mathematical modeling, numerical analysis, algorithm development, software implementation, program execution, analysis, validation, and visualization of results. The COMS program comprises all of the above.

MTSU’s program and research includes elements from computer science, applied mathematics, and science. The COMS program focuses on the integration of knowledge and methodologies from all of these disciplines, but is also distinct from the rest.

It is hard to capture how broad the program is without looking at some of the publications recently submitted. They are from across virtually every discipline. However, the common theme is the use of computers to solve cutting-edge scientific problems.

Publications

Publications of the Computational and Data Science Faculty 2018-Present

Bold indicates a faculty author. 

2025

A.Adeogun and M. Faezipour, “Ethical Perspectives into the Utilization of Health Informatics for Cancer Care,” in World Congress in Computer Science, Computer Engineering & Applied Computing, Springer, Cham, 2025, pp. 244–257. doi: 10.1007/978-3-031-85908-3_21.

A. Adeogun and M. Faezipour, “Patient-Centric Paradigm: A Systems Thinking Approach to Enhance Healthcare,” Healthcare, vol. 13, no. 3, p. 213, 2025. doi: 10.3390/healthcare13030213.

S. Adhikari, D. Yan, Z. Jiang, J. Han, Z. Xu, Y. Zhang, A.M. Sainju, & Y. Zhou. (2025). “Scaling Terrain-Aware Spatial Machine Learning for Flood Mapping on Large Scale Earth Imagery Data,” Trans. Spatial Algorithms Syst., 2025. [Online]. Available: https://doi.org/10.1145/3703157.

N.R. Alexander, R.S. Brown, S. Duwadi, S.G. Womble, D. W. Ludwig, K. C. Moe, J. N. Murdock, J. L. Phillips, A. M. Veach, & D. M. Walker. “Leveraging Fine-Scale Variation and Heterogeneity of the Wetland Soil Microbiome to Predict Nutrient Flux on the Landscape,” Microbial Ecology, 2025. doi: 10.1007/s00248-025-02516-1.

A. Ali, G. Gao, R. F. Al-Tobasei, R. C. Youngblood, G. C. Waldbieser, B. E. Scheffler, Y. Palti, & M. S. Salem. “Chromosome level genome assembly and annotation of the Swanson rainbow trout homozygous line,” Scientific Data, vol. 12, no. 1, p. 345, 2025. doi: 10.1038/s41597-025-04693-7.

L. Amao and M. Faezipour,”Modeling the Dynamics of Infectious Diseases in a College Campus: A Case Study of the 2016 Harvard Mumps Outbreak,” in Health Informatics and Medical Systems and Biomedical Engineering, A. Alsadoon et al., Eds., Communications in Computer and Information Science (CCIS), vol. 2259, Springer, pp. 167–179, Apr. 22, 2025. [Online]. Available: https://doi.org/10.1007/978-3-031-85908-3_15.

L. K. Andersen, N. F. Thompson, J. W. Abernathy, R. O. Ahmed, A. Ali, R. F. Al-Tobasei, B. H. Beck, B. Calla, T. A. Delomas, R. A. Dunham, C. G. Elsik, , et al.,  (2025). “Advancing genetic improvement in the omics era: status and priorities for United States aquaculture,” BMC Genomics, vol. 26, no. 1, p. 155, 2025. doi: 10.1186/s12864-025-11247-z.

V. N. Bedekar, Ed., “Energy Harvesting Technologies for Wireless Sensors,” Special Issue Guest Editor – Vishwas Bedekar, MDPI, 2025. [Online]. Available: https://www.mdpi.com/journal/sensors/special_issues/60U10BQY8E

W. Dong, J. Yuan, X. Yang, N. Zhang, and M. Zhang, “Quantitative analysis of cannabinoids by zone heat-assisted DART-MS with in-situ flash derivatization,” Forensic Chem, vol. 42, p. 100647, 2025, doi.org/10.1016/j.forc.2025.100641.

Y. Gu, J. Ranganathan, and L. Xiong, “Increasing Success and Retention of Female Students in Computer Science by Enhancing Two Key Factors: Math Proficiency and Programming Skills,” in Proc. 19th Annu. Southeastern STEM Educ. Res. Conf., Jan. 2025.

M. A. Hossain, W. Liu, N. Ansari, and M. Samad, “Federated meta-RL for network slicing aware VNF placement in 6G core networks,” to be published.

D. C. Jean and S. J. Seo, “Error-correcting open-locating-dominating sets,” Congr. Numerantium, vol. 235, pp. 23–40, 2025. [Online]. Available: https://doi.org/10.61091/cn235-03.

A. Kelly, A. M. Sainju, D. Shrestha, and R. Rimal, “Colocation Mining: Estimating Neighborhood Relationships and Identifying Regional Patterns,” in Proc. ACMSE 2025, pp. 105–113. 

A. Kelly, A. M. Sainju, D. Shrestha, and R. Rimal, “Colocation Mining: Identifying Regional Patterns with a Memory-Efficient Approach,” Int. Conf. Geoinformatics and Data Analysis (ICGDA).

A. M. Khaliq, “Efficient second-order accurate exponential time differencing for time-fractional advection–diffusion–reaction equations with variable coefficients,” Mathematics and Computers in Simulation, vol. 230, pp. 20–38, 2025.

A. M. Khaliq, “Generalized exponential time differencing for fractional oscillation models,” Journal of Computational and Applied Mathematics, vol. 461, p. 116456, 2025. 

D. S. Koti, F. S. Cottle, and J. L. Phillips, “Contrastive Point Cloud Pretraining for Enhanced Transformers,” 2025. doi: 10.1109/ICTAI62512.2024.00057. 

X. Li, L. Cai, and W. Ding, “Modeling the transmission dynamics of a two-strain dengue disease with infection age,” Int. J. Biomathematics, accepted. [Online]. Available: https://doi.org/10.1142/S1793524524500049.

W. Liu, M. A. Hossain, and N. Ansari, “Mobile edge computing for multi-services digital twin-enabled IoT heterogeneous networks,” IEEE Trans. Cogn. Commun. Netw., accepted. [Online]. Available: https://doi.org/10.1109/TCCN.2024.3490779.

Y. Liu and Y. Huang, “Treatment Effect Estimation using the Propensity Score in Non-randomized Clinical Trials with Unbalanced Treatment Arms,” Ann. Biostat. Biometr. Appl., 2025. 

D. W. Ludwig and J. L. Phillips, “DNAGAST: Generative Adversarial Set Transformers for High-throughput Sequencing,” accepted for publication.

L. Miao, C. Winfrey, and H. Zhang, “Outcomes and lessons learned from a first-time National Summer Transportation Institute pre-college program,” in Proc. ASEE Annu. Conf. Expo., Jun. 2025.

D. Ogungbesan, A. Adeogun, A. Adekoya, and M. Faezipour, “Understanding Public Policy Effects on Alcohol-Related Behaviors and Outcomes Using System Dynamics,” World Congress in Computer Science, Computer Engineering & Applied Computing, vol. 2257, pp. 181–192, 2025. doi: 10.1007/978-3-031-85884-0_16.

K. G. Paulson, H. M. Terletska, and H. Fotso, “Work Extraction from a Controlled Quantum Emitter,” J. Phys. Photonics, vol. 7, 025023, 2025. doi: 10.1088/2515-7647/adb255.

S. J. Seo and D. Jean, “Fault-Tolerant Locating Dominating Sets with Error-Correction,” Discrete Mathematics, Algorithms and Applications, accepted. [Online]. Available: https://doi.org/10.1142/S1793830925500491.

J. F. Wallin, “The Community of Scholars,” in Educator Reflections: The Power of Our Stories, MT Open Press, 2025, pp. 1–4. doi: 10.56638/mtopb00325.

D. Wang and W. Ding, “Innovative Biomarker Exploration in ASD: Combining Graph Neural Networks and Permutation Testing on fMRI Data,” NeuroImage: Reports, vol. 5, no. 2, p. 100249, 2025. doi: 10.1016/j.ynirp.2025.100249.

D. Wang, X. Yang, and W. Ding, “Autism Spectrum Disorder (ASD) Classification with Three Types of Correlations Based on ABIDE I Data,” Math. Found. Comput., vol. 8, no. 1, pp. 113–127, 2025. doi: 10.3934/mfc.2023042.

A. Yinusa and M. Faezipour, “Evaluating Artificial Intelligence Robustness Against FGSM and PGD Adversarial Attacks with L-Norms Perturbations,” in *World Congr. Comput. Sci., Comput. Eng. Appl. Comput.*, vol. 2251, pp. 315–328, 2025. [Online]. Available: https://doi.org/10.1007/978-3-031-85628-0_23.

A. Yinusa and M. Faezipour, “A multi-layered defense against adversarial attacks in brain tumor classification using ensemble adversarial training and feature squeezing,” Sci. Rep., vol. 15, no. 1, pp. 1–11, 2025, doi: 10.1038/s41598-025-00890-x.

H. Zhang, V. Bedekar, and E. Ledoux, Robotics and Control Engineering Textbook. Pressbooks, 2025. [Online]. Available: https://doi.org/10.56638/oermtb00325.

2024

A. Adeogun and M. Faezipour, “A system dynamics view of patient’s perception of AI and Big Data adoption in healthcare,” BMC Proceedings, vol. 18, p. P1, 2024. doi: 10.1186/s12919-024-00292-3.

H. Alrammah, Y. Gu, D. Yun, & N. Zhang. “Tri-Objective Optimization for Large-scale Workflow Scheduling and Execution in the Cloud,” J. Netw. Syst. Manag., vol. 32, no. 4, 2024. https://doi.org/10.1007/s10922-024-09863-3.

L. Amao and M. Faezipour, “A Comprehensive Review of Electronic Health Records Implementation in Healthcare,” in Proc. Int. Conf. Healthc. Informatics (ICHI), Apr. 2024.

H. N. Bhandari, N. R. Pokhrel, R. Rimal, K. R. Dahal, and B. Rimal, “Implementation of Deep Learning Models in Predicting ESG Index Volatility,” Financial Innovation, 2024. [Online]. Available: https://link.springer.com/article/10.1186/s40854-023-00604-0.

A. Baul, H. M. Terletska, K. M. Tam, and J. Moreno, “Quantum classical algorithm for the study of phase transitions in the Hubbard model via dynamical mean-field theory,” Quantum Rep., vol. 7, no. 2, Art. no. 18, May 2024. [Online]. Available: https://doi.org/10.48550/arXiv.2308.01392.

B. Cankaya, K, Ciftci, J. Garcia, N. Madkour, B. Tokgoz, & K. N. Poudel. “Operational Performance Analysis for Brazilian Aviation System using XAI: A Case Study for Load Factor Analysis,” Journal of Supply Chain and Operations Management, vol. 22, no. 2, p. 39, 2024.

X. Chai, H. Zhang, X. Lin, Y. Zhou, and Y. Yu, “Method for orthogonal fitting of arbitrary shaped aperture wavefront and aberration removal,” Optical Engineering, 2024. doi: 10.1117/1.OE.63.5.054112.

S. Chen, J. Liu, and Y. Wu, “Evolution of dispersal in advective patchy environments with varying drift rates,” SIAM Journal on Applied Dynamical Systems, vol. 23, no. 1, pp. 696–720, 2024. https://doi.org/10.1137/22M1542027.

W. Dong, X.  Yang, N. Zhang, P. Che, J. Sun, J., J. Harnly, & M. Zhang, “Study of UV-Vis Molar Absorptivity Variation and Quantitation of Anthocyanins Using Molar Relative Response Factor,” Food Chemistry, vol. 444, 2024. https://doi.org/10.1016/j.foodchem.2024.138653.

W. Dong and V. N. Bedekar, “Design, Modeling, and Feasibility Analysis of Rotary Valve for Internal Combustion Engine,” Journal of Combustion, 2024. doi: 10.1155/2024/8049436.

K. Givens, D. W. Ludwig, and J. L. Phillips, “StrXL: Modeling Potentially Infinite Length Sets of Data with Deep Learning,” in Proc. 37th Int. FLAIRS Conf. (FLAIRS 2024), May 2024. doi: 10.32473/flairs.37.1.135568.

N. Hasan, S. Mithun, S. Baral, and K. N. Poudel, “Personalized stress detection using a lightweight machine learning framework with convenient wrist-worn sensors,” in Proc. IEEE SPMB, 2024, pp. 1–7. [Online]. Available: https://www.researchgate.net/publication/388800027_Personalized_Stress_Detection_using_a_Lightweight_Machine_Learning_Framework_With_Convenient_Wrist-Worn_Sensors

L. Huang, Z. Jiang, Y. Wu, and Z. Yuan, “Analysis of a diffusive epidemic model with a zero-infection zone,” J. Math. Anal. Appl., vol. 538, no. 2, p. 128456, 2024. [Online]. Available: https://doi.org/10.1016/j.jmaa.2024.128456

D. Jean and S. J. Seo, “Open-locating-dominating sets with error correction,” in Proc. ACM, 2024, pp. 297–301. [Online]. Available: https://doi.org/10.1145/3603287.3651212

D. C. Jean and S. J. Seo, “Fault-tolerant identifying codes in special classes of graphs,” Discuss. Math. Graph Theory, vol. 44, no. 2, pp. 591–611, 2024.

A. M. Khaliq, “Physics-informed encoder-decoder gated recurrent neural network for solving time-dependent PDEs,” J. Mach. Learn. Model. Comput., vol. 5, no. 3, pp. 69–85, 2024.

A. M. Khaliq, “Quantum Recurrent Neural Networks: Predicting the Dynamics of Oscillatory and Chaotic Systems,” Algorithms, vol. 17, no. 4, pp. 163–181, 2024.

J. Kong, “Density functional theory for fractional charge: Locality, size consistency, and exchange-correlation,” Journal of Chemical Physics, vol. 161, p. 224111, 2024. doi: 10.1063/5.0234907. 

E. S. Kryachko, P. J. MacDougall, and S. Neal, “Unravelling the hydrogen bonding patterns in telomeric G-quadruplexes: from structure to function,” Molecular Physics, 2024. doi: 10.1080/00268976.2024.2390586.

R. N. Leander, G. Owanga, D. Nelson, and Y. Liu, “A mathematical model of stroma-supported allometric tumor growth,” Bull. Math. Biol., vol. 86, no. 4, p. 38, 2024. [Online]. Available: https://doi.org/10.1007/s11538-024-01265-5

S. Liu, Q. Wu, and X. Yang, “Pairwise learning for autism spectrum disorder imbalanced classification,” DOI: 10.1109/ECBIOS61468.2024.10885487

P. J. MacDougall and K. K. Donthula, “Using Quantum Atomics and Machine Learning to Advance Picotechnology,” Theor. Chem. Acc., vol. 143, Art. no. 68, 2024, doi: 10.1007/s00214-024-03142-9.

A.Mahfooz and J. L. Phillips, “Conditional Forecasting of Bitcoin Prices Using Exogenous Variables,” IEEE Access, 2024. doi: 10.1109/ACCESS.2024.3381516.

J. May et al., “Using the CARLA Simulator to Train A Deep Q Self-Driving Car to Control a Real-World Counterpart on A College Campus,” in Proc. IEEE BigData, 2024. doi: 10.1109/BigData59044.2023.10386739.

L. Miao, “Introducing Arduino to mechatronics engineering students via lab activities and a hands-on signature-thinking course project,” in Proc. ASEE Annu. Conf. Expo., Jun. 2024.

E. Mohammadrezae, P. Dongre, D. Gračanin, and H. Zhang, “Systematic review of extended reality for smart built environments lighting design simulations,” 2024. [Online]. Available: https://ieeexplore.ieee.org/document/10415024

M. Mohebbi and F. Rajabipour, “Reactive transport modeling to predict leaching of coal-derived fly ash,” Water, Air, & Soil Pollution, vol. 235, no. 2, 2024. [Online]. Available: https://doi.org/10.1007/s11270-023-06857-w

H. G. Momm et al., “Long term conservation practice effects on agricultural soil loss from concentrated and distributed sources,” J. Environ. Manage., vol. 371, 2024. [Online]. Available: https://doi.org/10.1016/j.jenvman.2024.123278

T. Nhan, J. Upadhyay, S. Poudel, S. Wagle, and K. N. Poudel, “Scalable Multimodal Machine Learning for Cervical Cancer Detection,” pp. 502–510, 2024. doi: 10.1109/AIIoT61789.2024.10578984.

T. Nhan, K. R. Upadhyay, and K. N. Poudel, “Towards Patient-Centric Healthcare: Leveraging Blockchain for Electronic Health Records,” pp. 1–8, 2024. doi: 10.1145/3632634.365588.

D. Ogungbesan and M. Faezipour, “Improving healthcare delivery with artificial intelligence: a diagnostic and prescription recommender system,” BMC Proceedings, vol. 18, p. O13, 2024. doi: 10.1186/s12919-024-00292-3.

S. N. Panak, D. Ogungbesan, M. A. Erskine, and M. Faezipour, “Mindful Disclosure: Exploring Privacy Decisions through Neurological Monitoring,” AMCIS 2024 TREOS, Jun. 14, 2024. [Online]. Available: https://aisel.aisnet.org/treos_amcis2024/99

N. Pokhrel et al., “Deep-SDM: A Unified Computational Framework for Sequential Data Modeling Using Deep Learning Models,” Software, 2024. doi: 10.3390/software3010003.

T. Qin et al., “Comparative Transcriptome Analysis of Deep-Rooting and Shallow-Rooting Potato (Solanum tuberosum L.) Genotypes under Drought Stress,” Plants, vol. 11, p. 2024, 2022. doi: 10.3390/plants11152024.

W. Qin et al., “Effects of conservation tillage and straw mulching on crop yield, water use efficiency, carbon sequestration and economic benefits in the Loess Plateau region of China: A meta-analysis,” Soil Tillage Res., vol. 238, p. 106025, published.

C. Rangi, H. Fotso, H. M. Terletska, J. Moreno, and K. M. Tam, “Disorder enhanced thermalization in interacting many-particle system,” Phys. Rev. B, accepted.

G. Raymo et al., “Fecal microbiome analysis uncovers hidden stress effects of low stocking density on rainbow trout,” Anim. Microbiome, vol. 6, no. 1, 2024. doi: 10.1186/s42523-024-00344-1.

R.Rimal, B. Rimal, H. N. Bhandari, K. R. Dahal, and N. R. Pokhrel, “Real Estate Market Prediction using Deep Learning Models,” Ann. Data Sci., 2024. doi: 10.1007/s40745-024-00543-2.

A. Romer et al., “Effects of snake fungal disease (ophidiomycosis) on the skin microbiome across two major experimental scales,” Conserv. Biol., 2024. doi: 10.1111/cobi.14411.

R. Salako and Y. Wu, “On degenerate reaction-diffusion epidemic models with mass action or standard incidence mechanism,” Eur. J. Appl. Math., pp. 1–28, 2024. doi: 10.1017/S0956792523000359.

S. Salako and Y. Wu, “On the dynamics of an epidemic patch model with mass‐action transmission mechanism and asymmetric dispersal patterns,” Stud. Appl. Math., vol. 152, no. 4, pp. 1208–1250, 2024. doi: 10.1111/sapm.12674.

M. S. Salem et al., “Functional annotation of regulatory elements in rainbow trout uncovers roles of the epigenome in genetic selection and genome evolution,” GigaScience, vol. 13, 2024. doi: 10.1093/gigascience/giae092.

S. J. Seo and D. Jean, “Optimal Error-detection system for Identifying Codes,” Networks, vol. 85, pp. 61–75, 2024. [Online]. Available: https://doi.org/https://doi.org/10.1002/net.22254

S. J. Seo and D. Jean, “Fault-tolerant Locating-Dominating Sets on the Infinite Tumbling Block Graph,” Australas. J. Combin., vol. 90, pp. 29–45, 2024.

S. A. Streeter et al., “Mitotic gene regulation by the N-MYC-WDR5-PDPK1 nexus,” BMC Genomics, vol. 25, no. 1, p. 360, 2024. doi: 10.7589/2019-04-109doi: 10.7589/2019-04-109.

T. Sun, H. Wang, and D. Wang, “Robust Prediction Intervals for Valuation of Large Portfolios of Variable Annuities: A Comparative Study of Five Models,” Computational Economics, 2024. [Online]. Available: https://link.springer.com/article/10.1007/s10614-024-10574-9.

M. I. Swindall et al., “Smart Digital Edition Management: A Blockchain Framework for Papyrology,” 2024. doi: 10.32604/cmc.2020.09763doi: 10.32604/cmc.2020.09763.

M. I. Swindall et al., “Towards a Platform for AI-Assisted Papyrology,” Joint Proc. ACM IUI Workshops, 2024.

H. Tian, X. Zhuang, A. Yan, and H. Zhang, “A novel multiple-image encryption with multi-petals structured light,” Sci. Rep., 2024.

J. Upadhya, K. Poudel, and J. Ranganathan, “A Comprehensive Approach to Early Detection of Workplace Stress with Multi-Modal Analysis and Explainable AI,” May 2024. doi: 10.1145/3632634.3655878.

J. Upadhya, K. Poudel, and J. Ranganathan, “Advancing Medical Image Diagnostics through Multi-Modal Fusion: Insights from MIMIC Chest X-Ray Dataset Analysis,” Jul. 2024. doi: 10.1109/ICMI60790.2024.10586129.

J. Upadhya et al., “VulnFusion: Exploiting Multimodal Representations for Advanced Smart Contract Vulnerability Detection,” in Proc. 2024 6th Int. Conf. Blockchain Comput. Appl. (BCCA), 2024, pp. 505–515. doi: 10.1109/bcca62388.2024.10844483.

J. Upadhya et al., “QuadraCode AI: Smart Contract Vulnerability Detection with Multimodal Representation,” in Proc. 2024 33rd Int. Conf. Comput. Commun. Netw. (ICCCN), 2024, pp. 1–9. doi: 10.1109/icccn61486.2024.10637655.

J. Upadhyay, K. N. Poudel, and J. Ranganathan, “Advancing Medical Image Diagnostics through Multi-Modal Fusion: Insights from MIMIC Chest X-Ray Dataset Analysis,” 2024, pp. 1–8. doi: 10.1109/ICMI60790.2024.10586129.

J. Upadhya, J. Vargas, K. Poudel, and J. Ranganathan, “Improving the efficiency of Multimodal approach for Chest X-ray,” Mar. 2024. doi: 10.1007/978-3-031-56950-0_5.

L. Vargas-Gastélum et al., “Herptile gut microbiomes: a natural system to study multi-kingdom interactions between filamentous fungi and bacteria,” MSphere, 2024. doi: 10.1128/msphere.00475-23.

S. Wagle, S. Pandey, S. Poudel, and K. N. Poudel, “Brain Tumor Segmentation and Classification Using ACGAN with U-Net and Independent CNN-Based Validation,” 2024, pp. 1–11. doi: 10.1109/SPMB62441.2024.10842266.

L. Wang et al., “Taming Cross-Domain Representation Variance in Federated Prototype Learning with Heterogeneous Data Domains,” Dec. 2024.

Z. Wang et al., “Laboratory channel widening quantification using deep learning,” Geoderma, vol. 450, p. 117034, 2024. doi: 10.1016/j.geoderma.2024.117034

R. Wells et al., “Cropland water erosion estimates simulated by RUSLE2 and WEPP: Results from two initial studies,” J. Soil Water Conserv., vol. 75, no. 5, pp. 215–232. [Online]. Available: https://doi.org/doi.org/10.2489/jswc.2024.00072.

G. West et al., “A deep learning pipeline for the palaeographical dating of ancient Greek papyrus fragments,” in Proc. 1st Workshop Mach. Learn. Anc. Lang. (ML4AL 2024), pp. 177–185, 2024. [Online]. Available: https://doi.org/10.18653/v1/2024.ml4al-1.18.

G. T. West et al., “Incorporating Crowdsourced Annotator Distributions into Ensemble Modeling to Improve Classification Trustworthiness for Ancient Greek Papyri,” J. Data Min. Digit. Humanit., 2024. [Online]. Available: https://doi.org/https://doi.org/10.46298/jdmdh.10297.

C. Winfrey and L. Miao, “Using Reinforcement Learning to Optimize Isolated Traffic Signals with High Priority Vehicles,” in Proc. 7th Int. Conf. Artif. Intell. Big Data (ICAIBD), May 2024.

L. Xiong, X. Chen, J. Liang, X. Cao, P. Zhu, and M. Zhao, “Tree-based Machine Learning Methods for Analytics of Online Shoppers’ Purchasing Intentions,” Int. J. Data Sci., vol. 9, no. 2, 2024. [Online]. Available: https://www.inderscience.com/offers.php?id=139680.

L. Xiong, Y. Zhu, and S. M. Zaza, “Enhancing Data Diversity and Traceability to Mitigate Bias in Healthcare AI: A Blockchain-Driven Approach,” in Proc. 2024 Computers and People Research Conf. (SIGMIS-CPR ’24), pp. 1–2, 2024. [Online]. Available: https://doi.org/10.1145/3632634.3655881.

A. Yinusa and M. Faezipour, “Enhancing Occupational Health and Safety in Industrial Workplaces Through System Dynamics Modeling,” Published, 2024. [Online]. Available: https://www.proquest.com/docview/3166809367/F6702F0390DD46B0PQ/1?sourcetype=Conference.

A. Yinusa and M. Faezipour, “Unveiling inequity: state-by-state disparities in years of potential life lost by race,” *BMC Proc.*, vol. 18, P30, 2024. [Online]. Available: https://doi.org/10.1186/s12919-024-00292-3

H. Zhang, D. Cao, W. Zhou, and K. Currie, “Laser and optical radiation weed control: a critical review,” *J. Precision Agric.*, 2024. [Online]. Available: https://doi.org/10.1007/s11119-024-10152-x

H. Zhang and R. Rajan, “Understanding Embodied Robotics Learning Using Video Based LLM Methods,” published. [Online]. Available: https://par.nsf.gov/biblio/10542577

H. Zhang, S. Byler, and W. Zhou, “Novel Under-Surface Soil Moisture Measurement with Laser Image Recognition,” published. [Online]. Available: https://doi.org/10.1109/10728705

H. Zhang, S. Byler, and W. Zhou, “A Novel Contactless Soil Moisture Measurement with Laser,” published. [Online]. Available: https://opg.optica.org/ViewMedia.cfm?r=1&uri=FiO-2023-JTu7C.3&seq=0

L. Zhang, J. Xu, Z. Lu, and L. Song, “CrossVision: Real-time On-Camera Video Analysis via Common RoI Load Balancing,” *IEEE Trans. Mobile Comput.*, 2024.

D. Zhu, Y. Khaliq, H. Wang, T. Sun, and D. Wang, “Enhancing mortgage rate prediction: a comprehensive evaluation of computational statistical approaches,” *Int. J. Comput. Math.*, vol. 101, no. 4, pp. 373–385, 2024. [Online]. Available: https://doi.org/10.1080/00207160.2024.2331691

2023

A. A. Adeogun and M. Faezipour, “Advancing Child and Maternal Health: A System Dynamics Exploration of Policy Interventions to Tackle Socioeconomic Disparities,” in Proc. Int. Conf. Comput. Sci. Comput. Intell. (CSCI), 2023, pp. 1318–1325. doi: 10.1109/csci62032.2023.00218. 

A. A. Adeogun and M. Faezipour, “Big Data in Healthcare: Acquisition, Management, and Visualization Using System Dynamics,” in Proc. Int. Conf. Comput. Sci. Comput. Intell. (CSCI), 2023, pp. 611–618. doi: 10.1109/csci62032.2023.00108.

A. A. Adeogun and M. Faezipour, “Exploring Risk Factors in PDAC Using System Dynamics,” in Proc. Congr. Comput. Sci., Comput. Eng., Appl. Comput. (CSCE), Las Vegas, NV, USA, 2023, pp. 1368–1373. doi: 10.1109/CSCE60160.2023.00229.

L. Amao and M. Faezipour, “Health Informatics for Contact Tracing in a Pandemic Response: A Perspective,” in Proc. Int. Conf. Comput. Sci. Comput. Intell. (CSCI), 2023, pp. 1484–1487. doi: 10.1109/csci62032.2023.00243.

L. Amao and M. Faezipour, “Modeling Obesity Prevention Programs to Reduce Overweight Rates at Schools: A Perspective,” in Proc. Congr. Comput. Sci., Comput. Eng., Appl. Comput. (CSCE), 2023, pp. 1290–1293. doi: 10.1109/csce60160.2023.00216.

S. Chen, J. Shi, Y. Wu, and Z. Shuai, “Evolution of dispersal in advective patchy environments,” Journal of Nonlinear Science, vol. 33, no. 3, p. 40, 2023. doi: 10.1007/s00332-023-09899-w.

S. Chen, J. Liu, and Y. Wu, “On the impact of spatial heterogeneity and drift rate in a three-patch two-species lotka–volterra competition model over a stream,” Zeitschrift Für Angewandte Mathematik Und Physik, vol. 74, no. 3, p. 117, 2023. doi: 10.1007/s00033-023-02009-6.

Z. Chen, H. Zhang, W. Zhou, and Y. Yu, “Phase aberration adaptive compensation in digital holography based on phase imitation and metric optimization,” 2023.  https://opg.optica.org/oe/fulltext.cfm? uri=oe-31-13-21048&id=531345

Z. Chen, W. Zhou, L. Duan, H. Zhang, H. Zheng, X. Xia, Y. Yu, & T. Poon. “Automatic elimination of phase aberrations in digital holography based on Gaussian 1σ criterion and histogram segmentation,” Optics Express, 2023.  https://opg.optica.org/oe/fulltext.cfm?uri=oe-31-9-13627&id=529009

W. Ding, J. Phillips, Z. Qu, and R. Zaretzki, “Special Issue: Machine Learning, Mathematical and Statistical Modeling for Systems Biology,” Math. Biosci. Eng., [Online]. Available: https://www.aimspress.com/mbe/article/6087/special-articles

E. Dohner, H. M. Terletska, and H. F. Fotso, “Thermalization of a disordered interacting system under an interaction quench,” Phys. Rev. B, vol. 108, no. 14, p. 144202, 2023. doi: 10.1103/PhysRevB.108.144202

J. E. Farzidayeri, R. A. Taylor, and V. N. Bedekar, “Design of a multicylinder crank-slider wind energy harvester utilizing Faraday’s law of electromagnetic induction,” Applied Energy, vol. 351, 2023. doi: 10.1016/j.apenergy.2023.121808.

J. E. Farzidayeri, W. W. Boles, and V. N. Bedekar, “A Simple Type 2 Lever for Lifting and Moving Monoliths,” Journal of Engineering and Architecture, vol. 11, no. 1, pp. 1–6, 2023. doi: 10.15640/jea.v11n1a1.

M. Faezipour, M. Faezipour, and S. Pourreza, “Resiliency and Risk Assessment of Smart Vision-Based Skin Screening Applications with Dynamics Modeling,” Sustainability, vol. 15, no. 18, 2023. doi: 10.3390/su151813832.

M. Faezipour, “A System Dynamics Approach to Exploring Personality Traits in Young Children,” published.

S. Hamdan, K. DuBray, J. Treutel, R. Paudyal, and K. N. Poudel, “Reducing MEG interference using machine learning,” Mach. Learn. with Appl., vol. 12, p. 100462, 2023. [Online]. Available: https://doi.org/10.1016/j.mlwa.2023.100462.

N. Hasan, S. Hamden, S. Poudel, J. Vargas, and K. N. Poudel, “Prediction of length-of-stay at intensive care unit (ICU) using machine learning based on MIMIC-III database,” in Proc. IEEE CAI, 2023, pp. 321–323. [Online]. Available: https://doi.org/10.1109/CAI54212.2023.00142

H. Hebert et al., “Connecting online graduate students to the university community,” J. Higher Educ. Theory Pract., 2023. [Online]. Available: https://articlegateway.com/index.php/JHETP/article/view/5815

H. Hebert et al., “Connecting online graduate students to the university community,” J. Higher Educ. Theory Pract., 2023. [Online]. Available: https://articlegateway.com/index.php/JHETP/article/view/5815

Y. Huang, L. Zhang, and J. Xu, “Adversarial group linear bandits and its application to collaborative edge inference,” unpublished, Aug. 29, 2023.

D. C. Jean and S. J. Seo, “Progress on fault-tolerant locating-dominating sets,” Discrete Math. Algorithms Appl., vol. 15, no. 2, 2023.

D. C. Jean and S. J. Seo, “On redundant locating-dominating sets,” Discrete Appl. Math., vol. 329, pp. 106–125, 2023.

D. C. Jean and S. J. Seo, “Optimal error-detecting open-locating-dominating set on the infinite triangular grid,” Discuss. Math. Graph Theory, vol. 43, no. 2, pp. 445–455, 2023. [Online]. Available: http://dx.doi.org/10.7151/dmgt.2374

N. Ji and D. Ye, “The clique number of graphs covered by long cycles,” SIAM J. Discrete Math., vol. 37, no. 2, pp. 917–924, 2023. [Online]. Available: https://doi.org/10.1137/22M147604https://doi.org/10.1137/22M147604

Y. Jia, R. Wells, H. G. Momm, Y. Yaoxin Zhang, and S. Bennett, “Physically based numerical model for the landscape evolution of soil-mantled watersheds driven by rainfall and overland flow,” Journal of Hydrology, vol. 620, Part A, p. 129419, 2023. doi: 10.1016/j.jhydrol.2023.129419. 

Z. Jiang et al., “Hidden Markov Forest for Terrain-Aware Flood Inundation Mapping on Earth Imagery,” 2023. doi: 10.1137/1.9781611977653.ch36. 

M. Lei, D. Jiang, and H. Zhang, “Wireless secret sharing game for Internet of Things,” Sustainability, vol. 15, no. 9, p. 7427, 2023. [Online]. Available: https://www.mdpi.com/2071-1050/15/9/7427

Y. Liu, L. Yang, and L. Xiong, “Performance Commitments and the Properties of Analyst Earnings Forecasts: Evidence from Chinese Reverse Merger Firms,” Int. Rev. Financ. Anal., vol. 89, p. 102775, 2023, doi: 10.1016/j.irfa.2023.102775.

P. J. MacDougall, “We are family!,” Chem. Eng. News, vol. 101, no. 7, p. 30, Feb. 27, 2023. [Online]. Available: https://pubs.acs.org/doi/10.1021/cen-10107-comment1

V. A. Manathunga, “The coefficients of the jones polynomial,” J. Knot Theory Ramifications, vol. 32, no. 7, 2023. doi: 10.1142/S0218216523500530.

V. A. Manathunga and L. Deng, “Pricing pandemic bonds under Hull–White–stochastic logistic growth model,” Risks, vol. 11, no. 9, 2023, Art. no. 155, doi: 10.3390/risks11090155.

J. May and K. Poudel, “A Brief Survey of Offline Explainability Metrics for Conversational Recommender Systems,” in Proc. IEEE SPMB, 2023, pp. 1–9. doi: 10.1109/SPMB59478.2023.10372769.

G. Metri and X. Yang, “Group-level analysis of relations between resting-state functional connectivity and arithmetic ability using CONN,” in Proc. CSCI, 2023, pp. 1509–1514. doi: 10.1109/CSCI58124.2022.00267.

L. Miao, D. Jiang, and H. Zhang, “Wireless secret sharing game for Internet of Things,” Sustainability, Special Issue: Advances in Smart City and Intelligent Transportation Systems, 2023. [Online]. Available: https://doi.org/10.3390/su15097427

M. Mohebbi, B. Smith, and J. G. Mendez, “A survey study of ergonomic perceptions among university students in Middle Tennessee,” 2023. [Online]. Available: https://isoes.info/conferences/2023/index.html

S. Muhammed et al., “Improved classification of Alzheimer’s disease with convolutional neural networks,” Dec. 2023. [Online]. Available: https://doi.org/10.1109/SPMB59478.2023.10372725

T. D. Nguyen, Y. Wu, T. Tang, et al., “Impact of resource distributions on competition of species in stream environment,” Journal of Mathematical Biology, vol. 87, 2023. doi: 10.1007/s00285-023-01978-6.

T. D. Nguyen, Y. Wu, A. Veprauskas, et al., “Maximizing metapopulation growth rate and biomass in stream networks,” SIAM Journal on Applied Mathematics, vol. 83, no. 6, pp. 2145–2168, 2023. doi: 10.1137/23M1556757.

S. Olukayode, C. Froese Fischer, and A. Volkov, “Revisited relativistic Dirac–Hartree–Fock X-ray scattering factors. II. Chemically relevant cations and selected monovalent anions for atoms with Z = 3 –112,” Acta Crystallographica Section A: Foundations and Advances, vol. 79, pp. 229–245, 2023. doi: 10.1107/S205327332300116X.

S. Olukayode, C. Froese Fischer, and A. Volkov, “Revisited relativistic Dirac–Hartree–Fock X-ray scattering factors. I. Neutral atoms with Z = 2–118,” Acta Crystallographica Section A: Foundations and Advances, vol. 79, pp. 59–79, 2023. doi: 10.1107/S2053273322010944.

E. Oluwasakin, T. Torku, A. Yinusa, S. Hamden, S. Poudel, N. Hassan, J. Vargas, and K. Poudel, “Minimization of high computational cost in data preprocessing and modeling using MPI4Py,” Machine Learning with Applications, vol. 13, 2023. doi: 10.1016/j.mlwa.2023.100483.

S. Poudel, R. Paudyal, C. Burak, and K. Poudel, “Cryptocurrency price and volatility predictions with machine learning,” J. Mark. Anal., vol. 11, pp. 642–660, 2023. doi: 10.1057/s41270-023-00239-1.

K. N. Poudel et al., “HealthCare Text Analytics Using Recent ML Techniques,” pp. 134–142, 2023.

W. Qin et al., “Impact of fertilization and grazing on soil N and enzyme activities in a karst pasture ecosystem,” Geoderma, vol. 437, p. 116578, 2023.

J. Ranganathan and G. Abuka, “Text Summarization using Transformer Model,” presented at AMCIS 2023, Mar. 2023. doi: 10.1109/SNAMS58071.2022.10062698.

R. Rimal, “Identifying the Neurocognitive Difference Between Two Groups Using Supervised Learning,” Stat. Optim. Inf. Comput., 2023. doi: 10.19139/soic-2310-5070-1340.

R. Salako and Y. Wu, “Global dynamics of epidemic network models via construction of Lyapunov functions,” Proc. Amer. Math. Soc., accepted.

M. Salem, R. F. Al Tobasei, Y. Palti, G. Gao, and H. Zhou, “Status Of The Assembly And Functional Annotation Of Rainbow Trout Genome,” Aquaculture America 2023, p. 489.

R. Rimal, M. Brannon, Y. Wang, and X. Yang, “Comparative study of machine learning methods on ASD classification,” Int. J. Data Sci. Anal., 2023.

C. Shen, L. Zhang, Z. Yang, M. Mortazavi, X. Song, L. Peng, and H. Yu, “Envisioning a Next Generation Extended Reality Conferencing System With Efficient Photorealistic Human Rendering,” Published, Jun. 28, 2023.

C. Shen, W. Zhou, H. Zhang, Y. Yu, and C. Liu, “Combined method of face super-resolution and rotation,” Published, 2023. [Online]. Available: https://www.spiedigitallibrary.org/conference-proceedings-of-spie/12550/125501J/Combined-method-of-face-super-resolution-and-rotation/10.1117/12.2667506.full

I. Shrik, J. F. Wallin, M. B. Hein, A. C. Friedli, R. F. Al Tobasei, M. Sharp, and A. Fine, “Measuring learner behavior using wearable AR,” NSF Grant, vol. 23, pp. 23–24, 2023.

F. Tawfik and Y. Gu, “An Advanced Convolutional Neural Network for Detecting Chest X-ray Abnormalities,” Int. J. Mach. Learn., vol. 13, no. 4, pp. 136–141, 2023.

F. Tawfik and Y. Gu, “An Advanced Convolutional Neural Network for Detecting Chest X-ray Abnormalities,” in Int. Conf. Image Process. Mach. Intell. (IPMI), Feb. 2023.

G. Toban, K. Poudel, and D. Hong, “REM Sleep Stage Identification with Raw Single Channel EEG,” Bioengineering, vol. 10, no. 9, 2023. doi: 10.3390/bioengineering10091074.

N. Tratnik and D. Ye, “Resonance Graphs on Perfect Matchings of Graphs on Surfaces,” Graphs Combin., vol. 39, no. 4, 2023. doi: 10.1007/s00373-023-02666-4.

J. F. Wallin et al., “CyberLearnAR: The development of a wearable augmented reality system for teaching STEM,” NSF Grant, vol. 95–96, 2023.

J. F. Wallin, “ChatGPT Vision vs the Real World,” 2023.

J. F. Wallin, “Course Policies for Using AI – a Blog post about AI ethics and policies,” 2023.

J. F. Wallin, “ChatGPT and AI-generated Code: The Impact of Natural Language Models on Software Creation and Sharing,” Astronomy Source Code Library Blog, 2023.

D. Wang, R. Sun, and L. B. Green, “Prediction Intervals of Loan Rate for Mortgage Data Based on Bootstrapping Technique: A Comparative Study,” Math. Found. Comput., vol. 6, no. 2, 2023. doi: 10.3934/mfc.2022027.

G. West, Z. Sinkala, and J. Wallin, “A kernel mixing strategy for use in stochastic optimization and adaptive Markov chain Monte Carlo contexts,” Front. Appl. Math. Stat., Accepted, 2023. [Online]. Available: https://doi.org/https://doi.org/10.1016/j.ascom.2023.100691.

G. West, M. Ogden, and J. Wallin, “A robust fitness function and genetic algorithm to morphologically constrain the dynamics of interacting galaxies,” Astron. Comput., vol. 42, p. 100691, 2023. [Online]. Available: https://doi.org/https://doi.org/10.1016/j.ascom.2023.100691.

C. Winfrey, P. Meleby, and L. Miao, “Using Big Data and Machine Learning to Rank Traffic Signals in Tennessee,” J. Traffic Transp. Eng., 2023. [Online]. Available: https://doi.org/https://doi.org/10.1016/j.jtte.2023.04.005.

C. Winfrey and L. Miao, “Utilizing MATLAB in Combination with Lego Mindstorm EV3 Kits for a First-year Engineering Course,” in Proc. ASEE Annu. Conf. Expo., Jun. 2023. [Online]. Available: https://nemo.asee.org/public/conferences/327/papers/40807/view.

L. Xiong, J. Luo, H. Vise, and M. White, “Distributed Least-Squares Monte Carlo for American Option Pricing,” Risks, vol. 11, no. 5, p. 145, 2023. [Online]. Available: https://doi.org/10.3390/risks11080145.

L. Xiong et al., “AutoReserve: A Web-Based Tool for Personal Auto Insurance Loss Reserving with Classical and Machine Learning Methods,” Risks, vol. 11, no. 7, 2023. [Online]. Available: https://doi.org/10.3390/risks11070131.

S. Xu, V. A. Manathunga, and D. Hong, “Framework on BERT Based Prediction Models for Pricing of Warranty Policies,” Variance, vol. 16, no. 2, 2023. [Online]. Available: https://variancejournal.org/article/89002-framework-of-bert-based-nlp-models-for-frequency-and-severity-in-insurance-claims.

S. Xu, S. Jagadamma, S. Cui, R. Nave, and J. Kubesch, “Forage species composition influenced soil health in organic forage transitioning systems,” Agric. Ecosyst. Environ., vol. 342, p. 108228, 2023.

X. Yang and R. Rimal, “Functional Connectivity Based Classification for Autism Spectrum Disorder using Spearman’s Rank Correlation,” Published, 2023. [Online]. Available: https://doi.org/10.1109/IECBES54088.2022.10079445.

A. Yinusa and M. Faezipour, “Optimizing Healthcare Delivery: A Model for Staffing, Patient Assignment, and Resource Allocation,” *Appl. Syst. Innov.*, vol. 6, no. 5. [Online]. Available: https://doi.org/10.3390/asi6050078

H. Zhang, S. Byler, and W. Zhou, “Multiple Wavelength Object Recognition with Spectrometer in the Wild for Precision Agriculture,” published. [Online]. Available: https://opg.optica.org/abstract.cfm?uri=pcAOP-2023-JW2A.35

A. Zhang, M. Lei, J. Zhou, and A. Yan, “Artificial Intelligence for Privacy Conservation in Remote Learning – Privacy and Safety in Online Learning,” *MTSU Open Press*, 2023. [Online]. Available: https://mtsu.pressbooks.pub/privacyandsafetyinonlinelearning/chapter/artificial-intelligence-for-privacy-conservation-in-remote-learning/

H. Zhang, L. Miao, J. Zhong, and A. Yan, “Artificial Intelligence for Privacy Conservation in Remote Learning,” *MT Open Press*, 2023. [Online]. Available: https://openpress.mtsu.edu/index.php/mtop/catalog/book/2

L. Zhang, J. Zhong, and W. Zhou, “Precision Optical Weed Removal Evaluation with Laser,” published. [Online]. Available: https://opg.optica.org/abstract.cfm?uri=CLEO_AT-2023-JW2A.145

L. Zhang, M. Li, C. Chen, and J. Xu, “IL-NeRF: Incremental Learning for Neural Radiance Fields with Camera Pose Alignment,” published. https://doi.org/10.48550/arXiv.2312.05748

L. Zhang and J. Xu, “E3Pose: Energy-Efficient Edge-assisted Multi-camera System for Multi-human 3D Pose Estimation,” published, May 9, 2023.

2022

F. Agusto, D. Bond, A. Cohen, W. Ding, R. Leander, & A. Royer. “Optimal Impulse Control of West-Nile Virus,” AIMS Mathematics, vol. 7, no. 10, pp. 19597–19628, 2022. doi: 10.3934/math.20221075

R. O. Ahmed, A. Ali, R.F. Al-Tobasei, T. Leeds, B. Kenney, & M. S. Salem. “Weighted Single-Step GWAS Identifies Genes Influencing Fillet Color in Rainbow Trout,” Genes, vol. 13, no. 8, 2022. doi: 10.3390/genes13081331

A. Ali, W. M. Shaalan, R. F. Al-Tobasei, & M. S. Salem.  “Coding and Noncoding Genes Involved in Atrophy and Compensatory Muscle Growth in Nile Tilapia,” Cells, vol. 11, no. 16, 2022.doi: 10.3390/cells11162504

H. N. Bhandari, B. Rimal, N. R. Pokhrel, R. Rimal, and K. R. Dahal, “LSTM-SDM: An integrated framework of LSTM implementation for sequential data modeling,” Software Impacts, vol. 14, 2022. doi: 10.1016/j.simpa.2022.100396.

H.N. Bhandari, B. Rimal, N. R. Pokhrel, R. Rimal, K.R. Dahal, &  R. K C Khatri. “Predicting stock market index using LSTM,” Machine Learning with Applications, 2022. doi: 10.1016/j.mlwa.2022.100320.

L. Cai, L. Bao, L. Rose, J. Summers, and W. Ding, “Malaria Modeling and Optimal Control Using Sterile Insect Technique and Insecticide-Treated Net,” Applicable Analysis, 2022.

L. Cai, L. Bao, L. Rose, J. Summers, and W. Ding, “Malaria modeling and optimal control using sterile insect technique and insecticide-treated net,” Applicable Analysis, vol. 101, no. 5, pp. 1715–1734, 2022. doi: 10.1080/00036811.2021.1999419.

S. Chen, J. Shi, Z. Shuai, and Y. Wu, “Global dynamics of a Lotka-Volterra competition patch model,” Nonlinearity, vol. 35, pp. 817–842, 2022. https://iopscience.iop.org/article/10.1088/1361-6544/ac3c2e/meta

S. Chen, J. Liu, and Y. Wu, “Invasion analysis of a two‐species Lotka–Volterra competition model in an advective patchy environment,” Studies in Applied Mathematics, vol. 149, no. 3, pp. 762–797, 2022. doi: 10.1111/sapm.12520.

S. Chen, J. Shi, S. Zhi, and Y. Wu, “Two novel proofs of spectral monotonicity of perturbed essentially nonnegative matrices with applications in population dynamics,” SIAM Journal on Applied Mathematics, vol. 82, no. 2, pp. 654–676, 2022. https://doi.org/10.1137/20M13452

C. Shen, W. Zhou, H. Zhang, and Y Yu, “Noise2Noise self-supervised deep learning holographic despeckling method,” published. [Online]. Available: https://www.spiedigitallibrary.org/conference-proceedings-of-spie/12318/123180D/Noise2Noise-self-supervised-deep-learning-holographic-despeckling-method/10.1117/12.2641871.short

E. Dohner, H. M. Terletska, K. Tam, J. Moreno, & H. F.  Fotso, et al., “Nonequilibrium DMFT+ CPA for correlated disordered systems,” Physical Review B, vol. 106, no. 19, p. 195156, 2022.

R. Dohner and S. J. Seo, “The NP-completeness of Redundant Open-Locating-Dominating Set,” arXiv, Cornell University, 2022. https://doi.org/https://arxiv-export-lb.library.cornell.edu/abs/2201.05252.

L. Duan, G. Huang, W.  Zhou, H. Zhang, & Y. Yu. “Vibration parameter detection based on digital holography,” 2022. https://ieeexplore.ieee.org/document/9949584

J. E. Farzidayeri and V. N. Bedekar, “Design of a V-Twin with Crank-Slider Mechanism Wind Energy Harvester Using Faraday’s Law of Electromagnetic Induction for Powering Small Scale Electronic Devices,” Energies, vol. 15, no. 17, 2022. doi: 10.3390/en15176215.

M. Faezipour, “Patient Safety Strategies in the Incidence of Ambulatory Care through Adverse Events,” Oct. 2022, published.

M. Faezipour, “Effects of Screen Time on Children from a Systems Engineering Perspective,” Published, Apr. 2022.

M. Faezipour, M. Faezipour, and B. Bauman, “Development of a Causal Model to Study the Disparate Effects of COVID-19 on Minorities,” in Proc. 2021 Int. Conf. Comput. Sci. Comput. Intell. (CSCI), 2022, pp. 1271–1274. doi: 10.1109/CSCI54926.2021.00259.

S. P. Graham, et al., “Georgia Distribution and Characterization of Species within the Eurycea quadridigitataComplex,” Southeastern Naturalist, 2022. doi: 10.1656/058.021.0207.

M. S. Grisnik, J. B. Grinath, J. P. Munafo, and D. M. Walker, “Functional Redundancy in Bat Microbial Assemblage in the Presence of the White Nose Pathogen,” Microb. Ecol., 2022. doi: 10.1007/s00248-022-02098-2.

W. He, A. M. Sainju, Z. Jiang, D. Yan, and Y. Zhou, “Earth imagery segmentation on terrain surface with limited training labels: A semi-supervised approach based on physics-guided graph co-training,” ACM Trans. Intell. Syst. Technol., vol. 13, no. 2, pp. 1–22, 2022. [Online]. Available: https://doi.org/10.1145/3508464

A. Hill, M. S. Grisnik, and D. M. Walker, “Bacterial skin assemblages of sympatric salamanders are primarily shaped by host genus,” Microb. Ecol., 2022. [Online]. Available: https://doi.org/10.1007/s00248-022-02127-0

A. Hill, M. S. Grisnik, and D. M. Walker, “Bacterial skin assemblages of sympatric salamanders are primarily shaped by host genus,” Microb. Ecol., 2022. [Online]. Available: https://doi.org/10.1007/s00248-022-02127-0

S. Iskakov, H. M. Terletska, and E. Gull, “Single- and two-particle finite size effects in interacting lattice systems,” Phys. Rev. B, vol. 106, no. 23, p. 235106, 2022. [Online]. Available: https://doi.org/10.1103/PhysRevB.106.235106

Z. Jiang et al., “Weakly Supervised Spatial Deep Learning for Earth Image Segmentation based on Imperfect Polyline Labels,” ACM Trans. Intell. Syst. Technol., vol. 13, no. 25, pp. 1–20, 2022. doi: 10.1145/3508464. 

M. Karabin et al., “Ab initio approaches to high-entropy alloys: a comparison of CPA, SQS, and supercell methods,” Journal of Materials Science, vol. 57, no. 23, pp. 10677–10690, 2022. 

J. Kubesch, R. Nave, A. Griffith, S. Cui, and G. Bates, “Economic outcomes for transitioning to organic forage production,” Crop Forage Turfgrass Manage, vol. 8, p. e220178, 2022.  https://doi.org/https://doi.org/10.1002/cft2.20178 (supervised graduate student as first author) . 

J. Kubesch et al., “Transitional organic forage systems in the U.S. Southeast: Production and nutritive value,” Agron. J., vol. 114, pp. 1269–1283, 2022. doi: 10.1002/agj2.21001. (supervised student as the first author)

R. N. Leander et al., “Optimal impulse control of West Nile Virus,” AIMS Math., vol. 7, no. 10, pp. 19597–19628, 2022. [Online]. Available: https://doi.org/10.3934/math.20221075

D. Leitner, P. Meleby, and L. Miao, “Recent advances in traffic signal performance evaluation,” J. Traffic Transp. Eng. (Engl. Ed.), 2022. [Online]. Available: https://doi.org/10.1016/j.jtte.2022.06.002

C. Li and T. Tsahai, “Skeletal based image processing for CNN based image classification,” Proc. 10th Int. Congr. Ind. Appl. Math., accepted.

Z. Li et al., “Optimizing wheat yield, water, and nitrogen use efficiency with water and nitrogen inputs in China: A synthesis and life cycle assessment,” Front. Plant Sci., vol. 13, p. 930484, 2022. [Online]. Available: https://doi.org/10.3389/fpls.2022.930484

Z. Li et al., “Productivity and nutritive value of no-input minimum tillage organic forage systems,” Nutr. Cycl. Agroecosyst., vol. 124, pp. 335–357, 2022.

Z. Li, D. Menefee, X. Yang, S. Cui, and N. Rajan, “Simulating productivity of dryland cotton using APSIM, climate scenario analysis, and remote sensing,” Agric. For. Meteorol., vol. 325, p. 109148, 2022. [Online]. Available: https://doi.org/10.1016/j.agrformet.2022.109148  (corresponding author)

W. Liu, M. N. Ellingham, and D. Ye, “Minimal quadrangulations of surfaces,” J. Combin. Theory Ser. B, vol. 157, pp. 235–262, 2022. [Online]. Available:  https://doi.org/https://doi.org/10.1016/j.jctb.2022.06.003

P. J. MacDougall, “ACS Comment: Only YOU can prevent truth decay!,” Chem. Eng. News, vol. 100, no. 16, p. 45, May 6, 2022. [Online]. Available: https://cen.acs.org/acs-news/comment/ACS-Comment-prevent-truth-decay/100/i16

E. Madadian, J. Rahimi, M. Mohebbi, and D. Simakov, “Grape pomace as an energy source for the food industry: A thermochemical and kinetic analysis,” Food Bioprod. Process., vol. 132, pp. 177–187, 2022, doi: 10.1016/j.fbp.2022.01.006.

V. A. Manathunga and D. Zhu, “Unearned premium risk and machine learning techniques,” Front. Appl. Math. Stat., vol. 118, 2022. doi: 10.3389/fams.2022.1056529.

B. D. E. McNiven et al., “One- and two-particle properties of the weakly interacting two-dimensional Hubbard model in proximity to the van Hove singularity,” Phys. Rev. B, vol. 106, no. 3, p. 035145, 2022. doi: 10.1103/PhysRevB.106.035145.

P. Meleby, L. Miao, and C. Winfrey, “Development of a Traffic Signal Performance Ranking Online Database for the State of Tennessee,” in Proc. Int. Conf. Transp. Dev., 2022. doi: 10.1061/9780784484326.013.

D. Menefee, N. Rajan, and S. Cui, “Modeling Carbon Uptake of Dryland Maize using High Resolution Satellite Imagery,” Front. Remote Sens., vol. 3, p. 810030, 2022.

L. Miao and H. Zhang, “Wireless secret sharing game between two legitimate users and an eavesdropper,” in Proc. 8th Int. Conf. Networks Commun. (NWCOM), Sep. 2022. [Online]. Available: https://aircconline.com/csit/papers/vol12/csit121603.pdf

J. Miller, S. Naderi, C. B. Mullinax, and J. L. Phillips, “Attention is not enough,” in Proc. 44th Annu. Meeting Cogn. Sci. Soc., [Online]. Available: https://escholarship.org/uc/item/30x346n8

M. Mohebbi, F. Rajabipour, and E. Madadian, “A framework for identifying the host phases in coal-derived fly ash,” Fuel, vol. 314, 2022. [Online]. Available: https://doi.org/10.1016/j.fuel.2021.122806

H. G. Momm et al., “Integrated surface and groundwater modeling to enhance water resource sustainability in agricultural watersheds,” Agric. Water Manage., vol. 269, 2022. [Online]. Available: https://doi.org/10.1016/j.agwat.2022.107692

N. R. Pokhrel et al., “Predicting NEPSE index price using deep learning models,” Mach. Learn. Appl., vol. 9, 2022. doi: 10.1016/j.mlwa.2022.100385.

S. Pourreza, M. Faezipour, and M. Faezipour, “Eye-SCOR: A Supply Chain Operations Reference-Based Framework for Smart Eye Status Monitoring Using System Dynamics Modeling,” Sustainability, vol. 14, no. 14. doi: 10.3390/su14148876.

F. Raji and L. Miao, “Optimal Wireless Rate and Power Control in the Presence of Jammers Using Reinforcement Learning,” ITU J. Future Evol. Technol., vol. 3, no. 2, pp. 508–522, 2022. doi: 10.52953/ANSC4385.

J. Ranganathan and T. Tsahai, “Sentiment Analysis of Tweets using Deep Learning,” in Proc. 2022, p. 13725. doi: 10.1007/978-3-031-22064-7_9.

W. M. Robertson, “Acoustic waveguide demultiplexer based on Fano resonance: Experiment and simulation,” AIP Adv., vol. 12, p. 045018, 2022. doi: 10.1063/5.0087034.

W. M. Robertson, “Acoustic ring resonator: Experiments and simulations,” AIP Adv., vol. 12, p. 015006, 2022. doi: 10.1063/5.0077330.

A. S. Romer, J. B. Grinath, K. C. Moe, and D. M. Walker, “Host microbiome responses to the Snake Fungal Disease pathogen (Ophidiomyces ophidiicola) are driven by changes in microbial richness,” Sci. Rep., 2022. doi: 10.1038/s41598-022-07042-5.

M. S. Salem, R. F. Al Tobasei, A. Ali, and B. Kenney, “Integrated Analyses of DNA Methylation and Gene Expression of Rainbow Trout Muscle under Variable Ploidy and Muscle Atrophy Conditions,” Genes, vol. 13, no. 7, 2022. doi: 10.3390/genes13071151.

S. J. Seo and D. Jean, “Extremal Cubic Graphs for Fault-tolerant Locating Domination,” Theor. Comput. Sci., vol. 917, pp. 94–106, 2022. [Online]. Available: https://doi.org/https://doi.org/10.1016/j.tcs.2022.03.014

S. J. Seo and D. Jean, “Fault-Tolerant Detection Systems on the King’s Grid,” Preprints.org, MDPI, 2022

S. J. Seo and D. Jean, “Fault-tolerant Locating-Dominating Sets on the Infinite King Grid,” arXiv, Cornell Univ., 2022. [Online]. Available: https://doi.org/arXiv:2201.09399.

M. Swindall et al., “Dataset Augmentation in Papyrology with Generative Models: A Study of Synthetic Ancient Greek Character Images,” Proc. 31st Int. Joint Conf. Artif. Intell. AI and Arts, pp. 4973–4979, 2022. doi: 10.24963/ijcai.2022/689.

D. Wang, Q. Wu, and D. Hong, “Extracting Default Mode Network Based on Graph Neural Network for Resting State fMRI Study,” Front. Neuroimaging, 2022. doi: 10.3389/fnimg.2022.963125.

D. Wang, D. Hong, and Q. Wu, “Attention Deficit Hyperactivity Disorder Classification Based on Deep Learning,” IEEE/ACM Trans. Comput. Biol. Bioinform., 2022. doi: 10.1109/TCBB.2022.3170527.

D. Wang, D. Hong, and Q. Wu, “Prediction of Loan Rate for Mortgage Data: Deep Learning versus Robust Regression,” Comput. Econ., 2022. doi: 10.1007/s10614-022-10239-5.

J. Wei, J. Zou, J. Li, Z. Li, and X. Yang, “Non-contact Heart Rate Detection Based on Fusion Method of Visible Images and Infrared Images,” Artif. Intell. Secur., pp. 62–75, 2022.

G. T. West, M. Ogden, and J. F. Wallin, “GA Galaxy: Interacting galaxies model fitter,” Astrophys. Source Code Libr., 2022. [Online]. Available: https://github.com/gtw2i/GA- Galaxy.

L. Xiong et al., “Determine the Undervalued US Major League Baseball Players with Machine Learning,” Int. J. Innov. Technol. Explor. Eng., vol. 12, no. 3, pp. 17–24, 2022. [Online]. Available: https://doi.org/10.35940/ijitee.B9406.0212323.

L. Xiong, “Predictive Modeling for Transportation Security Administration Claims Data,” ANWESH: Int. J. Manag. Inf. Technol., vol. 7, no. 2, pp. 10–20, 2022.L

L. Xiong and S. D. Williams, “Generalized Linear Model for Predicting the Credit Card Default Payment Risk,” Adv. Sci. Technol. Eng. Syst. J., Special Issue on Innovation in Computing, Engineering Science & Technology, 2022. [Online]. Available: https://doi.org/10.25046/aj070306.

L. Xiong and D. Hong, “A Solvency Assessment and Prediction Framework for Workers’ Compensation Captive Insurance Companies,” J. Insur. Issues, vol. 45, no. 2, pp. 82–113, 2022. [Online]. Available: https://www.jstor.org/stable/48703228.

S. Xu, C. Zhang, and D. Hong, “BERT-Based NLP Techniques for Classification and Severity Modeling in Basic Warranty Data Study,” Insur. Math. Econ., vol. 107, pp. 57–67, 2022. [Online]. Available: https://doi.org/10.1016/j.insmatheco.2022.07.013.

X. Yang, N. Zhang, and P. Schrader, “A Study of Brain Networks for Autism Spectrum Disorder Classification using Resting-State Functional Connectivity,” *Mach. Learn. Appl.*, Published.

X. Yang, R. Rimal, and T. Rogers, “Functional Connectivity Based Classification for Autism Spectrum Disorder Using Spearman’s Rank Correlation,” in 2022 IEEE-EMBS Conf. Biomed. Eng. Sci. (IECBES), pp. 46–51, 2022. [Online]. Available: https://doi.org/10.1109/iecbes54088.2022.10079445.

A. Yinusa, M. Faezipour, and M. Faezipour, “A Study on CKD Progression and Health Disparities Using System Dynamics Modeling,” *Healthcare*, vol. 10, no. 9. [Online]. Available: https://doi.org/10.3390/healthcare10091628

H. Zhang, “Control Goals of Whole-Body Coordination During Quiet Upright Stance,” published. [Online]. Available: https://link.springer.com/chapter/10.1007/978-3-031-21704-3_25

H. Zhang, J. Zhong, and W. Zhou, “Novel Temporal Weed Classification with Laser,” published. [Online]. Available: https://opg.optica.org/abstract.cfm?uri=LS-2022-JW5B.51

Y. Zhang, M. Du, F. Zhuang, Y. Jin, Y. Ma, and H. Zhang, “Joint Alignment and Compactness Learning for Multi-Source Unsupervised Domain Adaptation,” published. [Online]. Available: https://www.spiedigitallibrary.org/conference-proceedings-of-spie/12084/120841E/Joint-alignment-and-compactness-learning-for-multi-source-unsupervised-domain/10.1117/12.2623645.short

W. Zhou et al., “Study of Crack Growth of Transparent Materials Subjected to Laser Irradiation by Digital Holography,” *Appl. Sci.*, 2022. [Online]. Available: https://www.mdpi.com/2076-3417/12/15/7799

W. Zhou et al., “Elimination of the quadratic phase term in digital holographic microscopy by using transport of intensity,” *Front. Photonics*, 2022. [Online]. Available: https://www.frontiersin.org/articles/10.3389/fphot.2022.848453/full

2021

R. F. Al-Tobasei, A. Ali, L. S. Garcia Andre, D. Lourenco, T. Leeds, and M. Salem. “Genomic Predictions for Fillet Yield and Firmness in Rainbow Trout Using Reduced-Density SNP Panels,” BMC Genomics, vol. 22, no. 1, 2021. doi: 10.1186/s12864-021-07404-9.

H. Alrammah and Y. Gu, “Workflow Scheduling in Clouds Using Pareto Dominance for Makespan, Cost and Energy,” in Proc. IEEE Int. Conf. Commun. (ICC), 2021. doi: 10.1109/ICC42927.2021.9500489

T. A. Biala and A. Q. M. Khaliq, “A Fractional-Order Compartmental Model for the Spread of the COVID-19 Pandemic,” Communications in Nonlinear Science and Numerical Simulation, vol. 98, Jul. 2021. doi: 10.1016/j.cnsns.2021.105764.

C. Castillo, H. G. Momm, R. R. Wells, R. L. Bingner, and R. Pérez, “A GIS Focal Approach for Characterizing Gully Geometry,” Earth Surface Processes and Landforms, vol. 46, no. 9, 2021. doi: 10.1002/esp.5122.

X. Cui, T. Goff,S. Cui, D. Menefee, et al. “Predicting Carbon and Water Vapor Fluxes Using Machine Learning and Novel Feature Ranking Algorithms,” Science of The Total Environment, vol. 775, Jun. 2021. doi: 10.1016/j.scitotenv.2021.145130.

M. N. Ellingham, S. Shan, D. Ye, and X. Zha, “Toughness and Spanning Trees in K4‐minor‐free Graphs,” Journal of Graph Theory, vol. 96, no. 3, 2021. doi: 10.1002/jgt.22620.

M. Faezipour and M. Faezipour, “System Dynamics Modeling for Smartphone-Based Healthcare Tools: Case Study on ECG Monitoring,” IEEE Syst. J., vol. 15, no. 2, 2021. doi: 10.1109/JSYST.2020.3009187.

M. Faezipour and M. Faezipour, “Efficacy of Smart EEG Monitoring Amidst the COVID-19 Pandemic,” Electronics, vol. 10, no. 9, 2021. doi: 10.3390/electronics10091001.

M. Faezipour and M. Faezipour, “Smart Healthcare Monitoring Apps with a Flavor of Systems Engineering,” in Smart and Sustainable Engineering for Next Generation Applications, 2021. doi: 10.1007/978-3-030-71051-4_48.

K. Fernando and V. Manathunga, “An Alternative Approach to Evaluate American Options Price Using HJM Approach,” Sep. 2021.

N. Gerstenschlager, et al., “Double Demonstration Lessons: Authentically Participating in an Inquiry Stance,” Math. Teach. Educ., vol. 9, no. 2, 2021. doi: 10.5951/MTE.2020.0048.

A. Hunsaker, W. B. Goodwin, E. Rowe, C. Wheeler, L. Matei, V. Buliga, and A. Burger, “Ceramic Cs₂HfCl₆: A novel scintillation material for use in gamma ray spectroscopy,” Cryst. Res. Technol., vol. 56, no. 9, 2021. [Online]. Available: https://doi.org/10.1002/crat.202000166

R. S. Jones and H. G. Momm, “An Index for Quantifying Geometric Point Disorder in Geospatial Applications,” Computers & Geosciences, vol. 151, Jun. 2021. doi: 10.1016/j.cageo.2021.104756.

R. N. Leander, Y. Wu, W. Ding, D. E. Nelson, and Z. Sinkala, “A model of the innate immune response to SARS-CoV-2 in the alveolar epithelium,” R. Soc. Open Sci., vol. 8, no. 8, 2021. [Online]. Available: https://doi.org/10.1098/rsos.210090

C. Lewis, E. Proynov, J. Yu, and J. Kong, “Analyzing cases of significant nondynamic correlation with DFT using the atomic populations of effectively localized electrons,” unpublished, Sep. 2021.

Y. Li, Z. Li, S. Cui, G. Liang, and Q. Zhang, “Microbial-derived carbon components are critical for enhancing soil organic carbon in no-tillage croplands: A global perspective,” Soil Tillage Res., vol. 205, Jan. 2021. [Online]. Available: https://doi.org/10.1016/j.still.2020.104758

Z. Li, J. Zou, P. Yan, and D. Hong, “Non-contact real-time monitoring of driver’s physiological parameters under ambient light condition,” Intell. Autom. Soft Comput., vol. 28, no. 3, 2021. [Online]. Available: https://doi.org/10.32604/iasc.2021.016516

R. Liu, M. Rolek, D. C. Stephens, D. Ye, and G. Yu, “Connectivity for kite-linked graphs,” SIAM J. Discrete Math., vol. 35, no. 1, 2021. [Online]. Available:https://doi.org/10.1137/19M130282X

Y. Liu and J. Chen, “Non-Parametric Analysis of Interval-Censored Survival Data with Application to a Phase III Metastatic Colorectal Cancer Clinical Trial,” Biomed. Stat. Inf., vol. 6, no. 1, 2021, doi: 10.11648/j.bsi.20210601.13.

Y. Liu, J. Plott, and Y. Huang, “Sieve Estimation for Mixture Cure Rate Model with Informatively Interval-Censored Failure Time Data,” Am. J. Theor. Appl. Stat., vol. 10, no. 3, 2021, doi: 10.11648/j.ajtas.20211003.15.

Y. Liu and H. Li, “A Semiparametric Mixture Cure Model for Partly Interval Censored Failure Time Data,” J. Stat. Appl. Probab., vol. 10, no. 1, 2021, doi: 10.18576/jsap/100101. 

E. Luquin et al., “Model prediction capacity of ephemeral gully evolution in conservation tillage systems,” Earth Surf. Process. Landforms, vol. 46, no. 10, 2021. [Online]. Available: https://doi.org/10.1002/esp.5134

E. Madadian, J. B. Haelssig, M. Mohebbi, and M. Pegg, “From Biorefinery Landfills towards a Sustainable Circular Bioeconomy: A Techno-Economic and Environmental Analysis in Atlantic Canada,” J. Clean. Prod., vol. 296, May 2021, doi: 10.1016/j.jclepro.2021.126590.

D. Menefee et al., “Simulation of Dryland Maize Growth and Evapotranspiration Using DSSAT‐CERES‐Maize Model,” Agron. J., vol. 113, no. 2, 2021. doi: 10.1002/agj2.20524.

L. Miao and D. Leitner, “Adaptive traffic light control with quality-of-service provisioning for connected and automated vehicles at isolated intersections,” IEEE Access, vol. 9, 2021. [Online]. Available: https://doi.org/10.1109/ACCESS.2021.3064310

H. G. Momm et al., “Integrated technology for evaluation and assessment of multi-scale hydrological systems in managing nonpoint source pollution,” Water, vol. 13, no. 6, 2021. [Online]. Available: https://doi.org/10.3390/w13060842

M. Noroozi, R. Rimal, and M. Pensky, “Estimation and Clustering in Popularity Adjusted Block Model,” Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol. 83, no. 2, 2021. doi: 10.1111/rssb.12410.

N. Omatu and J. L. Phillips, “Benefits of Combining Dimensional Attention and Working Memory for Partially Observable Reinforcement Learning Problems,” in Proceedings of the 2021 ACM Southeast Conference, New York, NY, USA: ACM, 2021. doi: 10.1145/3409334.3452072.

E. Proynov and J. Kong, “Correcting the Charge Delocalization Error of Density Functional Theory,” J. Chem. Theory Comput., vol. 17, no. 8, 2021. doi: 10.1021/acs.jctc.1c00197.

S. J. Seo, “Fault-Tolerant Detectors for Distinguishing Sets in Cubic Graphs,” Discrete Appl. Math., vol. 293, Apr. 2021. [Online]. Available: https://doi.org/10.1016/j.dam.2021.01.008.

M. I. Swindall et al., “Exploring Learning Approaches for Ancient Greek Character Recognition with Citizen Science Data,” Proc. IEEE 17th Int. Conf. eScience, 2021.

E. V. Taguas, R. L. Bingner, H. G. Momm, R. Wells, and M. A. Locke, “Modelling Scenarios of Soil Properties and Managements in Olive Groves at the Micro-Catchment Scale with the AnnAGNPS Model to Quantify Organic Carbon,” CATENA, vol. 203, Aug. 2021. doi: 10.1016/j.catena.2021.105333.

K.-M. Tam et al., “Application of the Locally Self-Consistent Embedding Approach to the Anderson Model with Non-Uniform Random Distributions,” Annals of Physics, Apr. 2021. doi: 10.1016/j.aop.2021.168480.

H. Terletska et al., “Non-Local Corrections to the Typical Medium Theory of Anderson Localization,” Annals of Physics, Mar. 2021. doi: 10.1016/j.aop.2021.168454.

H. Terletska, S. Iskakov, T. Maier, and E. Gull, “Dynamical Cluster Approximation Study of Electron Localization in the Extended Hubbard Model,” Phys. Rev. B, vol. 104, no. 8, 2021. doi: 10.1103/PhysRevB.104.085129.

Y. Wang and J. Kong, “Efficient Spherical Surface Integration of Gauss Functions in Three-Dimensional Spherical Coordinates and the Solution for the Modified Bessel Function of the First Kind,” J. Math. Chem., vol. 59, no. 2, 2021. doi: 10.1007/s10910-020-01204-4.

Y. Wang, E. Proynov, and J. Kong, “Model DFT Exchange Holes and the Exact Exchange Hole: Similarities and Differences,” J. Chem. Phys., vol. 154, no. 2, 2021. doi: 10.1063/5.0031995.

J. Weatherly, P. Macchi, and A. Volkov, “On the Calculation of the Electrostatic Potential, Electric Field and Electric Field Gradient from the Aspherical Pseudoatom Model. II. Evaluation of the Properties in an Infinite Crystal,” Acta Crystallogr. Sect. A Found. Adv., vol. 77, no. 5, 2021. [Online]. Available: https://doi.org/10.1107/S2053273321005532.

A. Weh et al., “Dynamical Mean-Field Theory of the Anderson-Hubbard Model with Local and Nonlocal Disorder in Tensor Formulation,” Phys. Rev. B, vol. 104, no. 4, 2021. [Online]. Available: https://doi.org/10.1103/PhysRevB.104.045127.

J. C. Willingham, A. T. Barlow, D. C. Stephens, A. E. Lischka, and K. S. Hartland, “Mindset Regarding Mathematical Ability in K‐12 Teachers,” Sch. Sci. Math., vol. 121, no. 4, 2021. [Online]. Available: https://doi.org/10.1111/ssm.12466.

Y. Xu and Y. Liu, “Bias Adjustment Methods for Analysis of a Non-Randomized Controlled Trials of Right Heart Catheterization for Patients in ICU,” Biomed. Stat. Inf., vol. 6, no. 2, 2021. [Online]. Available: https://doi.org/10.11648/j.bsi.20210602.12.

J. Zou, N. Zhu, B. Ge, and D. Hong, “Elderly Fall Detection Based on Improved SSD Algorithm,” *J. New Media*, vol. 3, no. 1, 2021. [Online]. Available: https://doi.org/10.32604/jnm.2021.017763

2020

A. Ali, R. F. Al-Tobasei, D. Lourenco, T. Leeds, B. Kenney, and M. Salem 2020a. “Genome-Wide Scan for Common Variants Associated with Intramuscular Fat and Moisture Content in Rainbow Trout,” BMC Genomics, vol. 21, no. 1, 2020. doi: 10.1186/s12864-020-06932-0

A. Ali, R. F. Al-Tobasei, D. Lourenco, T. Leeds, B. Kenney, and M. Salem. 2020b. “Genome-Wide Identification of Loci Associated with Growth in Rainbow Trout,” BMC Genomics, vol. 21, no. 1, 2020. doi: 10.1186/s12864-020-6617-x

H. Alrammah, Y. Gu, and Z. Liu, “Tri-Objective Workflow Scheduling and Optimization in Heterogeneous Cloud Environments,” in Proc. IEEE Int. Parallel Distrib. Process. Symp. Workshops (IPDPSW), 2020. doi: 10.1109/IPDPSW50202.2020.00129.

A. Alshehri, J. Ford, and R. Leander, “The Impact of Maturation Time Distributions on the Structure and Growth of Cellular Populations,” Math. Biosci. Eng., vol. 17, no. 2, 2020. doi: 10.3934/mbe.2020098

M. Bagheri, K. T. Hemant, L. M. Anarina, R. F. Al-Tobasei, et al. “A Lipidome-Wide Association Study of the Lipoprotein Insulin Resistance Index,” Lipids in Health and Disease, vol. 19, no. 1, 2020. doi: 10.1186/s12944-020-01321-8.

S. Barbosa, “Using Holographically Compressed Embeddings in Question Answering,” in Computer Science & Information Technology (CS & IT), AIRCC Publishing Corporation, 2020. [Online]. Available: https://doi.org/10.5121/csit.2020.100919

P. Chapagain, D. M. Walker, T. Leeds, B. M. Cleveland, and M. Salem. 2020. “Distinct Microbial Assemblages Associated with Genetic Selection for High- and Low- Muscle Yield in Rainbow Trout,” BMC Genomics, vol. 21, no. 1, 2020. doi: 10.1186/s12864-020-07204-7.

S. Chen, J. Shi, Z. Shuai, and Y. Wu, “Asymptotic Profiles of the Steady States for an SIS Epidemic Patch Model with Asymmetric Connectivity Matrix,” Journal of Mathematical Biology, vol. 80, no. 7, 2020. doi: 10.1007/s00285-020-01497-8.

K. Deng, G. F. Webb, and Y. Wu, “Analysis of Age and Spatially Dependent Population Model: Application to Forest Growth,” Nonlinear Analysis: Real World Applications, vol. 56, Dec. 2020. doi: 10.1016/j.nonrwa.2020.103164.

M. Faezipour and M. Faezipour, “Sustainable Smartphone-Based Healthcare Systems: A Systems Engineering Approach to Assess the Efficacy of Respiratory Monitoring Apps,” Sustainability, vol. 12, no. 12, 2020. doi: 10.3390/su12125061.

W. E. Fitzgibbon, J. J. Morgan, G. F. Webb, and Y. Wu, “Analysis of a Reaction–Diffusion Epidemic Model with Asymptomatic Transmission,” J. Biol. Syst., vol. 28, no. 3, 2020. doi: 10.1142/S0218339020500126.

W. E. Fitzgibbon, J. J. Morgan, G. F. Webb, and Y. Wu, “Modelling the Aqueous Transport of an Infectious Pathogen in Regional Communities: Application to the Cholera Outbreak in Haiti,” J. R. Soc. Interface, vol. 17, no. 169, 2020. doi: 10.1098/rsif.2020.0429.

W. Fitzgibbon, J. Morgan, G. Webb, and Y. Wu, “Maritime Transport and the Threat of Bio Invasion and the Spread of Infectious Disease,” in Computational Methods in Applied Sciences, 2020. doi: 10.1007/978-3-030-37752-6_5.

A. Grajal-Puche, et al., “Microbial Assemblage Dynamics Within the American Alligator Nesting Ecosystem: A Comparative Approach Across Ecological Scales,” Microb. Ecol., vol. 80, no. 3, 2020. doi: 10.1007/s00248-020-01522-9.

M. Grisnik, et al,“The Cutaneous Microbiota of Bats Has in Vitro Antifungal Activity against the White Nose Pathogen,” FEMS Microbiol. Ecol., vol. 96, no. 2, 2020. doi: 10.1093/femsec/fiz193.

Y. Gu and C. Budati, “Energy-Aware Workflow Scheduling and Optimization in Clouds Using Bat Algorithm,” Future Gener. Comput. Syst., vol. 113, Dec. 2020. doi: 10.1016/j.future.2020.06.031.

E. Győri, M. D. Plummer, D. Ye, and X. Zha, “Cycle Traversability for Claw-Free Graphs and Polyhedral Maps,” Combinatorica, vol. 40, no. 3, 2020. doi: 10.1007/s00493-019-4042-z.

S. I. Haruna, S. H. Anderson, R. P. Udawatta, C. J. Gantzer, N. C. Phillips, S. Cui, and Y. Gao, “Improving soil physical properties through the use of cover crops: A review,” Agrosyst. Geosci. Environ., vol. 3, no. 1, 2020. [Online]. Available: https://doi.org/10.1002/agg2.20105

K. Kazmi and A. Q. M. Khaliq, “An Efficient Split-Step Method for Distributed-Order Space-Fractional Reaction-Diffusion Equations with Time-Dependent Boundary Conditions,” Applied Numerical Mathematics, vol. 147, Jan. 2020. doi: 10.1016/j.apnum.2019.08.019. 

N. Khan and J. Phillips, “Combined Model for Sensory-Based and Feedback-Based Task Switching: Solving Hierarchical Reinforcement Learning Problems Statically and Dynamically with Transfer Learning,” in Proc. IEEE ICTAI, 2020. doi: 10.1109/ICTAI50040.2020.00055. 

Y. Li et al., “Residue retention promotes soil carbon accumulation in minimum tillage systems: Implications for conservation agriculture,” Sci. Total Environ., vol. 740, Oct. 2020. [Online]. Available: https://doi.org/10.1016/j.scitotenv.2020.140147

Y. Li, Z. Li, S. Cui, and Q. Zhang, “Trade-off between soil pH, bulk density and other soil physical properties under global no-tillage agriculture,” Geoderma, vol. 361, Mar. 2020. [Online]. Available: https://doi.org/10.1016/j.geoderma.2019.114099

Z. Li et al., “Determining effects of water and nitrogen inputs on wheat yield and water productivity and nitrogen use efficiency in China: A quantitative synthesis,” Agric. Water Manag., vol. 242, Dec. 2020. [Online]. Available: https://doi.org/10.1016/j.agwat.2020.106397

A. E. Lischka and D. C. Stephens, “The area model: Building mathematical connections,” Math. Teach. Learn. Teach. PK–12, vol. 113, no. 3, 2020. [Online]. Available: https://doi.org/10.5951/MTLT.2019.0115

Y. Liu, T. Hu, and J. Sun, “Regression Analysis of Interval-Censored Failure Time Data with Cured Subgroup and Mismeasured Covariates,” Commun. Stat. Theory Methods, vol. 49, no. 1, 2020, doi: 10.1080/03610926.2018.1535075.

Y. Liu, “Extended Bayesian Framework for Multicategory Support Vector Machine,” J. Stat. Appl. Probab., vol. 9, no. 1, 2020, doi: 10.18576/jsap/090101. 

P. Magal, G. F. Webb, and Y. Wu, “Spatial Spread of Epidemic Diseases in Geographical Settings: Seasonal Influenza Epidemics in Puerto Rico,” Discrete Contin. Dyn. Syst. B, vol. 25, no. 6, 2020, doi: 10.3934/dcdsb.2019223.

D. Menefee et al., “Carbon Exchange of a Dryland Cotton Field and Its Relationship with PlanetScope Remote Sensing Data,” Agric. For. Meteorol., vol. 294, Nov. 2020. doi: 10.1016/j.agrformet.2020.108130.

H. G. Momm, R. ElKadiri, and W. Porter, “Crop-type classification for long-term modeling: An integrated remote sensing and machine learning approach,” Remote Sens., vol. 12, no. 3, 2020. [Online]. Available: https://doi.org/10.3390/rs12030449

D. Nguyen, P. Macchi, and A. Volkov, “Fast Analytical Evaluation of Intermolecular Electrostatic Interaction Energies Using the Pseudoatom Representation of the Electron Density. III. Application to Crystal Structures via the Ewald and Direct Summation Methods,” Acta Crystallographica Section A Foundations and Advances, vol. 76, no. 6, 2020. doi: 10.1107/S2053273320009584.

M. Ogden, G. West, J. Wallin, Z. Sinkala, and W. Smith, “Optimizing Numerical Simulations of Colliding Galaxies. II. Comparing Simulations to Astronomical Observations,” Research Notes of the AAS, vol. 4, no. 138, 2020.

A. Östlin, Y. Zhang, H. Terletska, F. Beiuşeanu, V. Popescu, K. Byczuk, L. Vitos, M. Jarrell, D. Vollhardt, and L. Chioncel, “Ab Initio Typical Medium Theory of Substitutional Disorder,” Physical Review B, vol. 101, no. 1, 2020. doi: 10.1103/PhysRevB.101.014210.

M. D. Plummer, D. Ye, and X. Zha, “Dominating Maximal Outerplane Graphs and Hamiltonian Plane Triangulations,” Discrete Appl. Math., vol. 282, Aug. 2020. doi: 10.1016/j.dam.2019.12.003.

J. Ranganathan, S. Sharma, and A. A. Tzacheva, “Hybrid Scalable Action Rule,” in Proc. 4th Int. Conf. Compute Data Anal., ACM, 2020. doi: 10.1145/3388142.3388143.

J. Ranganathan and A. A. Tzacheva, “Emotion Mining from Text for Actionable Recommendations Detailed Survey,” Int. J. Data Min. Model. Manag., vol. 12, no. 2, 2020. doi: 10.1504/IJDMMM.2020.106729.

W. M. Robertson, S. M. Wright, A. Friedli, J. Summers, and A. Kaszynski, “Design and Characterization of an Ultra-Low-Cost 3D-Printed Optical Sensor Based on Bloch Surface Wave Resonance,” Biosens. Bioelectron. X, vol. 5, Sep. 2020. doi: 10.1016/j.biosx.2020.100049.

I. O. Sarumi, K. M. Furati, and A. Q. M. Khaliq, “Highly Accurate Global Padé Approximations of Generalized Mittag–Leffler Function and Its Inverse,” J. Sci. Comput., vol. 82, no. 2, 2020. doi: 10.1007/s10915-020-01150-y.

J. Shi, Y. Wu, and X. Zou, “Coexistence of Competing Species for Intermediate Dispersal Rates in a Reaction–Diffusion Chemostat Model,” J. Dyn. Differ. Equ., vol. 32, no. 2, 2020. [Online]. Available: https://doi.org/10.1007/s10884-019-09763-0.

S. D. Snyder, W. B. Sutton, and D. M. Walker, “Prevalence of Ophidiomyces Ophiodiicola, the Causative Agent of Snake Fungal Disease, in the Interior Plateau Ecoregion of Tennessee, USA,” Journal of Wildlife Diseases, vol. 56, no. 4, 2020. doi: 10.7589/2019-04-109.

Y. Sun et al., “Driver Fatigue Detection System Based on Colored and Infrared Eye Features Fusion,” Computers, Materials & Continua, vol. 63, no. 3, 2020. doi: 10.32604/cmc.2020.09763.

I. Syzonenko and J. L. Phillips, “Accelerated Protein Folding Using Greedy-Proximal A*,” J. Chem. Inf. Model., vol. 60, no. 6, 2020. doi: 10.1021/acs.jcim.9b01194.

A. A. Tzacheva, J. Ranganathan, and A. Bagavathi, “Action Rules for Sentiment Analysis Using Twitter,” Int. J. Soc. Netw. Min., vol. 3, no. 1, 2020. doi: 10.1504/IJSNM.2020.105728.

A. Tzacheva, J. Ranganathan, and S. Y. Mylavarapu, “Actionable Pattern Discovery for Tweet Emotions,” in Adv. Intell. Syst. Comput., pp. 46–57, 2020. doi: 10.1007/978-3-030-20454-9_5.

D. M. Walker et al., “Variation in the Slimy Salamander (Plethodon Spp.) Skin and Gut-Microbial Assemblages Is Explained by Geographic Distance and Host Affinity,” Microbial Ecology, vol. 79, no. 4, 2020. doi: 10.1007/s00248-019-01456-x.

G. West, M. Ogden, J. Wallin, Z. Sinkala, and W. Smith, “Optimizing Numerical Simulations of Colliding Galaxies. I. Fitness Functions and Optimization Algorithms,” Res. Notes AAS, vol. 4, p. 136, 2020.

A. Williams and J. Phillips, “Transfer Reinforcement Learning Using Output-Gated Working Memory,” in Proc. AAAI Conf. Artif. Intell., vol. 34, no. 02, 2020. [Online]. Available: https://doi.org/10.1609/aaai.v34i02.5488.

Y. Wu and D. Ye, “Minimum $T$-Joins and Signed-Circuit Covering,” SIAM J. Discrete Math., vol. 34, no. 2, 2020. [Online]. Available: https://doi.org/10.1137/18M1226105.

L. Xiong and D. Hong, “Using Monte Carlo Simulation to Predict Captive Insurance Solvency,” in Proc. 2020 4th Int. Conf. Compute Data Anal., New York, NY, USA: ACM, 2020. [Online]. Available: https://doi.org/10.1145/3388142.3388171.

S. Xu, S. E. Barbosa, and D. Hong, “BERT Feature Based Model for Predicting the Helpfulness Scores of Online Customers Reviews,” in Lecture Notes in Computer Science, 2020. [Online]. Available: https://doi.org/10.1007/978-3-030-39442-4_21.

X. Yang et al., “Cropping System Productivity and Evapotranspiration in the Semiarid Loess Plateau of China under Future Temperature and Precipitation Changes: An APSIM-Based Analysis of Rotational vs. Continuous Systems,” *Agric. Water Manag.*, vol. 229, Feb. 2020. [Online]. Available: https://doi.org/10.1016/j.agwat.2019.105959.

Y. Liu and Y. Huang, “Semiparametric Likelihood Estimation with Clayton-Oakes Model for Multivariate Current Status Data,” *J. Biostat. Biometr.*, 2020. [Online]. Available: https://doi.org/10.29011/JBSB-109.100009.

L. Zhang, Y. Lu, R. Luo, D. Ye, and S. Zhang, “Edge Coloring of Signed Graphs,” *Discrete Appl. Math.*, vol. 282, Aug. 2020. [Online]. Available: https://doi.org/10.1016/j.dam.2019.12.004

2019

A. Ali, R. F. Al-Tobasei, D. Lourenco, T. Leeds, B. Kenney, and M. Salem. “Genome-Wide Association Study Identifies Genomic Loci Affecting Filet Firmness and Protein Content in Rainbow Trout,” Front. Genet., vol. 10, May 2019. doi: 10.3389/fgene.2019.00386

S. Alzahrani and A. Q. M. Khaliq, “Fourier Spectral Exponential Time Differencing Methods for Multi-Dimensional Space-Fractional Reaction–Diffusion Equations,” J. Comput. Appl. Math., vol. 361, Dec. 2019. doi: 10.1016/j.cam.2019.04.001.

A. G. Bratsos and A. Q. M. Khaliq, “An Exponential Time Differencing Method of Lines for Burgers–Fisher and Coupled Burgers Equations,” Journal of Computational and Applied Mathematics, vol. 356, Aug. 2019. doi: 10.1016/j.cam.2019.01.028.

P. Chapagain, B. Arivett, B. M. Cleveland, D. M. Walker, and M. Salem. “Analysis of the Fecal Microbiota of Fast- and Slow-Growing Rainbow Trout (Oncorhynchus Mykiss),” BMC Genomics, vol. 20, no. 1, 2019. doi: 10.1186/s12864-019-6175-2.

S. Chen, J. Shi, Z. Shuai, and Y. Wu, “Spectral Monotonicity of Perturbed Quasi-Positive Matrices with Applications in Population Dynamics,” Nov. 2019.

K.A. Connors, A. Beasley, M. G. Barron, S. E. Belanger, M. Bonnell, J. L. Brill, J.L. Phillips, et al. 2019. “Creation of a Curated Aquatic Toxicology Database: EnviroTox.” Environmental Toxicology and Chemistry 38 (5). https://doi.org/10.1002/etc.4382.

S. Cui et al., “Machine Learning-Based Microarray Analyses Indicate Low-Expression Genes Might Collectively Influence PAH Disease,” PLOS Computational Biology, vol. 15, no. 8, 2019. doi: 10.1371/journal.pcbi.1007264.

W. Ding et al., “Experience and Lessons Learned from Using SIMIODE Modeling Scenarios,” PRIMUS, vol. 29, no. 6, 2019. doi: 10.1080/10511970.2018.1488318. 

W. E. Fitzgibbon, J. J. Morgan, G. F. Webb, and Y. Wu, “Spatial Models of Vector-Host Epidemics with Directed Movement of Vectors over Long Distances,” Math. Biosci., vol. 312, Jun. 2019. doi: 10.1016/j.mbs.2019.04.003.

J. P. Gulizia, K. M. Downs, and S. Cui, “Kudzu (Pueraria montanavar. lobata) Age Variability Effects on Total and Nutrient-Specific in Situ Rumen Degradation,” J. Appl. Anim. Res., vol. 47, no. 1, 2019. doi: 10.1080/09712119.2019.1652615.

J. Gulizia, K. Downs, and S. Cui, “The Influence of Kudzu (Pueraria montanavar. lobata) Age on in Situ Rumen Degradation,” J. Anim. Sci., vol. 97, Suppl. 1, 2019. doi: 10.1093/jas/skz053.194.

J. He, E. Wei, D. Ye, and S. Zhai, “On perfect matchings in matching covered graphs,” J. Graph Theory, vol. 90, no. 4, 2019. [Online]. Available: https://doi.org/10.1002/jgt.22411

D. Hong, “On scattered data representations using bivariate splines,” in Handbook of Analytic-Computational Methods in Applied Mathematics, G. Anastassiou, Ed. Chapman and Hall/CRC, 2019. [Online]. Available: https://doi.org/10.1201/9780429123610

C. Li, M. Hains, J. Wallin, and Q. Wu, “Applying data science methods for early prediction of undergraduate student retention,” in Proc. 2019 Int. Conf. Comput. Sci. Comput. Intell. (CSCI), 2019. [Online]. Available: https://doi.org/10.1109/CSCI49370.2019.00250

C. Li, E. Imeokparia, M. Ketzner, and T. Tsahai, “Teaching the NAO robot to play a human-robot interactive game,” in Proc. 2019 Int. Conf. Comput. Sci. Comput. Intell. (CSCI), 2019. [Online]. Available: https://doi.org/10.1109/CSCI49370.2019.00134

Y. Li, S. Cui, S. X. Chang, and Q. Zhang, “Liming effects on soil pH and crop yield depend on lime material type, application method and rate, and crop species: A global meta-analysis,” J. Soils Sediments, vol. 19, no. 3, 2019. [Online]. Available: https://doi.org/10.1007/s11368-018-2120-2

Y. Li et al., “A global synthesis of the effect of water and nitrogen input on maize (Zea mays) yield, water productivity and nitrogen use efficiency,” Agric. For. Meteorol., vol. 268, Apr. 2019. [Online]. Available: https://doi.org/10.1016/j.agrformet.2019.01.018

Y. Li et al., “Residue retention and minimum tillage improve physical environment of the soil in croplands: A global meta-analysis,” Soil Tillage Res., vol. 194, Nov. 2019. [Online]. Available: https://doi.org/10.1016/j.still.2019.06.009

J. Liang, J. Zou, and D. Hong, “Non-Gaussian penalized PARAFAC analysis for fMRI data,” Front. Appl. Math. Stat., vol. 5, Aug. 2019. [Online]. doi.org/10.3389/fams.2019.00040

Y. Liu et al., “Quadrangular Embeddings of Complete Graphs and the Even Map Color Theorem,” J. Comb. Theory Ser. B, vol. 139, Nov. 2019, doi: 10.1016/j.jctb.2019.02.006.

Y. Liu, “Data Augmentation and Bayesian Methods for Multicategory Support Vector Machines,” Int. J. Data Sci. Anal., vol. 5, no. 3, 2019, doi: 10.11648/j.ijdsa.20190503.12.

P. Magal, G. F. Webb, and Y. Wu, “A Spatial Model of Honey Bee Colony Collapse Due to Pesticide Contamination of Foraging Bees,” Bull. Math. Biol., vol. 81, pp. 4908–4931, 2019.

P. Magal, G. F. Webb, and Y. Wu, “On the Basic Reproduction Number of Reaction-Diffusion Epidemic Models,” SIAM J. Appl. Math., vol. 79, no. 1, 2019, doi: 10.1137/18M1182243.

D. S. Maynard et al., “Consistent Trade-Offs in Fungal Trait Expression across Broad Spatial Scales,” Nat. Microbiol., vol. 4, no. 5, 2019. doi: 10.1038/s41564-019-0361-5.

F. Miao, Y. Li, S. Cui, S. Jagadamma, G. Yang, and Q. Zhang, “Soil extracellular enzyme activities under long-term fertilization management in the croplands of China: A meta-analysis,” Nutrient Cycling in Agroecosystems, vol. 114, no. 2, 2019. [Online]. Available: https://doi.org/10.1007/s10705-019-09991-2

A. Minoshima et al., “Pathogenicity and taxonomy of Tenuignomonia styracis gen. et sp. nov., a new monotypic genus of Gnomoniaceae on Styrax obassia in Japan,” Mycoscience, vol. 60, no. 1, 2019. [Online]. Available: https://doi.org/10.1016/j.myc.2018.08.001

H. G. Momm et al., “Enhanced field-scale characterization for watershed erosion assessments,” Environ. Model. Softw., vol. 117, Jul. 2019. [Online]. Available: https://doi.org/10.1016/j.envsoft.2019.03.025

H. G. Momm et al., “Evaluation of sediment load reduction by natural riparian vegetation in the Goodwin Creek Watershed,” Trans. ASABE, vol. 62, no. 5, 2019. [Online]. Available: https://doi.org/10.13031/trans.13492

H. G. Momm et al., “Crop conversion impacts on runoff and sediment loads in the Upper Sunflower River Watershed,” Agric. Water Manage., vol. 217, May 2019. [Online]. Available: https://doi.org/10.1016/j.agwat.2019.03.012

S. P. Morton, J. B. Phillips, and J. L. Phillips, “The molecular basis of pH-modulated HIV Gp120 binding revealed,” Evol. Bioinform., vol. 15, Jan. 2019. [Online]. Available: https://doi.org/10.1177/1176934319831308

D. Nguyen and A. Volkov, “Fast Analytical Evaluation of Intermolecular Electrostatic Interaction Energies Using the Pseudoatom Representation of the Electron Density. II. The Fourier Transform Method,” Acta Crystallographica Section A Foundations and Advances, vol. 75, no. 3, 2019. doi: 10.1107/S2053273319002535.

M. Noroozi, M. Pensky, and R. Rimal, “Sparse Popularity Adjusted Stochastic Block Model,” Oct. 2019.

J. Paki, H. Terletska, S. Iskakov, and E. Gull, “Charge Order and Antiferromagnetism in the Extended Hubbard Model,” Phys. Rev. B, vol. 99, no. 24, 2019. doi: 10.1103/PhysRevB.99.245146.

T. Pirtle, L. Rumble, M. Klug, F. Walker, S. Cui, and N. Phillips, “Impact of Biochar and Different Nitrogen Sources on Forage Radish Production in Middle Tennessee,” J. Adv. Agric., vol. 10, Jan. 2019. doi: 10.24297/jaa.v10i0.8035.

K. N. Poudel and W. M. Robertson, “Bloch Surface Wave Excitation Using a Maximum Length Sequence Grating Structure,” in Opt. Components Mater. XVI, SPIE, 2019. doi: 10.1117/12.2508184.

C. Qin, R. R. Wells, H. G. Momm, X. Xu, G. V. Wilson, and F. Zheng, “Photogrammetric Analysis Tools for Channel Widening Quantification under Laboratory Conditions,” Soil Tillage Res., vol. 191, Aug. 2019. doi: 10.1016/j.still.2019.04.002.

J. Ranganathan and A. Tzacheva, “Emotion Mining in Social Media Data,” Procedia Comput. Sci., vol. 159, 2019. doi: 10.1016/j.procs.2019.09.160.

V. Reshniak, A. Khaliq, and D. Voss, “Slow-Scale Split-Step Tau-Leap Method for Stiff Stochastic Chemical Systems,” J. Comput. Appl. Math., vol. 361, Dec. 2019. doi: 10.1016/j.cam.2019.03.044.

R. Rimal and M. Pensky, “Density Deconvolution with Small Berkson Errors,” Math. Methods Stat., vol. 28, no. 3, 2019. doi: 10.3103/S1066530719030025.

W. M. Robertson, I. Shirk, and E. Campbell, “Acoustic Waveguide Impedance Matching via Helmholtz Resonator Mediated Extraordinary Acoustic Transmission,” AIP Adv., vol. 9, no. 3, 2019. doi: 10.1063/1.5083906.

E. Rowe et al., “Preparation, Structure and Scintillation of Cesium Hafnium Chloride Bromide Crystals,” J. Cryst. Growth, vol. 509, Mar. 2019. doi: 10.1016/j.jcrysgro.2018.08.033.

S. Sharma, N. Rajan, S. Cui, S. Maas, K. Casey, S. Ale, and R. Jessup, “Carbon and Evapotranspiration Dynamics of a Non-Native Perennial Grass with Biofuel Potential in the Southern U.S. Great Plains,” Agric. For. Meteorol., vol. 269–270, May 2019. [Online]. Available: https://doi.org/10.1016/j.agrformet.2019.01.037.

A. Tzacheva, J. Ranganathan, and R. Jadi, “Multi-Label Emotion Mining From Student Comments,” in Proc. 2019 Int. Conf. Inf. Educ. Innov. (ICIEI), 2019. doi: 10.1145/3345094.3345112.

P. R. Varadwaj, A. Varadwaj, H. M. Marques, and P. J. MacDougall, “The Chalcogen Bond: Can It Be Formed by Oxygen?,” Phys. Chem. Chem. Phys., vol. 21, no. 36, 2019. doi: 10.1039/C9CP03783G.

D. M. Walker et al., “Variability in Snake Skin Microbial Assemblages across Spatial Scales and Disease States,” The ISME Journal, vol. 13, no. 9, pp. 2209–2222, 2019. doi: 10.1038/s41396-019-0416-x.

M. Wang et al., “Performance of New Density Functionals of Nondynamic Correlation on Chemical Properties,” J. Chem. Phys., vol. 150, no. 20, 2019. doi: 10.1063/1.5082745.

Y. Liu, “A Signal Detection Analysis of World Health Organization’s Pharmacovigilance Database,” *Int. J. Clin. Biostat. Biom.*, vol. 5, no. 2, 2019. [Online]. Available: https://doi.org/10.23937/2469-5831/1510023

S. Zhai, E. Wei, J. He, and D. Ye, “Homeomorphically Irreducible Spanning Trees in Hexangulations of Surfaces,” *Discrete Math.*, vol. 342, no. 10, 2019. [Online]. Available: https://doi.org/10.1016/j.disc.2019.01.032

Y. Zhang et al., “Locally Self-Consistent Embedding Approach for Disordered Electronic Systems,” *Phys. Rev. B*, vol. 100, no. 5, 2019. [Online]. Available: https://doi.org/10.1103/PhysRevB.100.054205

J. Zou, Z. Li, Z. Guo, and D. Hong, “Super-Resolution Reconstruction of Images Based on Microarray Camera,” *Comput. Mater. Continua*, vol. 60, no. 1, 2019. [Online]. Available: https://doi.org/10.32604/cmc.2019.05795

2018

A. Ali, R. F. Al-Tobasei, B. Kenney, T. D. Leeds, and M. Salem. “Integrated Analysis of LncRNA and MRNA Expression in Rainbow Trout Families Showing Variation in Muscle Growth and Fillet Quality Traits,” Scientific Reports, vol. 8, no. 1, 2018. doi: 10.1038/s41598-018-30655-8.

A. T. Barlow, N. E. Gerstenschlager, J. F. Strayer, A. E. Lischka, D.C. Stephens, K. S. Hartland, and J. C. Willingham. “Scaffolding for Access to Productive Struggle,” Mathematics Teaching in the Middle School, vol. 23, no. 4, 2018. doi: 10.5951/mathteacmiddscho.23.4.0202.

A.T. Barlow, A. E. Lischka, J. C. Willingham, K. Hartland, and D. C. Stephens. “The Relationship of Implicit Theories to Elementary Teachers’ Patterns of Engagement in a Mathematics-Focused Professional Development Setting,” Mid-Western Educational Researcher, vol. 30, no. 3, pp. 93–122, 2018

E. Brown, Z. D. Fleischman, L. D. Merkle, E. Rowe, A. Burger, S. A. Payne, and M. Dubinskii. 2018. “Optical Spectroscopy of Holmium Doped K2LaCl5.” Journal of Luminescence196 (April). https://doi.org/10.1016/j.jlumin.2017.12.040.

E. Brown, Z. D. Fleischman, L. D. Merkle, E. Rowe, A. Burger, S. Payne, and M. Dubinskiy. “Infrared Absorption and Fluorescence Properties of Holmium Doped Potassium Lanthanum Chloride (Conference Presentation),” in Laser Technology for Defense and Security XIV, SPIE, 2018. doi: 10.1117/12.2309578.

M. Faezipour and S. Ferreira, “A System Dynamics Approach for Sustainable Water Management in Hospitals,” IEEE Syst. J., vol. 12, no. 2, 2018. doi: 10.1109/JSYST.2016.2573141.

M. Grisnik et al., “Host and Geographic Range of Snake Fungal Disease in Tennessee, USA,” Herpetol. Rev., vol. 49, pp. 682–690, Oct. 2018.

A. J. Hill et al., “Common cutaneous bacteria isolated from snakes inhibit growth of Ophidiomyces ophiodiicola,” EcoHealth, vol. 15, no. 1, 2018. [Online]. Available: https://doi.org/10.1007/s10393-017-1289-y

A. J. Hill et al., “Common cutaneous bacteria isolated from snakes inhibit growth of Ophidiomyces ophiodiicola,” EcoHealth, vol. 15, no. 1, 2018. [Online]. Available: https://doi.org/10.1007/s10393-017-1289-y

S. Iskakov, H. M. Terletska, and E. Gull, “Momentum-space cluster dual-fermion method,” Phys. Rev. B, vol. 97, no. 12, 2018. [Online]. Available: https://doi.org/10.1103/PhysRevB.97.125114

S. N. Jator and V. Manathunga, “Block Nyström type integrator for Bratu’s equation,” J. Comput. Appl. Math., vol. 327, Jan. 2018. [Online]. Available: https://doi.org/10.1016/j.cam.2017.06.025

M. Jovanovich and J. Phillips, “N-Task Learning: Solving Multiple or Unknown Numbers of Reinforcement Learning Problems,” Cognitive Science, 2018. 

L. Kang et al., “Circuit Decompositions and Shortest Circuit Coverings of Hypergraphs,” Graphs and Combinatorics, vol. 34, no. 2, 2018. doi: 10.1007/s00373-018-1881-0. 

V. H. Khiabani, A. S. Nasab, and V. N. Bedekar, “An Experimental Adaptive Teaching Practice,” in Proc. Int. Annu. Conf. Amer. Soc. Eng. Manage., 2018.

R. N. Leander, W. Ding, and R. A. Salinas, “Dedication to Suzanne Lenhart,” Nat. Resour. Model., vol. 31, no. 4, 2018. [Online]. Available: https://doi.org/10.1111/nrm.12198

Q. Li, W. C. Shiu, P. K. Sun, and D. Ye, “On the anti-Kekulé problem of cubic graphs,” Art Discrete Appl. Math., vol. 2, no. 1, 2018. [Online]. Available: https://doi.org/10.26493/2590-9770.1264.94b

Z. Li et al., “In search of long-term sustainable tillage and straw mulching practices for a maize-winter wheat-soybean rotation system in the Loess Plateau of China,” Field Crops Res., vol. 217, Mar. 2018. [Online]. Available: https://doi.org/10.1016/j.fcr.2017.08.021

Z. Li et al., “Developing sustainable cropping systems by integrating crop rotation with conservation tillage practices on the Loess Plateau, a long-term imperative,” Field Crops Res., vol. 222, Jun. 2018. [Online]. Available: https://doi.org/10.1016/j.fcr.2018.03.027

A. E. Lischka, N. E. Gerstenschlager, D. C. Stephens, J. F. Strayer, and A. T. Barlow, “Making room for inspecting mistakes,” Math. Teach., vol. 111, no. 6, 2018. [Online]. Available: https://doi.org/10.5951/mathteacher.111.6.0432

P. Magal, G. F. Webb, and Y. Wu, “On a Vector-Host Epidemic Model with Spatial Structure,” Nonlinearity, vol. 31, pp. 5589–5614, Feb. 2018, doi: 10.1088/1361-6544/aae1e0.

E. W. Malone et al., “Which Species, How Many, and from Where: Integrating Habitat Suitability, Population Genomics, and Abundance Estimates into Species Reintroduction Planning,” Glob. Change Biol., vol. 24, no. 8, 2018. doi: 10.1111/gcb.14126.

H. G. Momm, R. R. Wells, and S. J. Bennett, “Disaggregating soil erosion processes within an evolving experimental landscape,” Earth Surf. Process. Landf., vol. 43, no. 2, 2018. [Online]. Available: https://doi.org/10.1002/esp.4268

S. P. Morton, J. Howton, and J. L. Phillips, “Sub-class differences of pH-dependent HIV GP120-CD4 interactions,” in Proc. 2018 ACM Int. Conf. Bioinf., Comput. Biol., Health Inform., 2018. [Online]. Available: https://doi.org/10.1002/esp.4268

D. Nguyen, Z. Kisiel, and A. Volkov, “Fast Analytical Evaluation of Intermolecular Electrostatic Interaction Energies Using the Pseudoatom Representation of the Electron Density. I. The Löwdin α-Function Method,” Acta Crystallographica Section A Foundations and Advances, vol. 74, no. 5, 2018. doi: 10.1107/S2053273318008690.

B. Paneru, A. Ali, R. Al-Tobasei, B. Kenney, and M. Salem, “Crosstalk among LncRNAs, MicroRNAs and MRNAs in the Muscle ‘Degradome’ of Rainbow Trout,” Sci. Rep., vol. 8, no. 1, 2018. doi: 10.1038/s41598-018-26753-2.

J. L. Phillips, M. E. Colvin, and S. Newsam, “Dimensionality Estimation of Protein Dynamics Using Polymer Models,” in Proc. ACM Int. Conf. Bioinformatics, Comput. Biol., Health Informatics, 2018. doi: 10.1145/3233547.3233713.

J. Ranganathan, N. Hedge, A. S. Irudayaraj, and A. A. Tzacheva, “Automatic Detection of Emotions in Twitter Data,” in Proc. Workshop on Opinion Mining, Summarization and Diversification, ACM, 2018.doi: 10.1145/3301020.3303751.

J. Ranganathan, A. S. Irudayaraj, A. Bagavathi, and A. A. Tzacheva, “Actionable Pattern Discovery for Sentiment Analysis on Twitter Data in Clustered Environment,” J. Intell. Fuzzy Syst., vol. 34, no. 5, 2018. doi: 10.3233/JIFS-169472.

M. Salem et al., “Genome-Wide Association Analysis With a 50K Transcribed Gene SNP-Chip Identifies QTL Affecting Muscle Yield in Rainbow Trout,” Front. Genet., vol. 9, Sep. 2018. doi: 10.3389/fgene.2018.00387.

A. Schulman and S. Barbosa, “Text Genre Classification Using Only Parts of Speech,” in Proc. 2018 Int. Conf. Comput. Sci. Comput. Intell. (CSCI), 2018. doi: 10.1109/CSCI46756.2018.00236.

L. A. Shuttleworth, D. I. Guest, and D. M. Walker, “The Fungus, the Code and the Mysterious Publication Date: Why Gnomoniopsis Smithogilvyi Is Still the Correct Name for the Chestnut Rot Fungus,” IMA Fungus, vol. 9, no. 2, 2018. [Online]. Available: https://doi.org/10.1007/BF03449443.

I. Syzonenko and J. L. Phillips, “Hybrid Spectral/Subspace Clustering of Molecular Dynamics Simulations,” Proc. 2018 ACM Int. Conf. Bioinformatics, Comput. Biol., Health Informatics, 2018. doi: 10.1145/3233547.3233595.

P. Tanguay et al., “QPCR Quantification of Ophiognomonia Clavigignenti-Juglandacearum from Infected Butternut Trees under Different Release Treatments,” Forest Pathology, vol. 48, no. 3, 2018. doi: 10.1111/efp.12418.

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Research Groups

The Faculty in the Computational Science Program at MTSU have a diverse set of research interests that cross between traditional departmental boundaries. The groups below outline some of the core research interests of our faculty. In some cases, faculty straddle two or more of the areas below. However, for simplicity, faculty are only associated with a single group on this page.