‘Is it wise to open up America again now?’ Learning from government responses to COVID-19 and economy reopen points in three Asia-Pacific countries - Yu, Jia; Pang, Yulei (British Journal of Healthcare Management, 2020)
With the continuously increasing number of new confirmed COVID-19 cases, many health experts worried about the possibility of a ‘second wave’ outbreak, which might cause more deaths and hit economies even worse. This article looks at the experiences of fighting COVID-19 from three Asia-Pacific countries and discusses whether it is a wise decision to open up America again at this time.
Keywords: dept.eco; dept.mat; peer-reviewed

Comparing the performance on the MI and SAT/PSAT for the purpose of monitoring student achievement - McDonald, Michael; Pang, Yulei (International Journal of Information and Education Technology, 2021)
This paper will discuss the correlation between the SAT and the Math Inventory Test. Many school districts adopted the Math Inventory as a tool to measure student growth from grades kindergarten through high school. The Math Inventory is a computer-administered test that gives students math problems spanning from counting to high school level math. When completed, the students are given a quantile measure, much like a Lexile score for reading skill. The purpose of this study is to figure out if success on the Math Inventory is a good indicator for performing well on the SAT. For most high schools around the United States, objectives and lessons are aligned with those of the SAT. The goal of high school teachers is for students to excel on the SAT so that they can go to college, which means the tests used in middle school should be aligned with that goal. If the Math Inventory is not, then it might not be a very good use of school time and resources. Data was analyzed from the 2017-2018 school year from ten different high schools in an urban school district to determine the correlation between Math Inventory score, and the math score/sub scores of SAT/PSAT. The value of the Pearson’s correlation coefficient is used to suggest a fairly moderate positive relationship between these two variables. © 2021, International Journal of Information and Education Technology. All rights reserved.
Keywords: dept.mat; peer-reviewed

Comparing Machine Learning Approaches for Predicting Spatially Explicit Life Cycle Global Warming and Eutrophication Impacts from Corn Production - Romeiko, Xiaobo Xue; Guo, Zhijian; Pang, Yulei; Lee, Eun Kyung; Zhang, Xuesong (Sustainability, 2020)
Agriculture ranks as one of the top contributors to global warming and nutrient pollution. Quantifying life cycle environmental impacts from agricultural production serves as a scientific foundation for forming effective remediation strategies. However, methods capable of accurately and efficiently calculating spatially explicit life cycle global warming (GW) and eutrophication (EU) impacts at the county scale over a geographic region are lacking. The objective of this study was to determine the most efficient and accurate model for estimating spatially explicit life cycle GW and EU impacts at the county scale, with corn production in the U.S.’s Midwest region as a case study. This study compared the predictive accuracies and efficiencies of five distinct supervised machine learning (ML) algorithms, testing various sample sizes and feature selections. The results indicated that the gradient boosting regression tree model built with approximately 4000 records of monthly weather features yielded the highest predictive accuracy with cross-validation (CV) values of 0.8 for the life cycle GW impacts. The gradient boosting regression tree model built with nearly 6000 records of monthly weather features showed the highest predictive accuracy with CV values of 0.87 for the life cycle EU impacts based on all modeling scenarios. Moreover, predictive accuracy was improved at the cost of simulation time. The gradient boosting regression tree model required the longest training time. ML algorithms demonstrated to be one million times faster than the traditional process-based model with high predictive accuracy. This indicates that ML can serve as an alternative surrogate of process-based models to estimate life-cycle environmental impacts, capturing large geographic areas and timeframes.
Keywords: Agriculture; Eutrophication; Global warming; Life cycle assessment; Machine learning; Midwest; Spatial assessment; United States; Zea mays; agricultural production; agriculture; crop production; dept.mat; eutrophication; global warming; life cycle analysis; life cycle assessment; machine learning; peer-reviewed; prediction; spatial analysis; spatial assessment

MaPP Challenge : Giving a Taste of Contemporary Mathematics Research to Secondary Students in a Puzzle‑Based Outreach Program - Lee, Younhee; Carrigan, Braxton; Clontz, Steven; Couch, P.; Jones, Cadavious; Noble, Abby; Owens, Andrew; Pang, Yulei (Mathematics Outreach: Examples and Impact from Across the Globe, 2026)
Keywords: Community‑driven; Event (particle physics); Focus (optics); Focus group; Outreach; Perception; Taste; contemporary mathematics; dept.mat; fostering positive mathematical identity; inclusive mathematical outreach program; puzzle‑ based outreach

CT-free attenuation correction for dedicated cardiac SPECT using a 3D dual squeeze-and-excitation residual dense network. - Chen, Xiongchao; Zhou, Bo; Shi, Luyao; Liu, Hui; Pang, Yulei; Wang, Rui; Miller, Edward J.; Sinusas, Albert J.; Liu, Chi (Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology, 2021)
Background: Attenuation correction (AC) using CT transmission scanning enables the accurate quantitative analysis of dedicated cardiac SPECT. However, AC is challenging for SPECT-only scanners. We developed a deep learning-based approach to generate synthetic AC images from SPECT images without AC. Methods: CT-free AC was implemented using our customized Dual Squeeze-and-Excitation Residual Dense Network (DuRDN). 172 anonymized clinical hybrid SPECT/CT stress/rest myocardial perfusion studies were used in training, validation, and testing. Additional body mass index (BMI), gender, and scatter-window information were encoded as channel-wise input to further improve the network performance. Results: Quantitative and qualitative analysis based on image voxels and 17-segment polar map showed the potential of our approach to generate consistent SPECT AC images. Our customized DuRDN showed superior performance to conventional network design such as U-Net. The averaged voxel-wise normalized mean square error (NMSE) between the predicted AC images by DuRDN and the ground-truth AC images was 2.01 ± 1.01%, as compared to 2.23 ± 1.20% by U-Net. Conclusions: Our customized DuRDN facilitates dedicated cardiac SPECT AC without CT scanning. DuRDN can efficiently incorporate additional patient information and may achieve better performance compared to conventional U-Net. © 2021, American Society of Nuclear Cardiology.
Keywords: adult; article; body mass; controlled study; deep learning; dept.mat; excitation; female; gender; human; male; myocardial perfusion imaging; patient information; peer-reviewed; physiological stress; qualitative analysis; quantitative analysis; single photon emission computed tomography; x-ray computed tomography