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  1. Misinformation in online spaces can stoke mistrust of established media, misinform the public and lead to radicalization. Hence, multiple automated algorithms for misinformation detection have been proposed in the recent past. However, the fairness (e.g., performance across left- and right- leaning news articles) of these algorithms has been repeatedly questioned, leading to decreased trust in such systems. This work motivates and grounds the need for an audit of machine learning based misinformation detection algorithms and possible ways to mitigate bias (if found). Using a large (N>100K) corpus of news articles, we report that multiple standard machine learning based misinformation detection approaches are susceptible to bias. Further, we find that an intuitive post-processing approach (Reject Option Classifier) can reduce bias while maintaining high accuracy in the above setting. The results pave the way for accurate yet fair misinformation detection algorithms. 
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  2. The prediction of Secondary Organic Aerosol (SOA) in regional scales is traditionally performed by using gas-particle partitioning models. In the presence of inorganic salted wet aerosols, aqueous reactions of semivolatile organic compounds can also significantly contribute to SOA formation. The UNIfied Partitioning-Aerosol phase Reaction (UNIPAR) model utilizes the explicit gas mechanism to better predict SOA formation from multiphase reactions of hydrocarbons. In this work, the UNIPAR model was incorporated with the Comprehensive Air Quality Model with Extensions (CAMx) to predict the ambient concentration of organic matter (OM) in urban atmospheres during the Korean-United States Air Quality (2016 KORUS-AQ) campaign. The SOA mass predicted with the CAMx-UNIPAR model changed with varying levels of humidity and emissions and in turn, has the potential to improve the accuracy of OM simulations. The CAMx-UNIPAR model significantly improved the simulation of SOA formation under the wet condition, which often occurred during the KORUS-AQ campaign, through the consideration of aqueous reactions of reactive organic species and gas-aqueous partitioning. The contribution of aromatic SOA to total OM was significant during the low-level transport/haze period (24-31 May 2016) because aromatic oxygenated products are hydrophilic and reactive in aqueous aerosols. The OM mass predicted with the CAMx-UNIPAR model was compared with that predicted with the CAMx model integrated with the conventional two product model (SOAP). Based on estimated statistical parameters to predict OM mass, the performance of CAMx-UNIPAR was noticeably better than the conventional CAMx model although both SOA models underestimated OM compared to observed values, possibly due to missing precursor hydrocarbons such as sesquiterpenes, alkanes, and intermediate VOCs. The CAMx-UNIPAR model simulation suggested that in the urban areas of South Korea, terpene and anthropogenic emissions significantly contribute to SOA formation while isoprene SOA minimally impacts SOA formation. 
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  3. null (Ed.)
    In engineering, students’ completion of prerequisites indicates an understanding of fundamental knowledge. Recent studies have shown a significant relationship between student performance and prior knowledge. Weak knowledge retention from prerequisite coursework can present challenges in progressive learning. This study investigates the relationship between prior knowledge and students’ performance over a few courses of Statics. Statistics has been considered as the subject of interest since it is the introductory engineering course upon which many subsequent engineering courses rely, including many engineering analysis and design courses. The prior knowledge was determined based on the quantitative and qualitative preparedness. A quiz set was designed to assess quantitative preparedness. The qualitative preparedness was assessed using a survey asking students’ subjective opinions about their preparedness at the beginning of the semester. Student performance was later quantified through final course grades. Each set of data were assigned three categories for grouping purposes to reflect preparedness: 1) high preparedness: 85% or higher score, 2) medium preparedness: between 60% and 85%, and 3) weak preparedness: 60% or lower. Pearson correlation coefficient and T-test was conducted on 129 students for linear regression and differences in means. The analysis revealed a non-significant correlation between the qualitative preparedness and final scores (p-value = 0.29). The data revealed that students underestimated their understanding of the prerequisites for the class, since the quantitative preparedness scores were relatively higher than the qualitative preparedness scores. This can be partially understood by the time gap between when prerequisites were taken and when the course under investigation was taken. Students may have felt less confident at first but were able to pick up the required knowledge quickly. A moderately significant correlation between students’ quantitative preparedness and course performance was observed (p -value < 0.05). Students with high preparedness showed > 80% final scores, with a few exceptions; students with weak preparedness also showed relatively high final scores. However, most of the less prepared students made significant efforts to overcome their weaknesses through continuous communication and follow-up with the instructor. Despite these efforts, these students could not obtain higher than 90% as final scores, which indicates that level of preparedness reflects academic excellence. Overall, this study highlights the role of prior knowledge in achieving academic excellence for engineering. The study is useful to Civil Engineering instructors to understand the role of students’ previous knowledge in their understanding of difficult engineering concepts. 
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  4. null (Ed.)
  5. Heparin is an essential anticoagulant used for treating and preventing thrombosis. However, the complexity of heparin has hindered the development of a recombinant source, making its supply dependent on a vulnerable animal population. In nature, heparin is produced exclusively in mast cells, which are not suitable for commercial production, but mastocytoma cells are readily grown in culture and make heparan sulfate, a closely related glycosaminoglycan that lacks anticoagulant activity. Using gene expression profiling of mast cells as a guide, a multiplex genome engineering strategy was devised to produce heparan sulfate with high anticoagulant potency and to eliminate contaminating chondroitin sulfate from mastocytoma cells. The heparan sulfate purified from engineered cells grown in chemically defined medium has anticoagulant potency that exceeds porcinederived heparin and confers anticoagulant activity to the blood of healthy mice. This work demonstrates the feasibility of producing recombinant heparin from mammalian cell culture as an alternative to animal sources. 
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  6. null (Ed.)
    Monitoring water quality by detecting chemical and biological contaminants is critical to ensuring the provision and discharge of clean water, hence protecting human health and the ecosystem. Among the available analytical techniques, infrared (IR) spectroscopy provides sensitive and selective detection of multiple water contaminants. In this work, we present an application of IR spectroscopy for qualitative and quantitative assessment of chemical and biological water contaminants. We focus on in-line detection of nitrogen pollutants in the form of nitrate and ammonium for wastewater treatment process control and automation. We discuss the effects of water quality parameters such as salinity, pH, and temperature on the IR spectra of nitrogen pollutants. We then focus on application of the sensor for detection of contaminants of emerging concern, such as arsenic and Per- and polyfluoroalkyl substances (PFAS) in drinking water. We demonstrate the use of multivariate statistical analysis for automated data processing in complex fluids. Finally, we discuss application of IR spectroscopy for detecting biological water contaminants. We use the metabolomic signature of E. coli bacteria to determine its presence in water as well as distinguish between different strains of bacteria. Overall, this work shows that IR spectroscopy is a promising technique for monitoring both chemical and biological contaminants in water and has the potential for real-time, inline water quality monitoring. 
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