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Item 148. Exploring the Impact of College Students' COVID-19- and Capitol Insurrection-Related Horizontal and Vertical Collectivism/Individualism on Emotional Reaction to Those Events(Elsevier, 2022) Sorge, Brandon H.; Fore, Grant; Williamson, Francesca; Angstmann, Julia; Hensel, Devon J.; Engineering Technology, School of Engineering and TechnologyPurpose: While many studies have explored individuals’ feelings related to recent national events, none have explored the relationship of individualism and collectivist leanings caused by these events on the individuals emotions related to those events. For this research we specifically focus on the COVID-19 Pandemic and the January 6 Capitol Insurrection. Methods: Data were collected from college students at a small, private midwestern private university over a 10-day period at the end of January and the beginning of February 2021. A Qualtrics survey was sent to 1,041 students who had completed a similar survey 5 months earlier related to their feelings about the COVID-19 pandemic. We used a subsample (N=314 students; 74.2% female; 83.4% White; 0.6% freshman, 24.5% sophomores, 34.7% juniors and 29.3% seniors) who provided complete data. Measures included horizontal (“We are the same, high freedom, equality”) and vertical (“I am different, Authority ranking, high freedom”) individualism as well as horizontal (“We are the same, share, less freedom”) and vertical (“I am different, sharing, authority ranking”) collectivism. Participants also provided data on the positive and negative affective responses to COVID-19 and to the January 6 Capitol Insurrection. Structural equation modeling was used to investigate the direct effects between individual and collectivism and the affective responses to each event (all standardized; Stata v. 17.0). Global fit was evaluated using the chi-square test and the root mean square error of approximation (RMSEA). Local fit was addressed using the Comparative Fit Index (CFI) and the Tucker Louis Index (TLI). We also investigated group differences by gender (male/female) and race (minority/white) where significant overall direct effects were observed. Results: Fit indices (Chi-sq[df]: 60.99[31], p<.001; RMSEA[90% CI]: 0.046[0.035-0.076); CFI: 0.972; TLI: 0.905) suggested the specified model provided a good fit to the data. Higher COVID VI was associated with higher positive (B=0.12) and negative (B=0.15) affective reactions to COVID (B=0.12). Higher Capitol HI and HC were both associated with higher positive (both: B=0.21) and higher negative (B=0.12-0.23) affective reaction to the capitol riots. Higher COVID VI was associated with lower negative affective response (B=-0.16) to COVID. We observed no gender or race/ethnicity differences in these significant effects. Conclusions: Students who felt more strongly that people were the same (horizontal individualism and horizontal collectivism) were more likely to have both strong positive and negative emotions to the Janury 6th insurrection. For COVID-19 negative feelings, students whose feelings towards COVID were more individualistic had mixed results. Those who believed people are different (vertical individualism) were more likely to have lower negative feelings towards COVID-19 while those who believed people are the same (horizontal individualism) had greater negative feelings. These data have implications for scaffolding young adult support in advance of future socio-political emergencies.Item Analysis of the Flow Performance of the Complex Cross-Section Module to Reduce the Sedimentation in a Combined Sewer Pipe(MDPI, 2020-11) Ji, Hyon Wook; Yoo, Sung Soo; Koo, Dan Daehyun; Kang, Jeong-Hee; Engineering Technology, School of Engineering and TechnologyThe difference in the amount of stormwater and sewage in a combined sewer system is significantly large in areas where heavy rainfall is concentrated. This leads to a low water level and slow flow velocity inside the pipes, which causes sedimentation and odor on non-rainy days. A complex cross-section module increases the flow velocity by creating a small waterway inside the pipe for sewage to flow on non-rainy days. While considering Manning’s equation, we applied the principle where the flow velocity is proportional to two-thirds of the power of the hydraulic radius. The flow velocity of a circular pipe with a diameter of 450 mm and the corresponding complex cross-section module was analyzed by applying Manning’s equation and numerical modeling to show the effects of the complex cross-section module. The tractive force was compared based on a lab-scale experiment. When all conditions were identical except for the cross-sectional shape, the average flow velocity of the complex cross-section module was 14% higher while the size of the transported sand grains was up to 0.5 mm larger. This increase in flow velocity can be even higher if the roughness coefficient of aging pipes can be decreased.Item Annotation and Information Extraction of Consumer-Friendly Health Articles for Enhancing Laboratory Test Reporting(American Medical Informatics Association, 2024-01-11) He, Zhe; Tian, Shubo; Erdengasileng, Arslan; Hanna, Karim; Gong, Yang; Zhang, Zhan; Luo, Xiao; Lustria, Mia Liza A.; Engineering Technology, School of Engineering and TechnologyViewing laboratory test results is patients' most frequent activity when accessing patient portals, but lab results can be very confusing for patients. Previous research has explored various ways to present lab results, but few have attempted to provide tailored information support based on individual patient's medical context. In this study, we collected and annotated interpretations of textual lab result in 251 health articles about laboratory tests from AHealthyMe.com. Then we evaluated transformer-based language models including BioBERT, ClinicalBERT, RoBERTa, and PubMedBERT for recognizing key terms and their types. Using BioPortal's term search API, we mapped the annotated terms to concepts in major controlled terminologies. Results showed that PubMedBERT achieved the best F1 on both strict and lenient matching criteria. SNOMED CT had the best coverage of the terms, followed by LOINC and ICD-10-CM. This work lays the foundation for enhancing the presentation of lab results in patient portals by providing patients with contextualized interpretations of their lab results and individualized question prompts that they can, in turn, refer to during physician consults.Item Are Recent Terrorism Trends Reflected in Social Media?(IEEE, 2017-10) Terziyska, Ivana; Shah, Setu; Luo, Xiao; Engineering Technology, School of Engineering and TechnologySocial media plays an important role in shaping the beliefs and sentiments of an audience regarding an event. A comparison between public data sets that have holistic features and social media data set that include more user features would give insight into the spread of misinformation and aspects of events that are reflected in user behavior. In this research, we compare the trends identified in the public data set - Global Terrorism Database (GTD) with the trends reflected through the social media data obtained using the Twitter API. The unsupervised learning algorithm Self-Organizing Map (SOM) is used to identify the features and trends summarized by the clusters. The results show discrepancies in the features and related trends of terrorism events in the GTD data set and obtained Twitter data set to suggest some media bias and public perception on terrorism.Item Art, Architecture, and Community: Create Spaces to Highlight Local Talent(ASEE Peer, 2020-06-22) Nickolson, Darrell D.; Pruitt, Katie; Engineering Technology, School of Engineering and TechnologyThe paper will focus on a two-semester service-learning project in which Architectural Technology Students are partnering with a local entity called Reclaiming Community. Reclaim is a subsidiary of a larger local organization with a mission to bring about sustainable regeneration, improvement, and management of the physical environment through their Art Shed initiative. Each semester will develop a separate set of shed designs, with separate assessment methods and outcomes. The over-arching goal of the project is revitalizing the neighborhoods that will house these sheds, and encourage the love of art and design in area. Sheds are designed with the intent that after a certain about of time in residence the materials will be recycled for custom designed furniture. Utilizing the evidence-based design process (EBD) students will collaborate with Reclaiming project organizers to identify goals for the destination points. Sheds are studied and designed utilizing varying roof styles and interactive design ideas. Through this process each student will design a version of the shed, creating detailed instruction manual with materials and construction methods, and do a miniature 3D study model of the shed. Community partners from the reclaim project will play an integral role in reviewing the design process of the sheds, giving critical feedback for revisions and use. This is a very important part to ensure the evidence basedesign strategies are effectively solving the design problem. Assessment methods include our institutions Start/Stop/Continue along with customized end of course survey specifically aligned with this project. The community partners will also assist in development of end of course surveys, further integrating them into the culture of the course. The Start/Stop/Continue assessment is a student-centered mid-semester assessment of the project and its process. The completed paper will include the assessment results and course/project modifications carried into the second part of the semester. The customized end of semester course survey will allow the community partner along with the faculty member to specifically target questions at the students participation in the project and the outcomes. Results will be used for phase two of the project to take place in the spring semester.Item Attention Mechanism with BERT for Content Annotation and Categorization of Pregnancy-Related Questions on a Community Q&A Site(IEEE, 2020-12) Luo, Xiao; Ding, Haoran; Tang, Matthew; Gandhi, Priyanka; Zhang, Zhan; He, Zhe; Engineering Technology, School of Engineering and TechnologyIn recent years, the social web has been increasingly used for health information seeking, sharing, and subsequent health-related research. Women often use the Internet or social networking sites to seek information related to pregnancy in different stages. They may ask questions about birth control, trying to conceive, labor, or taking care of a newborn or baby. Classifying different types of questions about pregnancy information (e.g., before, during, and after pregnancy) can inform the design of social media and professional websites for pregnancy education and support. This research aims to investigate the attention mechanism built-in or added on top of the BERT model in classifying and annotating the pregnancy-related questions posted on a community Q&A site. We evaluated two BERT-based models and compared them against the traditional machine learning models for question classification. Most importantly, we investigated two attention mechanisms: the built-in self-attention mechanism of BERT and the additional attention layer on top of BERT for relevant term annotation. The classification performance showed that the BERT-based models worked better than the traditional models, and BERT with an additional attention layer can achieve higher overall precision than the basic BERT model. The results also showed that both attention mechanisms work differently on annotating relevant content, and they could serve as feature selection methods for text mining in general.Item Authenticating Users Through Fine-Grained Channel Information(IEEE, 2018-02) Liu, Hongbo; Wang, Yan; Liu, Jian; Yang, Jie; Chen, Yingying; Poor, H. Vincent; Engineering Technology, School of Engineering and TechnologyUser authentication is the critical first step in detecting identity-based attacks and preventing subsequent malicious attacks. However, the increasingly dynamic mobile environments make it harder to always apply cryptographic-based methods for user authentication due to their infrastructural and key management overhead. Exploiting non-cryptographic based techniques grounded on physical layer properties to perform user authentication appears promising. In this work, the use of channel state information (CSI), which is available from off-the-shelf WiFi devices, to perform fine-grained user authentication is explored. Particularly, a user-authentication framework that can work with both stationary and mobile users is proposed. When the user is stationary, the proposed framework builds a user profile for user authentication that is resilient to the presence of a spoofer. The proposed machine learning based user-authentication techniques can distinguish between two users even when they possess similar signal fingerprints and detect the existence of a spoofer. When the user is mobile, it is proposed to detect the presence of a spoofer by examining the temporal correlation of CSI measurements. Both office building and apartment environments show that the proposed framework can filter out signal outliers and achieve higher authentication accuracy compared with existing approaches using received signal strength (RSS).Item AutoForecast: Automatic Time-Series Forecasting Model S(National Science Foundation, 2022) Abdallah, Mustafa; Rossi, Ryan; Mahadik, Kanak; Kim, Sungchul; Zhao, Handong; Bagchi, Saurabh; Engineering Technology, School of Engineering and TechnologyIn this work, we develop techniques for fast automatic selection of the best forecasting model for a new unseen time-series dataset, without having to first train (or evaluate) all the models on the new time-series data to select the best one. In particular, we develop a forecasting meta-learning approach called AutoForecast that allows for the quick inference of the best time-series forecasting model for an unseen dataset. Our approach learns both forecasting models performances over time horizon of same dataset and task similarity across different datasets. The experiments demonstrate the effectiveness of the approach over state-of-the-art (SOTA) single and ensemble methods and several SOTA meta-learners (adapted to our problem) in terms of selecting better forecasting models (i.e., 2X gain) for unseen tasks for univariate and multivariate testbeds.Item Big Data Edge on Consumer Devices for Precision Medicine(IEEE, 2022) Stauffer, Jake; Zhang, Qingxue; Engineering Technology, School of Engineering and TechnologyConsumer electronics like smartphones and wearable computers are furthering precision medicine significantly, through capturing/leveraging big data on the edge towards real-time, interactive healthcare applications. Here we propose a big data edge platform that can, not only capture/manage different biomedical dynamics, but also enable real-time visualization of big data. The big data can also be uploaded to cloud for long-term management. The system has been evaluated on the real-world biomechanical data-based application, and demonstrated its effectiveness on big data management and interactive visualization. This study is expected to greatly advance big data-driven precision medicine applications.Item Building capacity for socio-ecological change through the campus farm: A mixed-methods study(Taylor & Francis, 2022) Williamson, Francesca A.; Rollings, Amber J.; Fore, Grant A.; Angstmann, Julia L.; Sorge, Brandon H.; Engineering Technology, School of Engineering and TechnologyGiven the ongoing socio-ecological crises, higher education institutions need curricular interventions to support students in developing the knowledge, skills, and perspectives needed to create a sustainable future. Campus farms are increasingly becoming sites for sustainability and environmental education toward this end. This paper describes the design and outcomes of a farm-situated place-based experiential learning (PBEL) intervention in two undergraduate biology courses and one environmental studies course over two academic years. We conducted a mixed-method study using pre/post-surveys and focus groups to examine the relationship between the PBEL intervention and students’ sense of place and expressions of pro-environmentalism. The quantitative analysis indicated measurable shifts in students’ place attachment and place-meaning scores. The qualitative findings illustrate a complex relationship between students’ academic/career interests, backgrounds, and pro-environmentalism. We integrated these findings to generate a model of sustainability learning through PBEL and argue for deepening learning to encourage active participation in socio-ecological change.