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Item Electrocatalytic CO2 reduction on earth abundant 2D Mo2C and Ti3C2 MXenes(Royal Society of Chemistry, 2020) Attanayake, Nuwan H.; Banjade, Huta R.; Thenuwara, Akila C.; Anasori, Babak; Yan, Qimin; Strongin, Daniel R.; Engineering Technology, School of Engineering and TechnologyMo2C and Ti3C2 MXenes were investigated as earth-abundant electrocatalyts for the CO2 reduction reaction (CO2RR). Mo2C and Ti3C2 exhibited faradaic efficiencies of 90% (250 mV overpotential) and 65% (650 mV overpotential), respectively, for the reduction of CO2 to CO in acetonitrile using an ionic liquid electrolyte. The use of ionic liquid 1-ethyl-2-methylimidazolium tetrafluoroborate as an electrolyte in organic solvent suppressed the competing hydrogen evolution reaction. Density functional theory (DFT) calculations suggested that the catalytic active sites are oxygen vacancy sites on both MXene surfaces. Also, a spontaneous dissociation of adsorbed COOH species to a water molecule and adsorbed CO on Mo2C promote the CO2RR.Item Determination of Internal Elevation Fluctuation from CCTV Footage of Sanitary Sewers Using Deep Learning(MDPI, 2021) Ji, Hyon Wook; Yoo, Sung Soo; Koo, Dan Daehyun; Kang, Jeong-Hee; Engineering Technology, School of Engineering and TechnologyThe slope of sewer pipes is a major factor for transporting sewage at designed flow rates. However, the gradient inside the sewer pipe changes locally for various reasons after construction. This causes flow disturbances requiring investigation and appropriate maintenance. This study extracted the internal elevation fluctuation from closed-circuit television investigation footage, which is required for sanitary sewers. The principle that a change in water level in sewer pipes indirectly indicates a change in elevation was applied. The sewage area was detected using a convolutional neural network, a type of deep learning technique, and the water level was calculated using the geometric principles of circles and proportions. The training accuracy was 98%, and the water level accuracy compared to random sampling was 90.4%. Lateral connections, joints, and outliers were removed, and a smoothing method was applied to reduce data fluctuations. Because the target sewer pipes are 2.5 m concrete reinforced pipes, the joint elevation was determined every 2.5 m so that the internal slope of the sewer pipe would consist of 2.5 m linear slopes. The investigative method proposed in this study is effective with high economic feasibility and sufficient accuracy compared to the existing sensor-based methods of internal gradient investigation.Item Environment-independent In-baggage Object Identification Using WiFi Signals(IEEE Xplore, 2021-10) Shi, Cong; Zhao, Tianming; Xie, Yucheng; Zhang, Tianfang; Wang, Yan; Guo, Xiaonan; Chen, Yingying; Engineering Technology, School of Engineering and TechnologyLow-cost in-baggage object identification is highly demanded in enhancing public safety and smart manufacturing. Existing approaches usually require specialized equipment and heavy deployment overhead, making them hard to scale for wide deployment. The recent WiFi-based approach is unsuitable for practical deployment as it did not address dynamic environmental impacts. In this work, we propose an environment-independent in-baggage object identification system by leveraging low-cost WiFi. We exploit the channel state information (CSI) to capture material and shape characteristics to facilitate fine-grained inbaggage object identification. A major challenge of building such a system is that CSI measurements are sensitive to real-world dynamics, such as different types of baggage, time-varying ambient noises and interferences, and different deployment environments. To tackle these problems, we develop WiFi features based on polarized directional antennas that can capture objects’ material and shape characteristics. A convolutional neural network-based model is developed to constructively integrate the WiFi features and perform accurate in-baggage object identification. We also develop a material-based domain adaptation using adversarial learning to facilitate fast deployments in different environments. We conduct extensive experiments involving 14 representation objects, 4 types of bags in 3 different room environments. The results show that our system can achieve over 97% in the same environment, and our domain adaptation method can improve the object identification accuracy by 42% when the system is deployed in a new environment with little training.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.Item Fused multi-modal similarity network as prior in guiding brain imaging genetic association(Frontiers Media, 2023-05-05) He, Bing; Xie, Linhui; Varathan, Pradeep; Nho, Kwangsik; Risacher, Shannon L.; Saykin, Andrew J.; Yan, Jingwen; Alzheimer’s Disease Neuroimaging Initiative; Engineering Technology, School of Engineering and TechnologyIntroduction: Brain imaging genetics aims to explore the genetic architecture underlying brain structure and functions. Recent studies showed that the incorporation of prior knowledge, such as subject diagnosis information and brain regional correlation, can help identify significantly stronger imaging genetic associations. However, sometimes such information may be incomplete or even unavailable. Methods: In this study, we explore a new data-driven prior knowledge that captures the subject-level similarity by fusing multi-modal similarity networks. It was incorporated into the sparse canonical correlation analysis (SCCA) model, which is aimed to identify a small set of brain imaging and genetic markers that explain the similarity matrix supported by both modalities. It was applied to amyloid and tau imaging data of the ADNI cohort, respectively. Results: Fused similarity matrix across imaging and genetic data was found to improve the association performance better or similarly well as diagnosis information, and therefore would be a potential substitute prior when the diagnosis information is not available (i.e., studies focused on healthy controls). Discussion: Our result confirmed the value of all types of prior knowledge in improving association identification. In addition, the fused network representing the subject relationship supported by multi-modal data showed consistently the best or equally best performance compared to the diagnosis network and the co-expression network.Item Future Needs of the Cybersecurity Workforce(ACI, 2022-03-02) Justice, Connie; Sample, Char; Engineering Technology, School of Engineering and TechnologyExpected growth of the job market for cyber security professionals in both the US and the UK remains strong for the foreseeable future. While there are many roles to be found in cyber security, that vary from penetration tester to chief information security officer (CISO). One job of particular interest is security architect. The rise in Zero Trust Architecture (ZTA) implementations, especially in the cloud environment, promises an increase in the demand for these security professionals. A security architect requires a set of knowledge, skills, and abilities covering the responsibility for integrating the various security components to successfully support an organization’s goals. In order to achieve the goal of seamless integrated security, the architect must combine technical skills with business, and interpersonal skills. Many of these same skills are required of the CISO, suggesting that the role of security architect may be a professional stepping-stone to the role of CISO. We expected degreed programs to offer courses in security architecture. Accredited university cyber security programs in the United Kingdom (UK) and the United States of America (USA) were examined for course offerings in security architecture. Results found the majority of programs did not offer a course in security architecture. Considering the role of the universities in preparing C-suite executives, the absence of cyber security architecture offerings is both troubling and surprising.Item Comparison of Undergraduate Student Writing in Engineering Disciplines at Campuses with Varying Demographics(ASEE PEER, 2022-08-23) Edinbarough, Immanuel; Gonzalez, Jesus; Bodenhamer, Johanna; Pflueger, Ruth Camille; Weissbach, Robert; Engineering Technology, School of Engineering and TechnologyWriting is generally recognized as fundamental to the formation and communication of scientific and technical knowledge to peer groups and general audiences. Often, persuasive writing is an essential attribute emphasized by industries and businesses for a successful career in STEM fields. Nevertheless, the current scenario is that students in STEM fields, with their increased demand for more specialized skills in fewer credit hours combined with a lack of emphasis on writing from engineering faculty members, make addressing this need difficult. In addition, students in engineering fields often do not value writing skills and underestimate the amount of writing they will do in their careers. Hence, it is essential to understand and quantify the level of writing skills STEM students exhibit in their technical courses so that mitigation efforts can be designed using commonly available resources to enhance this important skillset among the students, including university writing centers. A research question was posed to study this aspect of technical writing: How do STEM students at institutions conceive of writing and its role in classroom laboratories? This research was conducted at three different universities with students of varied demographics, including one which is designated as a Hispanic-serving institution, via a sequential mixed-methods design. The demography variation among the institutions includes the level of college preparation among students and the mix of ethnicity to see if there are variations among certain groups. Although the sample size is small, the goal was to establish a methodology and a preliminary outcome set that could be used in further research with larger populations. Research data in the form of reports and surveys, were collected from groups of students from four distinct campuses to ascertain the technical writing capability of each group and provide a comparison to better understand the level of intervention required. The quantitative data was collected throughout the academic year through Likert scale surveys as well as rubric-based evaluation of reports. The research design, methodology, and results of the research findings and the proposed mitigation efforts to improve student writing in STEM fields are presented in the paper.Item A multi-scale time-series dataset with benchmark for machine learning in decarbonized energy grids(Springer Nature, 2022-06-22) Zheng, Xiangtian; Xu, Nan; Trinh, Loc; Wu, Dongqi; Huang, Tong; Sivaranjani, S.; Liu, Yan; Xie, Le; Engineering Technology, School of Engineering and TechnologyThe electric grid is a key enabling infrastructure for the ambitious transition towards carbon neutrality as we grapple with climate change. With deepening penetration of renewable resources, the reliable operation of the electric grid becomes increasingly challenging. In this paper, we present PSML, a first-of-its-kind open-access multi-scale time-series dataset, to aid in the development of data-driven machine learning (ML)-based approaches towards reliable operation of future electric grids. The dataset is synthesized from a joint transmission and distribution electric grid to capture the increasingly important interactions and uncertainties of the grid dynamics, containing power, voltage and current measurements over multiple spatio-temporal scales. Using PSML, we provide state-of-the-art ML benchmarks on three challenging use cases of critical importance to achieve: (i) early detection, accurate classification and localization of dynamic disturbances; (ii) robust hierarchical forecasting of load and renewable energy; and (iii) realistic synthetic generation of physical-law-constrained measurements. We envision that this dataset will provide use-inspired ML research in safety-critical systems, while simultaneously enabling ML researchers to contribute towards decarbonization of energy sectors.Item Pregnancy-Related Information Seeking in Online Health Communities: A Qualitative Study(Springer, 2021) Lu, Yu; Zhang, Zhan; Min, Katherine; Luo, Xiao; He, Zhe; Engineering Technology, School of Engineering and TechnologyPregnancy often imposes risks on women's health. Consumers are increasingly turning to online resources (e.g., online health communities) to look for pregnancy-related information for better care management. To inform design opportunities for online support interventions, it is critical to thoroughly understand consumers' information needs throughout the entire course of pregnancy including three main stages: pre-pregnancy, during-pregnancy, and postpartum. In this study, we present a content analysis of pregnancy-related question posts on Yahoo! Answers to examine how they formulated their inquiries, and the types of replies that information seekers received. This analysis revealed 14 main types of information needs, most of which were "stage-based". We also found that peers from online health communities provided a variety of support, including affirmation of pregnancy, opinions or suggestions, health information, personal experience, and reference to health providers' service. Insights derived from the findings are drawn to discuss design opportunities for tailoring informatics interventions to support consumers' information needs at different pregnancy stages.Item Quasi Self-Excited DFIG-Based Wind Energy Conversion System(IEEE Xplore, 2021-05) Akbari, Rasoul; Izadian, Afshin; Weissbach, Robert S.; Engineering Technology, School of Engineering and TechnologyThis article introduces a new configuration of the doubly-fed induction generator (DFIG) based wind energy conversion system (WECS) employing only a reduced-size rotor side converter (RSC) in tandem with a supercapacitor. In the proposed structure, the grid side converter (GSC) utilized in conventional DFIG-based WECSs is successfully eliminated. This is accomplished by employing the hydraulic transmission system (HTS) as a continuously variable and shaft decoupling transmission unit. This transforms the conventional constant-ratio drives by providing an opportunity to control the power flow through the generator's rotor circuit regardless of the wind turbine's shaft speed. This feature of HTS can be utilized to control the RSC power and ultimately regulate the supercapacitor voltage without a need for GSC. The proposed system is investigated and simulated in MATLAB Simulink at various wind speeds to validate the results and demonstrate the dynamic performance of the system.