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electronics and programming. These results were consistentacross both male and female respondents as well as education level. Table I: Participant pre-participation survey questions and responses (n = 11) Q# Question Result* (Ave.) 1 How likely are you to pursue a career in STEM? 3.91 2 How likely are you to pursue a career in the ocean industry? 2.45 3 How well prepared do you feel to participate in an ocean engineering project? 2.64 4 How
solve in the Week 1in Colombia were constrained to select a other country Synchronous class - presentations bychallenge specific to musculoskeletal disorders experts from bothand the students in the United States were countries and Q&Arestricted to problems within one of the followingbiomedical themes: neglected tropical diseases, Week 2cardiovascular disease, or orthopedics. Students start Week 3 As described below, our approach interacting
Deep Learning multilayer RNN; 4 NLP, Transformer, attention mechanisms; DL applications. The basic principles of RL; Reinforcement Q-learning algorithm; 2 Learning The development of RL applications.In contrast, as illustrated in Table 5, Course A not only includes a review of mathematics,computer science, and programming basics at the beginning of the course, but also dives deeperinto the details of AI knowledge, focusing on the implementation logic of AI algorithms andtools. This means a more in-depth exploration and
step-based tutoring for linear circuit analysis and comprehensive evaluation," in Proc. 2022 American Society for Engineering Education Annual Conference, Minneapolis, MN, USA, June 2022. [2] J. Sweller, “Cognitive load during problem solving: Effects on learning,” Cogn. Sci., vol. 12, pp. 257-285, 1988. [3] J. W. Nilsson and S. Riedel, Electric Circuits, 12th ed. Boston, MA, USA: Pearson, 2022. [4] J. D. Irwin and R. M. Nelms, Basic Engineering Circuit Analysis, 12th ed. Hoboken, NJ, USA: Wiley, 2021. [5] C. D. Whitlatch, Q. Wang, and B. J. Skromme, "Automated problem and solution generation software for computer-aided instruction in elementary linear circuit analysis," in Proc. 2012 American
: Based on the calculations, the pavement thickness needs toincrease from 8 to 9 inches to handle the increased load from EVs. Futureinfrastructure designs should consider this increased load to ensure road durabilityand safety.Appendix C Example Mathematical Calculation 2Case Study: Evaluating Bridge Load Capacity with Increasing EV TrafficBackground:A small bridge is designed to support a distributed load of q=4,000 lbs/ft under normal vehicletraffic. Anticipating variations in traffic, engineers initially designed the bridge to handle amaximum allowable load of Pmax=220,000 lbs, incorporating a safety margin above typicaltraffic loads. However, with the increasing adoption of electric vehicles (EVs), which aregenerally heavier than gas-powered
didn’t have time to cover, or to areas where there is more depth to explore.They are encouraged to use these questions to either ask themselves during their Q&A period, orto prime the audience to get the Q&A rolling.2. Student Input Example: Class Contribution Extra Credit Assignment DesignStudents rarely get to provide input on class structure or the content of individual modules. Oftenthe class syllabus has often been carefully crafted to balance learning goals, workloads (forteachers, graders, and students), and overall flow. While not set in stone, changing one elementof the syllabus can have cascading effects for the rest.In the work world, however, there is more flexibility with respect to the detailed specificationand timing of
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dictated by the grid as 1800 RPM and 460 V, respec- tively. If not, fuses will blow up and all values will decrease to zero. You have to restart the maneuver.Once the connection is done, the user will increase/decrease the field voltage ofthe DC machine, which will change the load at the coupling and consequently thepower flow as summarized in the table. Vf P Q Va Ia Speed Torque Angle (V) (W) (VAR) (V) (A) (RPM) (N.m) (deg) 413 -2 -62 460 1 1800 0 0 410 997 -177 460 3.2 1800 5.3 5.8 405 2594 -705 460 6.9 1800 14.1 15.6 400 4101 -1709
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desire for more knowledge in my field of study d. A desire to pursue a passion e. A lack of something better to do f. A desire to make new discoveries in the field g. A desire for higher pay h. A desire to teach i. A desire to help others j. The lifestyle of an engineer k. A desire to conduct research l. Poor economy/lack of available jobs m. A desire to change careers n. A desire to advance in my career o. To obtain credential p. The high regard in which engineers are held q. The opportunity for me to apply undergraduate work to my Master’s requirements r. Other4. Please indicate the extent to which you utilized
., Wang, X., Wang, J., Wu, Y., Zhu, K,, Chen, H., Yang, L., Yi, X., Wang, C.,Wang, Y., Ye, W., Zhang, Y., Chang, Y., Yu, P., Yang, Q. & Xie, Xing. (2024). A Survey onEvaluation of Large Language Models. CM Trans. Intell. Syst. Technol. 15, 3, Article 39(June), 45 pages. https://doi.org/10.1145/3641289Gemini for Google Workspace. 2024. Prompting guide 101: A quick-start handbook foreffective prompts, October 2024, https://services.google.com/fh/files/misc/gemini-for-google-workspace-prompting-guide-101.pdf, accessed 4/19/2025.Hsieh, H-F., & Shannon, S. E. (2005). Three Approaches to Qualitative Content Analysis.Qualitative Heath Research. 15, 9, 1277-1288. https://doi.org/10.1177/1049732305276687Huang, J., Gu, S., Hou, L., Wu, Y., Wang, X
, “The kirkpatrick model: A useful tool for evaluating training outcomes,” Journal of Intellectual and Developmental Disability, vol. 34, no. 3, pp. 266–274, 2009.[20] D. R. Krathwohl, “A revision of bloom’s taxonomy: An overview,” Theory into practice, vol. 41, no. 4, pp. 212–218, 2002.[21] A. Alhamadah, M. Mamun, H. Harms, M. Redondo, Y.-Z. Lin, J. Pacheco, S. Salehi, and P. Satam, “Photogrammetry for digital twinning industry 4.0 (i4) systems,” arXiv preprint arXiv:2407.18951, 2024.[22] Y.-Z. Lin, Q. Shi, Z. Yang, B. S. Latibari, S. Shao, S. Salehi, and P. Satam, “Ddd-gendt: Dynamic data-driven generative digital twin framework,” arXiv preprint arXiv:2501.00051, 2024.[23] D. Hamilton, J. McKechnie, E. Edgerton, and C. Wilson
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student might create a basic generativemodel or evaluate ChatGPT's performance in a robotics Q&A scenario. This approach integratesAI into the curriculum and prepares students for an AI-augmented workforce. An external surveyby the Digital Education Council found that 80% of students felt their university's AI integrationwas lacking [7]. Similarly, our Robotics students suggest that the curriculum hasn't fully adapted.Considering their feedback could position the program as a leader in effectively incorporatingGenerative AI.In conclusion, our research emphasizes the importance of equilibrium and preemptive adaptation.Students regard Generative Artificial Intelligence (GenAI) as both a robust educational instrumentand a considerable risk if
real-world problem-solvingabilities.References1. R. Deng, G. Xiao, R. Lu, H. Liang, and A. V. Vasilakos, "False Data Injection on State Estimation in Power Systems—Attacks, Impacts, and Defense: A Survey," IEEE Transactions on Industrial Informatics, vol. 13, no. 2, pp. 411-423, Apr. 2017, doi: 10.1109/TII.2016.2614396.2. R. Deng, G. Xiao, R. Lu, H. Liang, and A. V. Vasilakos, "False Data Injection on State Estimation in Power Systems—Attacks, Impacts, and Defense: A Survey," IEEE Transactions on Industrial Informatics, vol. 13, no. 2, pp. 411-423, Apr. 2017, doi: 10.1109/TII.2016.2614396.3. Z. Zhao, Y. Shang, B. Qi, Y. Wang, Y. Sun, and Q. Zhang, "Research on Defense Strategies for Power System Frequency Stability Under False
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engineeringstudents while highlighting the nuanced contributions of motivational profiles. By integratingrobust statistical analyses with theoretical frameworks, the research offers valuable insights intothe interplay between achievement goals, self-belief, and adaptability. These findings havepractical implications for designing targeted interventions to optimize student motivation,resilience, and academic success in engineering education. Future work should build on theseinsights to refine strategies for fostering resilience and adaptability, ultimately enhancingoutcomes in STEM education.Reference[1] U. P. Supervía, S. C. Bordás, and Q. A. Robres, "The mediating role of self-efficacy in the relationship between resilience and academic performance
Computational Screening of Adhesive Molecules Derived from Dihydroxyphenyl Alanine," ACS Omega, vol. 9, no. 1, pp. 994-1000, 2024/01/09 2024, doi: 10.1021/acsomega.3c07208.[16] T. Liu, Z. Chen, J. Yang, L. Ma, A. Mol, and D. Zhang, "Machine learning assisted discovery of high-efficiency self-healing epoxy coating for corrosion protection," npj Materials Degradation, vol. 8, no. 1, p. 11, 2024/01/19 2024, doi: 10.1038/s41529-024- 00427-z.[17] X. Q. Wang, P. Chen, C. L. Chow, and D. Lau, "Artificial-intelligence-led revolution of construction materials: From molecules to Industry 4.0," Matter, vol. 6, no. 6, pp. 1831- 1859, 2023/06/07/ 2023, doi: https://doi.org/10.1016/j.matt.2023.04.016.[18] S. Suhaib
, limiting insights into how undergraduate students orthose in other disciplines might experience redesigned assessments. The short-term focus of the studyalso means that long-term impacts on learning and skill retention remain unexplored. Additionally,studies could examine the impact of redesigned assessments on instructor workload, studentengagement, and equity and accessibility, ensuring that innovative assessment practices benefit alllearners.ReferencesAnthropic. (2024). Claude [Large language model]. https://www.anthropic.com/Google. (2024). Gemini [Large language model]. https://gemini.google.com/Huang, A. Y. Q., Lu, O. H. T., & Yang, S. J. H. (2023). Effects of artificial intelligence–enabledpersonalized recommendations on learners
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