layered materials: Feedforward neural network classifies metallic andsemiconducting phases as well as defects. MISTIQS (https://github.com/USCCACS/MISTIQS): Quantum many-body dynamicssimulations on emerging quantum computers such as IBM Q and Rigetti Aspen [11, 12, 35, 36].We will re-architect MISTIQS in a way easily extensible to other problems and quantumlanguages/circuits. Quantum computing will be mirrored by exascale simulations on an exaflopcomputer to assess quantum supremacy. We are developing: (1) AI-inspired domain-specificquantum compilers to address the fundamental limitation of the noisy intermediate-scalequantum (NISQ) computing technology—environmental noise—by reducing the size and depthof quantum circuits, thereby extending
office toolkit. https://www.nationalpostdoc.org/page/npa_toolkitsPattton, M. Q. (2015). Qualitative research and evaluation methods: Integrating theory and practice (4th ed.). Sage.Powell, K. (2015). The future of the postdoc. Nature, 520, 144–147.Proudfoot, S., & Hoffer, T. B. (2016). Science and engineering labor force. In L. Gokhberg, N. Shmatko, & L. Auriol (Eds.), The science and technology labor force: The value of doctorate holders and development of professional careers (pp. 77–119). Springer.Pyhältö, K. (2018). Function of supervisory and researcher community support in PhD and post- PhD trajectories. In E. Bizer, L. Frick, M. Fourie-Malherbe, & K. Pyhältö (Eds.), Spaces, journeys
tivity1 Perception Supervised Learning Interactive exercises, hands-on drone model development activity Conversational AI Programming social robot, dialogue NLP, intent recognition flow training Bias and Ethical Implications Case studies, small group discussions Bias in ML, ethical principles Reinforcement Learning Interactive robot activity, Q-learning in- Robot behavior, path following Machine troduction2 Behavior Deep Reinforcement Learning Hands-on drone
acceptance level,” Cleaner Engineering and Technology, vol. 1, p. 100007, Dec. 2020. [Online]. Available: https://doi.org/10.1016/j.clet.2020.100007. [Accessed June 5, 2023].[3] D. Zhuocheng, Q. Huang, and Q. Zhang, “Life cycle assessment of mass timber construction: A review,” Building and Environment, vol. 221, p. 109320, Aug. 2022, [Online]. Available: https://doi.org/10.1016/j.buildenv.2022.109320. [Accessed Oct. 5, 2023].[4] M. F. Laguarda Mallo and O. Espinoza, “Awareness, perceptions and willingness to adopt cross-laminated timber by the architecture community in the United States,” ScienceDirect,” Journal of Cleaner Production, vol. 94, pp. 198–210, May 2015. [Online]. Available: ScienceDirect
technical executives, Board of Directors level presentation, feedback, and an executive round table Q&A discussion. Students join the instructors and visiting executives for an in-person, intensive, day-long meeting to present their technical executive strategy and implementation plan to a “board of directors” role-played by four “visiting executives” who hold senior leadership positions in their respective companies. In the first deliveries of this course both teams were prepared to brief the visiting executives, but because of time-constraints only one team was selected by the flip of a coin. The presenting team, with visiting executive coaching allowed during the presentations, was expected to apply critical thinking
. Thurston, R. Thielstrom, T. Fong, Q. Pham, and M. Scheutz, “Toward genuine robot teammates: Improving human-robot team performance using robot shared mental models.” in Aamas, 2020, pp. 429–437.[16] K. Bezrukova, T. L. Griffith, C. Spell, V. Rice Jr, and H. E. Yang, “Artificial intelligence and groups: Effects of attitudes and discretion on collaboration,” Group & Organization Management, vol. 48, no. 2, pp. 629–670, 2023.[17] L. Giray, J. Jacob, and D. L. Gumalin, “Strengths, weaknesses, opportunities, and threats of using chatgpt in scientific research,” International Journal, vol. 7, no. 1, pp. 40–58, 2024.[18] L. Memmert and N. Tavanapour, “Towards human-ai-collaboration in brainstorming: Empirical insights into the
-Achieving Asian and Black STEM Students,” AERA Open, vol. 4, no. 4, p. 2332858418816658, Oct. 2018, doi: 10.1177/2332858418816658.[13] A. Master, S. Cheryan, and A. N. Meltzoff, “Computing whether she belongs: Stereotypes undermine girls’ interest and sense of belonging in computer science.,” J. Educ. Psychol., vol. 108, no. 3, p. 424, 2016.[14] U. Nguyen and C. Riegle-Crumb, “Who is a scientist? The relationship between counter- stereotypical beliefs about scientists and the STEM major intentions of Black and Latinx male and female students,” Int. J. STEM Educ., vol. 8, no. 1, pp. 1–18, 2021.[15] A. N. Washington, L. Burge, M. Mejias, K. Jean-Pierre, and Q. Knox, “Bridging the Divide: Developing Culturally-Responsive Curriculum
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knowledge by integratingtechnological tools with pedagogical strategies. Flipped learning (FL) reverses traditionalteaching methods by providing course materials on datapath design beforehand, fostering active,self-directed learning in the classroom. The pedagogy is enriched with structured practiceexercises, enhancing students’ understanding of datapath design, along with theirproblem-solving, analytical, and critical thinking skills. The effectiveness of this method isvalidated through various assessments, including homework, exams, Q&A sessions, and studentfeedback, with a positive comparison to the instructor’s previous teaching experiences. Thisholistic evaluation confirms the efficacy of this innovative approach in improving the
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operatewith high coordination and efficiency, leading to increased emotional stability and resilience to stress. The integrationof these diverse HRV measures offers a robust tool for monitoring cardiac health, stress levels, and autonomicfunction [16]. HRV Metric Formula Heart Rate Pulse({IBIi }) = 1 PN60 N i=1 IBIi q PN 1 SDNN SDNN({IBIi }) = N −1 i=1 (IBIi − IBI)2
, demonstrates technol- ogy and tools, identifies common pitfalls, and articulates deliverables. As illustrated in Figure 8b, 76.5% of the students from both institutions positively confirm the effectiveness of presentations and videos. Finally, to address students’ concerns about timely feedback, we use the chat feature of Slack chatbot 36 and Zoom 37 to facilitate Q&A sessions. Because of these efforts, we kept students’ satisfaction consistent during and after the Pandemic. 7 Reflections and Future work After conducting a 5-year pedagogic project, we have gained valuable insights from both students and faculty in cybersecurity. In the following, we have compiled a list of lessons learned and recommendations for researchers and educators.• Does
, Goldman E, Eagle KA, Crawford TC. , "Pacemaker recycling: A notion whose time has come," World J Cardiol, vol. 9(4), pp. 296-303, 2017.[12] M. Landolina et al., "The economic impact of battery longevity in implantable cardioverter-defibrillators for cardiac resynchronization therapy: the hospital and healthcare system perspectives," EP Europace, vol. 19, no. 8, pp. 1349-1356, 2016.[13] Y. Quan, X. Wu, S. Zhu, X. Zeng, Z. Zeng, and Q. Zheng, "Triboelectric nanogenerators for clinical diagnosis and therapy: A report of recent progress," Medicine in Novel Technology and Devices, vol. 16, p. 100195, 2022/12/01/ 2022.[14] H.-J. Yoon and S.-W. Kim, "Nanogenerators to Power Implantable Medical Systems," Joule, vol. 4
5 shows the frequency of human rating scores for the mental model dimensions, andFigure 6 shows the frequency of human rating scores for the mental model uses. The color ineach plot corresponds to temperature values. The rows of plots correspond to the model’srankings of the questions (i.e., top choice, second choice, and third choice). For example, “QRanking 1” was all the questions that the model ranked highest, “Q Ranking 2” was all thequestions that the model ranked second highest, and “Q Ranking 3” was all the questions rankedthird highest. Looking at the questions in this way illustrates our main findings. First, themodel’s rankings were not necessarily aligned with the human’s rankings. Second, thetemperature did not produce any
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not issued in CIP, but there are several means for program faculty to assess studentcontent mastery. Weekly presentations are the most frequent assessments, as students applyprinciples and techniques from the previous workshop in their clinical immersion. Studentspresent their primary research, secondary research, and synthesized conclusions from each weekof learning. Live Q/A with the entire cohort at the end of each presentation is a useful means toestablish appropriate standards among all teams. Department clinicians are encouraged to attendthe final presentations and to give feedback on the work presented by teams, and to supplement itwith their own experience. The final report offers the definitive assessment of student learninggiven it
50-min lectures had to be freed. In the past, three reviewsessions were done before the midterms, and two project description sessions were needed forthe smaller assigned projects. The review sessions were moved to out-of-class Q&A sessions,with the opportunity to watch previously recorded review sessions asynchronously. The projectdescription lectures were not needed anymore. The final (6th) lab session was used to test thetruss and was added to the class time by moving the third midterm to the final exam slot afterclasses ended. Figure 3: Student welding components of their prototype. Figure 4: Student welding the prototype truss connections behind a protective screen. Figure
Analysis: A Methods Sourcebook. SAGE Publications, 2018.[29] J. Saldaña, The Coding Manual for Qualitative Researchers. SAGE Publications, 2021.[30] J. W. Creswell, Qualitative inquiry and research design: choosing among five approaches, 3rd ed. Los Angeles: SAGE Publications, 2013.[31] C. Raffel et al., “Exploring the limits of transfer learning with a unified Text-to-Text Transformer.” arXiv, Jul. 28, 2020. Accessed: Apr. 03, 2023. [Online]. Available: http://arxiv.org/abs/1910.10683[32] OpenAI, “GPT-4 Technical Report.” arXiv, Mar. 27, 2023. doi: 10.48550/arXiv.2303.08774.[33] A. Q. Jiang et al., “Mixtral of Experts.” arXiv, Jan. 08, 2024. doi: 10.48550/arXiv.2401.04088.[34] “AI Coding powered by
career"?: In thisdiversity, Q&A session, mentees were encouraged to ask questions about computing careerequity, and pathways, including the available career opportunities, skillsets required, internshipinclusion in tips, and other related topics. Mentors shared their experience and their opinions oncomputing these topics. (Focus: objective v)iii) develop Mentoring Session 3 - Develop strategies to overcome barriers to reach goals: This wasstrategies to a Q&A session as well where mentees were able to ask questions related to theirbe successful perceived obstacles in computing careers, such as low sense of belonging & self-in computing efficacy, preparedness, academic struggle including
: Topic Modeling and Knowledge Mapping," Educational Technology & Society, vol.24, no. 1, pp. 205–222, 2021.[12] T. K. F. Chiu, H. Meng, C.-S. Chai, I. King, S. Wong, and Y. Yam, "Creation andEvaluation of a Pretertiary Artificial Intelligence (AI) Curriculum," IEEE Transactions onEducation, vol. 65, no. 1, pp. 30–39, 2022, doi: 10.1109/TE.2021.3085878[13] F. Wu, Y. Yang, and Q. He, "Thinking about the curriculum of artificial intelligenceundergraduate specialty: clarifying the connotation, promoting the intersection, and enablingthe application," China University Teaching, no. 2, pp. 14-19, 2019. (in Chinese)[14] D. Hu and Ji Xuan, "Innovation Path and Evolution Mechanism of AI Personal Trainingin US Research Universities," Journal of
balance, 1st law of thermodynamics andsecond law of thermodynamics for control volumes. Please use these equations to answerquestions 15 through 20. dmcv = m i − m e (A) dt i edEcv V2 V2 = Q cv − W cv + m i hi + i + gzi − m e he + e + gze (B) dt i 2 e 2 •dS cv Qj • • • = + mi si − me se + cv (C) dt j Tj i e15. (5 points)Consider the schematic drawing of a general control volume shown bellow. Place mcv, Ecv, Scv
, 2024]. © American Society for Engineering Education, 2024 2024 ASEE Midwest Section Conference[16] P. R. Taylor, L. M. Jackson, and S. K. Williams, "Educational Interventions for Improving Nutritional Status in School-aged Children," Education Sciences, vol. 13, no. 10, p. 988, 2023. [Online]. Available: https://www.mdpi.com/2227-7102/13/10/988. [Accessed: Mar. 5, 2024].[17] T. Q. Li, J. S. Huang, and M. Y. Chen, "Effects of a Nutritional Education Program on Diet Quality and Weight Loss in Adolescents," PLOS ONE, vol. 18, no. 11, p. e0294894, 2023. [Online]. Available: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0294894
study were asked toadminister the pre-assessment and pre-engagement survey before the unit implementation began,and they administered the post-assessment and post-engagement survey immediately after theunit implementation ended. Pre- and post-mean scores were calculated for both measures, andpaired-samples t-tests were conducted to determine if the differences were statisticallysignificant. The analyses were performed using R Studio and IBM SPSS Statistics 28.0.Statistical assumptions were assessed for data normality through skewness and kurtosis values,as well as Q-Q plots. Skewness values for all variables indicated moderate to low skewness,falling between -1 and 1. Similarly, kurtosis ranged between -2 to +2, meeting acceptable criteriafor
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Conference and Exposition, Conference Proceedings 2017, 2017.[47] Nite, S.; Allen, G.; Bicer, A.; Morgan, J.; Warren, V.; Barroso, L. College freshman beliefs about studying and learning mathematics: Results from a summer engineering calculus bridge program. ASEE Annual Conference and Exposition, Conference Proceedings 2017, 2017.[48] Lee, W.; Brozina, C.; Amelink, C.; Jones, B. Motivating incoming engineering students with diverse backgrounds: Assessing a summer bridge program’s impact on academic motivation. Journal of Women and Minorities in Science and Engineering 2017, 23, 121–145.[49] Whalin, R.; Pang, Q.; Lowe, L.; Latham, J. Assessment of a summer bridge program: Seven years and counting. ASEE Annual Conference and
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