Industrial Engineering and Co-PI of the MERIT project. Her research interests include Sustainable Energy, Green Manufacturing, Quality Control, and Multi Objective Decision Making and Optimization as well as Engineering Education. She has served as PI and Co-PI in several NSF, NIST, DoEd, NASA, USDA and industry sponsored projects.Dr. Jaya S. Goswami, Texas A&M University-Kingsville American c Society for Engineering Education, 2022 1 Session XXXX Improving Minority Students’ Career Readiness Through Enhanced Capstone
Dominion University.Monica Brown, Granby High School Mathematics Teacher Taught Secondary Mathematics for 20 years. Masters in Secondary Education from The College of New Jersey BS in Electrical Engineering from Polytechnic Institute Bucharest (accredited in US)Bright S Tsevi, Norfolk State UniversitySheryl ShajiDr. Sacharia Albin, Norfolk State University Dr. Sacharia Albin joined Norfolk State University in July 2011 as the Chair of the Engineering Depart- ment. He received his BS and MS degrees from the University of Kerala, and Ph.D. from the University of Poona, India. He was a design engineer in microelectronics at Hindustan Aeronautics, India for three years. He was awarded a Post-Doctoral Research Fellowship by
/10.1364/AO.32.001154.[2] P. K. Koech, M. Ogini, S. Mohan, A. Alice Francis, M. Deo, S. Albin, and K. B. Sundaram, “Characterization of Silicon Nanowires Reflectance by Effective Index Due to Air-Silicon Ratio,” ECS Transactions, 89(4), 17–30, 2019. https://doi.org/10.1149/08904.0017ecst[3] S. Patchett, M. Khorasaninejad, O, N., and S. S. Saini, “Effective index approximation for ordered silicon nanowire arrays,” Journal of the Optical Society of America B, 30(2), 306. 2013. https://doi.org/10.1364/josab.30.000306.[4] F. Kimeu, S. Albin, K. Song, and K. C. Santiago, “ALD-passivated silicon nanowires for broadband absorption applications,” AIP Advances, 11(6), 065101, 2021. https://doi.org/10.1063
elevation height and shorter tower. Introduction Wind energy harvesting systems have gained significant attention in exploiting wind energyin recent times. According to WoodMac [1], the world-installed wind power capacity is projected tobe over 1,756 GW by 2030, with an increasing cumulative annualized growth rate (CAGR) of 9%between 2021 and 2030 [1]. Because wind energy is sustainable, clean, and does produce greenhouseemissions and U. S is planning to generate 20% of its electric power from wind energy by 2030 [2].According to the Global Wind Energy Council (GWEC) 2021 report [3], the installed wind powercapacity was 743 GW in 2021, which prevented over 1.1 billion tonnes of carbon dioxide
ConclusionThis paper presents preliminary work of the implementation of object detection on Raspberry Pi for asenior design project. We run a mobile deep learning model, SSD-MobileNet, on Raspberry Pi todetect various objects. Preliminary valudation results demonstrate the effectiveness of thisimplementation. Moreover, the on-going work is to improve weapon detection. Future work will focuson completing comprehensive and systematical validation on weapon detection in different testingscenarios. References1. Dave, E. (2011). How the next evolution of the internet is changing everything. The Internet of Things.2. Islam, S. R., Kwak, D., Kabir, M. H., Hossain, M., and Kwak, K. S. (2015). The internet
discussed.Below are the basics of the Gravitational N-Body Problem for the sake of this experiment. Findingpositions and movements of bodies in space subject to gravitational forces from other bodies usingNewton’s laws of motion.Gravitational force F between two bodies of masses ma and mb as seen in Figure 1 is: (1) Figure 1: Gravitational N-Body ProblemG is the gravitational constant (6.673 × 10-11 m3 kg-1 s-2) and r the distance between the bodies. (2)For a system of N particles, the sum of the forces is
particular, researchers performanalyses of the environments associated with a microgreens market using a PESTLE framework –which identifies the political (P), economic (E), sociocultural (S), technological (T), legal (L), andenvironmental (E) forces influencing a market. The political environment (P) is favorable towardsincreased microgreen production. For example, the Farm Bill of 2018 provided the USDA’s NationalInstitute of Food and Agriculture with up to $10 million of annual funding toward a competitive grantprogram supporting the development of urban, indoor, and emerging agriculture practices (USDA)2.The economic environment (E) is perceived to be favorable for a microgreens market. Microgreenstrade at a premium when compared to other
, fabricated, and tested successfully at the PVAMU's SMART Center for bike laneapplications. The EGP is composed of seven layers of compound materials. A 3-feet-long integratedPZT strip of 18 thin-film PZT cells was placed in the middle of the EGP. The prototype of the EGPwas tested using an output load of 7.5 kΩ. A bicycle with a rider weighing a total of about 142pounds served as the mechanical pressure source for the test. The tested PZT cells produced anaverage of 68 VDC, and the energy of the DCPR was 616.53 mW/s for the rider riding the bike onthe EGP. IntroductionGlobal energy demand is increasing due to technological advances and consumer patterns that haveled to increased energy demand [1-2
of the 2022 ASEE Gulf-Southwest Annual Conference Prairie View A&M University, Prairie View, TX Copyright © 2022, American Society for Engineering Education 7 References1. Sumarni, S., 2016, "Think Pair Share effect of understanding the concept and achievement," in Proceeding of the International Conference on teacher Training and Education, Vol. 2, No. 1, pp. 783-787.2. Deshpande, A. and Salman, B., 2016, "Think-pair-share: application of an active learning technique in engineering and construction management classes," in Associate Schools of Construction, 52nd
slightly betterresults. Our next steps include building on existing sketch recognition research andincorporating more features such as tilt and pressure measurements in hopes of building areliably accurate application to access children’s fine motor skills. References 1. Grissmer, D., Grimm, K.J., Aiyer, S.M., Murrah, W.M., Steele, J.S.: Fine motor skills and early comprehension of the world: two new school readiness indicators. Develop-mental psychology 46(5), 1008 (2010). 2. Anthony, L., Brown, Q., Nias, J., Tate, B., Mohan, S.: Interaction and recognition challenges in interpreting children’s touch and gesture input on mobile devices. In:Proceedings of the 2012 ACM
, F. u., Baig, S., & Junaid Mughal, M. (2012). Performance comparison of time-domain equalization (TEQ) techniques in a discrete wavelet multi-tone (DWMT) system for asymmetric digital subscriber line (ADSL). World Applied Sciences Journal, 20, 1008-1013. doi:10.5829/idosi.wasj.2012.20.07.2652 4. Ibraheem, O. W., & Khamiss, N. N. (2008). Design and Simulation of Asymmetric Digital Subscriber Line (ADSL) modem. 2008 3rd International Conference on Information and Communication Technologies: From Theory to Applications, ICTTA. doi:10.1109/ICTTA.2008.4530251 5. Katiyar, R., & Padmaja, K. V. (2016). Performance analysis of time domain and frequency domain equalizer for ADSL
Paper ID #35732Engineers and AccountabilityDr. Kenneth W. Van Treuren, Baylor University Ken Van Treuren is an Associate Professor in the Department of Engineering at Baylor University. He received his B. S. in Aeronautical Engineering from the USAF Academy in Colorado Springs, Colorado and his M. S. in Engineering from Princeton University in Princeton, New Jersey. After serving as USAF pilot in KC-135 and KC-10 aircraft, he completed his DPhil in Engineering Sciences at the University of Oxford, United Kingdom and returned to the USAF Academy to teach heat transfer and propulsion systems. At Baylor University, he teaches
7 References 1. Roberts, F. L., Kandhal, P. S., Brown, E. R., Lee, D. Y., & Kennedy, T. W. (1991). Hot mix asphalt materials, mixture design and construction. 2. Van Poel, C. D. (1954). A general system describing the visco‐elastic properties of bitumens and its relation to routine test data. Journal of applied chemistry, 4(5), 221-236. 3. Bari, J. (2005). Development of a new revised version of the Witczak E* predictive models for hot mix asphalt mixtures. Arizona State University. 4. Witczak, M. W., Quintas, H. V., Kaloush, K., Pellinen, T., & Elbasyouny, M. (2000). Simple performance test: Test results and
) under Physical Confinement. Macromolecules 2005, 38, 4769.7. Guo, Q.; Thomann, R.; Gronski, W.; Staneva, R.; Ivanova, R.; Stühn, B. Nanostructures, Semicrytalline Morphology, and Nanoscale Confinement Effect on the Crystallization Kinetics in Self-Organized Block Copolymer/Thermoset Blends. Macromolecules 2003, 36, 3635.8. Jiang, S.; Ji, X.; An, L.; Jiang, B. Crystallization behavior of PCL in hybrid confined environment. Polymer 2001, 42, 3901.9. Sakurai, T.; Nagakura, H.; Gondo, S.; Nojima, S. Crystallization of poly(ε-caprolactone) blocks confined in crystallized lamellar morphology of poly(ε-caprolactone)-block-polyethylene copolymers: Effects of polyethylene crystallinity and confinement size. Polym. J
-grid-ercot/. [Accessed: 24- Jan-2022].6. “Text - H.R.2482 - 117th Congress (2021-2022): Microgrid ...” [Online]. Available: https://www.congress.gov/bill/117th-congress/house-bill/2482/text?r=165&s=1. [Accessed: 24-Jan-2022].7. “S.1183 - A bill to establish an energy storage and microgrid grant and technical assistance program.,” Congress.Gov, 11-Apr-2019. [Online]. Available: https://www.congress.gov/bill/116th-congress/senate- bill/1183/text?q=%7B%22search%22%3A%5B%22microgrid%22%2C%22microgrid%22%5D%7D&r=43&s=1. [Accessed: 23-Jan-2022].8. “(H.R. 4447) to establish an energy storage and microgrid grant and technical assistance
-Southwest Annual Conference Prairie View A&M University, Prairie View, TX Copyright 2022, American Society for Engineering Education 10 References1. S. S. Bhalshankar and C. S. Thorat, “Integration of smart grid with renewable energy for Energy Demand Management: Puducherry Case Study,” 2016 International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES), 2016.2. F. E. Tahiri, K. Chikh, M. Khafallah, A. Saad, and D. Breuil, “Modeling and performance analysis of a solar PV power system under irradiation and Load Variations,” 2017 14th
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instructional and socioemotional challenges that manystudents encountered during the shift to remote instruction. This study and its findings add tonascent research exploring rapid responses by faculty to address issues associated with remoteinstruction and document effective instructional practices. References[1] Andre, E., Williams, N., Schwartz, F., Bullard, C. Benefits of Campus Outdoor RecreationPrograms: A Review of the Literature. Journal of Outdoor Recreation, Education, and Leadership.2017, Vol. 9, No. 1, pp 15-25.[2] Bailey, T., Alfonso, M. Paths to persistence: An analysis of research on program effectivenessat community colleges. Indianapolis, IN: Lumina Foundation of Education. 2005.[3] Bauman, S
” forreporting “Male/Female/Non-binary Gender/Another Gender or Unknown” [7]. Please see the 2016report of the Integrated Postsecondary Education Data System (IPEDS) Technical Review Panel(TRP) discussion on gender for additional context [8]. References 1. S. L. Eddy and S. E. Brownell, “Beneath the numbers: A review of gender disparities in undergraduate education across science, technology, engineering, and math disciplines,” Phys. Rev. Phys. Educ. Res., vol. 12, no. 2, p. 020106, Aug. 2016, doi: 10.1103/PhysRevPhysEducRes.12.020106. 2. M. Bahnson, C. Cass, H. L. Perkins, M. A. Tsugawa-Nieves, and A. Kirn, “WIP: A Case for Disaggregating Demographic Data,” presented
] Available: https://www.texastribune.org/2021/07/26/texas-covid-19-delta/ (accessed Jan. 19, 2022).[2] S. Jaschik, “Delta variant raises questions for colleges about reopening plans,” Inside Higher Ed, 2021. [Online] Available: https://www.insidehighered.com/news/2021/08/16/delta-variant-raises- questions-colleges-about-reopening-plans (accessed Jan. 19, 2022).[3] C. Mooney and B. A. Becker, “Investigating the impact of the COVID-19 pandemic on computing students’ sense of belonging,” in 52nd ACM Technical Symposium on Computer Science Education, 2021, vol. 12, no. 2, pp. 38–45, doi: 10.1145/3463408.[4] M. Pownall, R. Harris, and P. Blundell-Birtill, “Supporting students during the transition to university in
Subaward Agreementwith the Louisiana Space Grant Consortium (LaSPACE).” References1. Radhwan, H., Shayfull, Z., Abdellah, A. E. H., Irfan, A. R., & Kamarudin, K. (2019). Optimization parameter effects on the strength of 3D-printing process using Taguchi method. In AIP Conference Proceedings (Vol. 2129, No. 1, p. 020154). AIP Publishing LLC.2. Oropallo, W., & Piegl, L. A. (2016). Ten challenges in 3D printing. Engineering with Computers, 32(1), 135-148.3. Prabhakar, M. M., Saravanan, A. K., Lenin, A. H., Mayandi, K., & Ramalingam, P. S. (2021). A short review on 3D printing methods, process parameters and materials. Materials Today: Proceedings, 45, 6108-6114.4. Chan
teachers200,000 years ago, we would teach the students the skills of making stone tools. If we were technicalteachers 5,000 years ago, we would teach the students the skills of making mostly agriculture relatedtools. If we were engineering professors in year 1960’s, we would include a class to teach the studentsusing a slider ruler to calculate the solutions of engineering problems. If we were engineeringprofessors in year 1980, we would ask the students solving engineering problems with Fortran on amainframe computer. If we were engineering professors in year 2000, we would ask the studentssolving engineering problems on a personal computer.We are now in year 2022, the main computing tool is still personal computer. However, the capabilityof a personal
XXXX Is the “E” in Engineering for Entrepreneurship? An Emerging Concept of Entrepreneurial Engineering Identity Le Shorn S Benjamin PhD, Jerrod A. Henderson PhD William A. Brookshire Department of Chemical and Biomolecular Engineering University of Houston Erik M. Hines, PhD College of Education Florida State University AbstractThe topic of engineering identity is neither new nor complete in its coverage within current literature.By applying both quantitative and qualitative lenses to
Challenges Facing the US In-cluding Impacts on Sustainability”, Paper 69c, AIChE Annual Conference, Nashville, TN, November 9,2009.(42) Emmanuel Dada, ”Overview of MAC-AIChE College Scholarships for Underrepresented Minoritiesin Chemical Engineering”, AIChE Annual Meeting, Philadelphia, PA, Nov. 16-21, 2008(43) Emmanuel Dada, Dalbir Sethi, and Dayi Deng, ”Production of Hydrogen Peroxide via the DirectCombination of H2 and O2 in a Pilot Microreactor.” AIChE Spring National Meeting, New Orleans,April 7, 2008.(44) A. Lawal, R. Halder, S. Tadepalli, Y. Voloshin, H. Chen, W. Y. Lee, and E. Dada, ”MicrochannelReactor System Design & Demonstration for On-site H2O2 Production by Controlled H2/O2 Reaction,”Invited Presentation, Process Intensification
Timoshenko Problem Solved Using All the Five SIMS.A rigid bar AB with rollers of weights P = 40 N at end A and Q = 80 N at end B is placedinside a circular ring in a vertical plane as shown in the system space diagram. Radius of the ringand the length AB are such that the radii AC and BC make a right angle at the center of the ringC. Neglecting friction and the weight of the bar AB, prove that the angle ψ, AB makes with thehorizontal is [(α – β) / 2]. Find α, β & ψ; the contact (normal) reactions NA & NB at A and B. and the axial compressive force S in the bar AB. P C QThe author was inspired in 2015, by this classic A αhomework problem by
. Asgari, S., Trajkovic, J., Rahmani, M., Zhang, W., Lo, R. C., and A. Sciortino, (2021). An Observational Study of Engineering Online Education During the COVID-19 Pandemic. PLOS ONE. 16(4): e0250041. 5. Xu J., Tao Y., and H. Lin, (2016). Semantic word cloud generation based on word embeddings. In: 2016 IEEE Pacific Visualization Symposium (PacificVis): 239–243. 6. Felix C., Franconeri S., and E. Bertini, (2018). Taking word clouds apart: an empirical investigation of the design space for keyword summaries. IEEE Trans. Vis. Comput. Graph, 24(1): 657–666. 7. Savage, N., Birch, R. and E. Noussi, (2011). Motivation of engineering students in higher education. Eng. Educ., 6:39-46. 8. Sadikin, A.N., Mohd-Yusof, K
radio—an integrated agent architecture for a software-defined radio,” 2000.4. J. Mitola Iii, ”Cognitive radio for flexible mobile multimedia communications,” Mobile Networks and Applications,vol. 6, pp. 435-441, 2001.5. T. Yucek and H. Arslan, ”A survey of spectrum sensing algorithms for cognitive radio applications,”IEEEcommunications surveys and tutorials, vol. 11, pp. 116-130, 2009.6. D. Cabric, S. M. Mishra, and R. W. Brodersen, ”Implementation issues in spectrum sensing for cognitive radios,” inSignals, systems, and computers, 2004. Conference record of the thirty-eighth Asilomar conference on, 2004, pp. 772-776.7. W. Ejaz, ul Hasan, N., Lee, S. et al., ”Intelligent spectrum sensing scheme for cognitive radio networks,” EURASIPJournal on
Instruction and Learning for over two years as a Production Assistant, and focuses his work on using technology to enable course development and innovation. Brandon is from Austin, Texas, and plans on going into a career in financial technology.Sidharth Dhaneshkumar Shah American c Society for Engineering Education, 2022 1 Session 2022 Creating Course Material through Automation of Lecture Caption S. Enrico P. Indiogine, Brandon Chi-Tien Le Engineering Studio for Advanced Instruction
meetings on WebEx were also arranged to helpthem doing their labs more effectively.IET 3510 Motion and Time Study was taught in fall 2020 and in fall 2021. This is a lecture-orientedcourse coupled with a semester project conducted in different industries. The semester project isdesigned to adopt the students to work in a group within a real industrial environment. However, dueto the pandemic the course was offered online, as well as group projects and physical visits to theindustry became infeasible. Hence, unlike previous years, in fall 2020 and 2021 the semester projectwas assigned to individual students based on some recorded video clips of the assigned workstation(s)imitating virtual visits to the industry. Multiple recorded videos of some
/future work, we will explore the use of speechand language processing technology to automate measurement of target PMLU and conversationalturn analysis. Conversational interactions of adults and children in naturalistic environments can beanalyzed to provide tracking data for teachers to consider when writing lesson plans and adaptingteaching methods for diverse students. References1. Durham, R. E., Farkas, G., Hammer, C. S., Bruce Tomblin, J., & Catts, H. W. (2007). Kindergarten oral language skill: A key variable in the intergenerational transmission of socioeconomic status. Research in Social Stratification and Mobility, 25(4), 294-305. doi:10.1016/j.rssm.2007.03.0012