Optimization ResultsFrom these charts, conclusions were drawn that for a speed of 2.5 mph (1.2 m/s), the maximumvelocity increase will come from a 25° angle and the largest feasible outlet area.4.4 Preliminary Shroud TestingThe current testing setup measures free stream velocity but not velocity within the shroud, so adirect assessment of velocity increase is not possible. However, testing does show increasedpower output from shrouded geometry that can be compared to unshrouded tests to determineand “effective” velocity to see what speed is necessary to produce the same power without ashroud.To further confirm CFD predictions, three different shroud sizes were tested: 20°, 25°, and 30°.All had an outlet area to inlet area ratio of four
. Nation’s Business, 82(6), 72-75.5. Bento, A. M., & White, L. F. (2001). Organizational form, performance and information costs in small businesses. Journal of Applied Business Research, 17(4), 41-61.6. Berrah, L., Mauris, G., & Vernadat, F. (2004). Information aggregation in industrial performance measurement: Rationales, issues and definitions. International Journal of Production Research, 42(20), 4271-4293.7. Bititci, U. S., Turner, T., & Begemann, C. (2000). Dynamics of performance measurement systems. International Journal of Operations and Production Management, 20(6), 692
testing of candidate assessment items is accomplished using cognitive interviews. Oncea number of questions have been brainstormed for a particular assessment, the questions areprinted (one to a page) and tested with students. Each interviewer takes a number of thequestions (no more than can be tested in 20 minutes with a student) and sits one-on-one with astudent to test the questions.During a cognitive interview, the interviewer first reassures the student that the purpose of theinterview is to see if the questions are good ones, not to test the student. Whether or not thestudent knows the answer to a question, we ask that s/he let us know what s/he thinks thequestion is asking, and whether any words or phrases are particularly confusing. The
Batra & Davis6and Crismond9’s work, which investigate expertise in design across different domains,find that experts tend to recognize similarities among situations and make connectionsbetween their works. Cross’s8 study summarizes most of the vital features of expertperformance, like the ability to form abstract conceptualizations and decompose Page 15.797.5problems explicitly. An additional summary of characteristics of expertise wasconducted by Farrington-Darby and Wilson20: Experts are inclined to perceive largemeaningful patterns, encode new information quickly, adapt decision strategies tochanging task conditions, possess the ability to make more
has been steadily and rapidly changing for many years. From about the mid-1980’s, concepts of cost control, quality and overall efficiency have become an increasingly sharp focus. In recent years, many companies have tunneled in on lean manufacturing as their savior. It is certainly true that the precepts and procedures of lean, ToC, TQM and other regimens are essential for modern manufacturing competitiveness, and instruction in these matters has become a fundamental component in manufacturing education. With far less visible excitement, however, another ‘revolution’ has entered the scene. The fastest growing sectors of product type are those that require new processing technologies. In 21st century
a network of opportunities external to the universityPage 15.1122.11VI. Bibliography[1] Berger, J. B., & Lyon, S. C. (2005). Past and present: A historical view of retention. In A. Seidman (Ed.), College student retention: Formula for student success. Westport, CT: Praeger.[2] Seidman, A. (2005). College student retention: Formula for student success. Westport, CT: Praeger.[3] Tinto, V. & Pusser, B. (2006). Moving from theory to action: Building a model of institutional action for student success. Commissioned paper presented at the 2006 Symposium of the National Postsecondary Education Cooperative (NPEC).[4] Tinto, V. (1993). Leaving college: Rethinking the causes and cures of student attrition
succinct, redundancies are avoided, etc) 5 Figures/Tables - Figures and tables are effectively used to support the discussion (e.g. they are referenced properly from the text, they complement the information given in the text, and are complete with respect to units and labels) 6 Problem Definition - A clearly stated design problem definition is presented (e.g. what need(s) does this design meet, what are important constraints, etc.) 7 Goals/Criteria - Design goals, criteria, and functional requirements are clearly defined 8 Concept Evaluation - Design alternatives considered are presented, and a clear methodology is used for the evaluation of alternatives (e.g
This research is supported by the National Science Foundation under Grant No. EEC-0648267. We also acknowledge the support of Mitchell Nathan, L. Allen Phelps and our othercolleagues in the UW-Madison School of Education. Page 15.227.12Bibliography1. Sheppard, S., Macatangay K., Colby, A., Sullivan, W. (2009). Educating Engineers: Designing for the Futureof the Field. The Carnegie Foundation for the Advancement of Teaching. San Francisco, CA: Jossey-Bass.2. Trevelyan, J. (2007). Technical coordination in engineering practice. Journal of Engineering Education, 96 (3),p. 191-204.3. Wirsbinski, S., Anderson, K. J. B., Courter, S. (2009
one “course” per semester, and they have daily contact with their problem group and afaculty member or guide. Since the early 1970’s, this PBL approach has been successful inmedical education at many institutions. There are very few engineering programs that have fullyimplemented a similar model. Are we hampered from adopting more PBL teaching models inEngineering programs because of our fundamental conceptual model of what is an engineeringeducation, and, ultimately, what is an engineer? The System ParadigmWithin the existing structure at most engineering schools, students recognize that each coursecarries equal weight towards their degree, and each course gives them an independent grade thatis equal in value towards their degree and for
, legal, and social issues surrounding the use of information, and access and use information ethically and legally7In addition, the ACRL Science and Technology Section (STS)’s ‘flavor’ of information literacystandards8 also maintains that a student ‘…understands that information literacy is an ongoingprocess and an important component of lifelong learning and recognizes the need to keep currentregarding new developments in his or her field.’Viewed from the perspective of lifelong learning, the ability to ‘determine the extent ofinformation needed,’ corresponds to articulating a ‘learning need’. ‘Access[ing] the neededinformation’ and ‘using information effectively to accomplish a specific purpose’ fits within theframework of developing
are the sameFigure 1. A Multiple Choice Question from DIRECT1.08 (Reproduced with permission.) 9) Which circuit(s) will light the bulb?For example, consider a multiple-choice question from DIRECT CI as shown above in Fig. 1.Based on the categories (A) A of analysis discussed above, the question below can be characterized asshown in Table (B)3.C The question targets common student misconceptions about current: current (C) D and order of elements, and current is “used up” in circuit. Option (A) “Pointdepends on direction (D) that1” is a distractor A and C targets misconception that current supplied by the battery is used up in the (E) B and
who did not take the junior Page 15.384.2nanosystems laboratory course).I. IntroductionNanotechnology education is evolving from the inclusion of a broad freshman/sophomore leveloverview courses to greater depth leading to certificates, concentrations, and minors. _ hasdeveloped a complete B. S. level Nanosystems Engineering Degree. Details of the structure ofthis program have been delineated in the literature1,2. The approach utilizes a common freshmanengineering sequence, a nanosystems specific sophomore introductory course, and a junior levelnanosystems seminar course. Pre-existing graduate microsystems engineering courses areutilized to
schedule Page 15.644.13Bibliography1 National Academy of Sciences. (2006). Rising Above the Gathering Storm: Energizing and Employing America for a Brighter Economic Future. Washington, DC: Author.2 National Science Board. 2008. Science and Engineering Indicators 2008, NSF 07-308. Arlington, VA: National Science Foundation, Division of Science Resources Statistics.3 National Science Foundation, Division of Science Resources Statistics, Women, Minorities, and Persons with Disabilities in Science and Engineering: 2007, NSF 07-315 (Arlington, VA; February 2007). Available from http://www.nsf.gov/statistics/wmpd/.4 Zweben, S. (2005). 2003-2004 Taulbee Survey: Record
fortransfer to a four-year institution after completion their A.S. degrees in engineering and science.Example of the articulation agreement between Drexel University and BCC is presented below.Articulation Agreement between DU and BCCTable 1 indicates all courses that could be transferred from BCC towards Drexel’s B. S. degreein AET with concentration in Mechanical Engineering Technology. The AET curriculumconsists of 187.5 quarter credits. To transfer to AET, BCC students must complete theirAssociate of Applied Science (A. A. S.) degree at BCC. According to the articulation agreementbetween DU and BCC, BCC students can transfer total of 68 semester credits to Drexel’s AETprogram, which corresponds to 91.5 DU quarter credits. Students are required to
inaggregate they appear to, while for high correlations they would be very likely to have had thesame opinion between questions. For sets that are independent, the interpretations remain thesame, although the strength or significance of those conclusions cannot be strongly asserted. It isalso important to note that the ANOVA statistical significance and correlation coefficient are allin reference to student responses based on the terms of word phases utilized in each question.Results_________ ____’s “______ ____ ___ ___” program offers an outstanding resource for graduatestudent instructor development of teaching skills through in-class mentoring of first-year collegestudents 9, 10. Aforesaid survey question categories were utilized to discern the
verses for acetylene.References [1] Jeremy Allaire. (2009, Allaire, Jeremy. “Macromedia Flash July 8) Macromedia Flash MX- A next generation rich client. [Online]. http://www.adobe.com/devnet/flash/whitepapers/richclient.pdf [2] (2009, July) Flash Player penetration. [Online]. http://www.adobe.com/products/player_census/flashplayer/ [3] C. P. Paolini and S. Bhattacharjee, "A Web Service Infrastructure for Thermochemical Data," J. Chem. Inf. Model., vol. 48(7), pp. 1511-1523, 2008. [4] C. P. Paolini and S. Bhattacharjee, "A Web Service Infrastructure for Distributed Chemical Equilibrium Computation," in Proceedings of the 6th International Conference on Computational Heat and Mass Transfer
AC 2010-1218: TEACHING INQUIRY-BASED STEM IN THE ELEMENTARYGRADES USING MANIPULATIVES: A SYSTEMIC SOLUTION REPORTLouis Nadelson, Boise State University Louis S. Nadelson is an Assistant Professor in the College of Education at Boise State University. His research agenda is conducted within the context of STEM education and includes aspects of conceptual change, inquiry, and pre-service and in-service teacher education. He has published research ranging from teacher professional development to the impact of inquiry on STEM learning. Dr. Nadelson earned a B.S. degree in Biological and Physics Science from Colorado State University, a B.A. with concentrations in computing, mathematics and
theory of delays. Retrieved from http://www.deltadynamicsinc.com6. Bozzone, V. (2002). Speed to market: Lean manufacturing for job shops (2nd ed.). New York: AMACOM.7. Celano, G., Costa, A., & Fichera, S. (2003). An evolutionary algorithm for pure fuzzy flow shop scheduling problems. International Journal of Uncertainty, Fuzziness & Knowledge-Based Systems, 11(6), 655-669.8. Choi, B. K., & You, N. K. (2006). Dispatching rules for dynamic scheduling of one-of-a-kind production. International Journal of Computer Integrated Manufacturing, 19(4), 383-392.9. Choi, S. H., & Yang, F. Y. (2005). Quick value-setting algorithms for the longest path problem of job shop scheduling. Journal of Manufacturing
Page 15.5.3continuum, and it is believed that by providing a continuum of resources to entrepreneurs andstartup companies, the probability of success is significantly increased. Details of each of theprograms listed in the figure will be discussed below. Students Companies Re sea rch • H inm an CEOs • On- campus I ncubat or Base • Tech St ar tup Boot Cam p • B- Plan Competition • 2 n d stage I ncubator I nnovat ive Concepts • H illm an Entr epr en eur s
], which resulted in the need forthis project.Methods A series of gradually more challenging homework assignments were developed for thecomputer architecture course. An overview of each assignment follows.Assignment 1—Introduction to HDL and Utilities As an introductive exercise, students implement basic components using differentdesigning schemes. Knowing how to effectively navigate through these design schemes assistthem in future homework. The following strategies are given to the students:Implement a half adder using dataflow modeling: • Outputs: S-Sum, C-Carry • Inputs: X-Bit 1, Y-Bit-2Use hierarchal and gate-level modeling to implement a full adder: • Outputs: S-Sum, C-Carry • Inputs: X-Bit1, Y-Bit-2, Z-Carry InCreate a
. Further development of the physical model and image processing algorithms shouldmake it possible to control virtual objects from any location in cyber space using a laptop withembedded web-camera.Bibliography1. Bluemel, E., Hintze, A., Schulz, T., Schumann, M., & Stuering, S. (2003). Virtual environments for the training of maintenance and service tasks. In Proceedings of the 2003 Winter Simulation Conference, USA, 2001-2007.2 De Lara, J., & Alfonseca, M. (2001). Constructing simulation-based web documents. IEEE MultiMedia, 8, 42- 49.3 Fishwick, P. A. (1996). Web-based simulation: Some personal observations. Proceedings of the 1996 Winter Simulation Conference, USA, 772-779.4 Manojlovich, J., Prasithsangaree, P
AC 2010-1518: REFINING A CRITICAL THINKING RUBRIC FOR ENGINEERINGPatricia Ralston, University of Louisville Dr. Patricia A. S. Ralston is Chair of the Department of Engineering Fundamentals at the University of Louisville. She holds a joint appointment in Engineering Fundamentals and in Chemical Engineering. Dr. Ralston teaches undergraduate engineering mathematics and is currently involved, with other Speed faculty, in educational research on effective use of Tablet PCs in engineering education and the incorporation of critical thinking in engineering education. Her fields of expertise include process modeling, simulation, and process control.Cathy Bays, University of Louisville
Gogotsi — Professor of Materials Science & Engineering in Drexel’s College of Engineering and Director of the A.J. Drexel Nanotechnology Institute (DNI). Dr. Gogotsi’s research is focused on the fundamental and applied aspects of synthesis and characterization of carbon nanomaterials (nanotubes, nanodiamond and nanoporous carbons), ceramic nanoparticles (whiskers, nanowires, etc) and composites. Dr. Gogotsi has extensive experience with NSF-funded education and training programs including an IGERT Ph.D. training program and an RET teacher training program, both of which are focused on nanotechnology.Dhruv Sakalley, Drexel University Dhruv Sakalley received a B. S. degree in engineering from
filter after the DAC channel. O3. Compute and analyze signal spectra using DFT/FFT algorithms. O4. Analyze filter frequency response; perform digital filtering; verify the signal spectral effects. O5. Design FIR filters and implement them in real-time using the floating-point format. O6. Design IIR filters and implement them in real-time using the floating-point format. O7. Waveform generation using digital filter(s). O8. Develop comprehensive real-time DSP project and demonstrate the implementation.B. DSP Laboratories with MATLAB and TI TSM320C67C13 DSKIn order to fulfill our course learning outcomes, we have developed our labs using bothMATLAB and TMS320C6713 DSK
protocol was also used to alleviate inherent issues thatarise when attempting to use verbal protocol to examine “team” interaction including tacitgestures not verbalized and written communication, such as notes and sketches 20. Page 15.869.7 The playground problem coding scheme was congruent with the approach used in priorstudies 7, 21-22. The data were coded into these nine categories presented below by Atman et al.8: Design Activity Example(s) Coded Example(s)(PD) PROBLEM DEFINITION Reading, re-reading, or rehashing “That means we’ll the
DescriptivesLicense(s) 95% Confidence Interval for Mean N Mean Std. Deviation Std. Error Lower Bound Upper Bound Minimum MaximumKindergarten 20 3.10 1.447 .324 2.42 3.78 2 7Primary 181 2.88 1.208 .090 2.71 3.06 2 10Secondary 2773 2.63 1.203 .023 2.58 2.67 2
National Technical Conference and Exhibition, New Orleans, LA, March 31- April 2.7. Kaiser, M., Pulsipher, A. 2007. Generalized Functional Models for Drilling Cost Estimation. SPE Drilling and Completion, June: 67-73.8. Kitchel, B., Moore, S., Banks, W., Borland, B. 1997. Probabilistic Drilling-Cost Estimating. SPE Computer Applications, August: 121-125. Page 15.716.109. Murtha, J. 1997. Monte Carlo Simulation: Its Status and Future. JPT, April: 361-373.10. Noerager, J., White, J., Floetra, A., Dawson, R. 1987. Drilling Time Predictions From Statistical Analysis. Paper 16164 presented at the SPE/IADC Drilling
funded-research program. His research interests include Learning/Collaborative Systems, Software Engineering, Open Source Development, Computer Science Education.Raghvinder Sangwan, Pennsylvania State University, Great Valley Raghvinder S. Sangwan, an Associate Professor of Software Engineering at the Pennsylvania State University's School of Graduate Professional Studies, holds a Ph.D. in Computer and Information Sciences from Temple University. He joined Penn State in 2003 after a 7+ year career in industry, where he worked mostly with large software-intensive systems in the domains of healthcare, automation, transportation and mining. His teaching and research involves analysis, design
. Page 15.149.6The F value is calculated as: sbt 2 Fcalc ? swt 2where 2 π f s f 2 − π m sm 2 swt ? π f − πm 2 2 sbt ? ∗ nf yf / y + − n ∗y m m
beenintroducing the student participants, who are earning Ph.D. degrees in research Page 15.532.2universities, to the possibility of more teaching-focused careers in institutions servingundergraduates. At UC, practical experience and mentoring in a teaching-focusedprogram could be obtained by PFF participants in the University's College of AppliedScience, which offers two-year and four-year technology degrees in many fields. Butchanges in career prospects for new engineering Ph.D.'s, along with major changes inacademic programs at UC, are providing the impetus for changes in the PFF program.We describe some motivating factors in the changes we have made and are