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8/5/2020: Course Change Form MGMT 693
No full textThe credit hours for the MGMT 693 Graduate Internship Course will be changed to either two or four credit hours based on the number of clock hours worked per term. Four-hour credit enrollment will count as a full-time course load. The course will be designed for the on-campus graduate students only. Students can register for the course more than once. It has the built-in flexibility to allow a graduate student to work full-time for up to two terms or work part-time while taking courses
8/5/2020: Course Change Form MGMT 417
No full textThe course number and course title change is to match the graduate MGMT-679 Leadership course number and course title for the combined graduate-undergraduate course.
The description has been updated for 400/600 congruency with MGMT679
2/12/2020: Emeritus Revision
No full textThe President’s proposal of “University Policy #19” prompted my examination of the Faculty Handbook provisions for emeritus designations. The two overlap but are different. These different standards and procedures to achieve the same end produce confusion. The solution proposed here is to replace Faculty Handbook section 6.2.5 with an improved section informed by University Policy #19 and delete Faculty Handbook section 6.5.6. Those sections of University Policy #19 that do not relate to faculty would be retained in that policy while those relating to faculty would be deleted
2/26/2020: BUSN 312
No full textThis change is intended to retain important topics of business process analysis that would be lost given the elimination of BUSN 361, Lean Operations Management. However, an opportunity exists with this situation to transform this course from a direct mathematical problem-solution teaching method to a more holistic problem-solving, student-active approach. Such problem-solving methods are common in the workplace, but not often the approach in formal education. To accommodate space in the course, some of the less common operations research and redundant statistical procedures will be removed. On balance these changes in the course will better prepare students to confidently address problems assigned in the workplace with proven approaches that encourage data-based decision alternatives and communication of implementation plans
Structural Analysis and Design Modification of Seat Rail Structures in Various Operating Conditions
No full textThis paper is based on, and in continuation of the work previously published in ASEE NCS Conference held in Grand Rapids, MI [1]. Automotive seating rail structures are one of the key components in the automotive industry because they carry the entire weight of passenger and they hold the structure for seating foams and other assembled key components such as side airbag and seatbelt systems. The entire seating is supported firmly and attached to the bottom bodywork of the vehicle through the linkage assembly called the seat rails. Seat rails are adjustable in their longitudinal motion which plays an important role in giving the passengers enough leg room to make them feel comfortable. Therefore, seat rails under the various operating conditions, should be able to withstand the weight of the passenger along with the other assembled parts as mentioned above. Also, functional requirements such as crash safety is very important to avoid or to minimize injuries to the occupants. Keeping the above requirements in view, the goal of this paper is to perform simulation studies on the seat rails under different operating conditions using CAE tool for structural, vibration (dynamic), durability, and crash analyses. Different grades of steel, aluminum, and multi-materials have been used in the study. Based on these studies, a slightly modified seat rail structure design is proposed to increase the fatigue life, decrease the damage percentage, increase the resonant frequencies, and to increase the amount of absorbed crush energy. The results of crash analysis are not presented in this paper
September 4, 2020: Safe Return to Campus Fall 2020 Update 4
Get PDFThe COVID-19 Response Team and University leadership continue to closely monitor the local, state and national situation in consultation with health experts and in accordance with government guidelines. If conditions change, the University will alter plans to ensure the safety of the campus community. Fall Term 2020 classes begin on October 5. As the University’s COVID-19 Response Team continues to finalize details for the fall, please carefully review these update
Rotating Machinery, Optical Methods & Scanning LDV Methods, Volume 6
No full textRotating Machinery, Optical Methods & Scanning LDV Methods, Volume 6: Proceedings of the 38th IMAC, A Conference and Exposition on Structural Dynamics, 2020, the sixth volume of eight from the Conference brings together contributions to this important area of research and engineering. The collection presents early findings and case studies on fundamental and applied aspects of Structural Health Monitoring, including papers on: Novel Techniques, Optical Methods,Scanning LDV Methods, Photogrammetry & DIC, Rotating Machinery
Metabolite Structure Assignment Using In Silico NMR Techniques
No full textA major challenge for metabolomic analysis is to obtain an unambiguous identification of the metabolites detected in a sample. Among metabolomics techniques, NMR spectroscopy is a sophisticated, powerful, and generally applicable spectroscopic tool that can be used to ascertain the correct structure of newly isolated biogenic molecules. However, accurate structure prediction using computational NMR techniques depends on how much of the relevant conformational space of a particular compound is considered. It is intrinsically challenging to calculate NMR chemical shifts using high-level DFT when the conformational space of a metabolite is extensive. In this work, we developed NMR chemical shift calculation protocols using a machine learning model in conjunction with standard DFT methods. The pipeline encompasses the following steps: (1) conformation generation using a force field (FF)-based method, (2) filtering the FF generated conformations using the ASE-ANI machine learning model, (3) clustering of the optimized conformations based on structural similarity to identify chemically unique conformations, (4) DFT structural optimization of the unique conformations, and (5) DFT NMR chemical shift calculation. This protocol can calculate the NMR chemical shifts of a set of molecules using any available combination of DFT theory, solvent model, and NMR-active nuclei, using both user-selected reference compounds and/or linear regression methods. Our protocol reduces the overall computational time by 2 orders of magnitude over methods that optimize the conformations using fully ab initio methods, while still producing good agreement with experimental observations. The complete protocol is designed in such a manner that makes the computation of chemical shifts tractable for a large number of conformationally flexible metabolites