3854 research outputs found
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1/16/2019: Course Change Form MGMT 314
No full textThis proposal realigns the course content to better support managerial application of the course material
1/16/2019: Course Change Form BUSN 483
No full textDeletion since this course is being combined with BUSN 482
Full-field Strain Prediction using Mode Shapes Measured with Digital Image Correlation
No full textHealth and condition monitoring of composite structures are critical in engineering especially in the wind, civil, aviation, and auto industries. However, considering the geometry and size of the structures, analyzing critical locations can become challenging. Traditional sensors such as strain-gauges are widely used to collect operating data, but these conventional methods cannot present full-field data and only show the measurement data at a few discrete locations. Baqersad and Bharadwaj have recently developed a Strain Expansion-Reduction Approach (SERA) to bridge this gap and to expand a limited set of measurements and obtain full-field strain data. This approach uses the strain mode shapes from Finite Element Analysis (FEA) to develop a transformation matrix that expands the limited strain data measured using strain-gauges and predicts full-field strain over the entire structure. However, for many structures, it is challenging to accurately model the geometry or material properties for finite element analysis. Many of these structures are made of composite materials and material modes for these structures might not be readily available. In this paper, we use the strain mode shapes extracted using Digital Image Correlation (DIC) in the expansion process. These mode shapes represent actual properties of the structures. The strain mode shapes for a sample structure of a product can be extracted in a test facility using this approach (e.g., a wind turbine blade or a suspension A-arm). An in situ limited set of measurement can be performed using strain-gauges or fiber optic sensors on the structure. Then, the limited data can be expanded using the strain mode shapes to extract full-field strain results. To demonstrate the merit of the approach, we applied the proposed technique to expand real-time operating data measured using a few strain-gauges mounted to a composite spoiler. Using a transformation matrix generated using the DIC operating deflection shapes, the expansion technique predicted the full field strain on the spoiler. It was shown that the proposed methodology could effectively expand the strain data at limited locations to accurately predict the strain at locations where no sensors were placed
Review of the Augmented Reality Systems for Shoulder Rehabilitation
Get PDFLiterature shows an increasing interest for the development of augmented reality (AR) applications in several fields, including rehabilitation. Current studies show the need for new rehabilitation tools for upper extremity, since traditional interventions are less effective than in other body regions. This review aims at: Studying to what extent AR applications are used in shoulder rehabilitation, examining wearable/non-wearable technologies employed, and investigating the evidence supporting AR effectiveness. Nine AR systems were identified and analyzed in terms of: Tracking methods, visualization technologies, integrated feedback, rehabilitation setting, and clinical evaluation. Our findings show that all these systems utilize vision-based registration, mainly with wearable marker-based tracking, and spatial displays. No system uses head-mounted displays, and only one system (11%) integrates a wearable interface (for tactile feedback). Three systems (33%) provide only visual feedback; 66% present visual-audio feedback, and only 33% of these provide visual-audio feedback, 22% visual-audio with biofeedback, and 11% visual-audio with haptic feedback. Moreover, several systems (44%) are designed primarily for home settings. Three systems (33%) have been successfully evaluated in clinical trials with more than 10 patients, showing advantages over traditional rehabilitation methods. Further clinical studies are needed to generalize the obtained findings, supporting the effectiveness of the AR applications
Methods of assaying volatile oxygenated organic compounds in effluent samples by gas chromatography—A review
No full textThe paper is a review of the procedures for the determination of volatile and semivolatile oxygenated organic compounds (O-VOCs) in effluent samples by gas chromatography. Current trends and outlook for individual steps of the procedure for the determination of O-VOCs in effluents are discussed. The available sample preparation techniques and their limitations are described along with GC capillary columns used for O-VOCs separation and selective and universal detectors used for their determination. The results of determination of O-VOC content in various types of real effluents are presented. The lack of legal regulations regarding the presence of the majority of O-VOCs is pointed out as well as the availability of just a few procedures allowing a comprehensive evaluation of the O-VOC content in effluents
AutoWaze: Towards Automatic Event Inference in Intelligent Transportation Systems
No full textTraffic monitoring is one of the key challenges in Intelligent Transportation Systems (ITS). In this paper, we propose to build a crowdsourcing application for traffic monitoring. The novelty of the proposed approach is that visual data is collected to enable automatic event inference with the recent advance in Computer Vision. The challenge is that mobile devices are not capable of handling visual task processing in high accuracy. We propose to build a networked system so that mobile devices can offload data via available wireless access interfaces (e.g., 4G LTE, WiFi, DSRC) to edge servers, e.g., GENI Rack. We plan to use the testbed at Kettering University to validate the proposed approach
The Return on Investment of Orthopaedic Fellowship Training: A Ten-year Update
No full textBackground: Over 90% of graduating orthopaedic residents now pursue fellowship training, and only 15% of practicing orthopaedic surgeons now characterize themselves as generalists. Fellowship training has significant financial effects due to both opportunity cost of that year of training and changes in compensation throughout one\u27s career. The purpose of this study was to estimate the financial return on investment by pursuing additional training in an orthopaedic fellowship versus general practice. Methods: Using described techniques of financial analysis, net present value (NPV), internal rate of return (IRR), and break-even point were estimated over the average working career length of an orthopaedic surgeon. Compensation data were drawn from the American Medical Group Association physician compensation surveys. Seven fellowships were studied and referenced to a career in general orthopaedic practice. Results: Fellowship training in spine surgery yields the highest return on investment with a break-even point of 5 years. Adult reconstruction has a positive NPV and IRR, but when corrected for number of hours worked per week offers no productivity advantage to general practice. Sports medicine and trauma offer neutral returns, but when corrected for work hours, NPV and IRR both become negative. Hand, pediatrics, and foot and ankle never break even following the loss of compensation realized during fellowship year. Discussion: The recent trend across all medical specialties has been for increased fellowship training and subspecialization. There are numerous reasons to pursue fellowship training, both personal and financial. This study presents an updated estimate of the financial impact of fellowship training in orthopaedics. This analysis demonstrates that selecting different fellowships can generate positive, negative, or neutral financial returns. This study has the potential to influence residents\u27 decisions to pursue general practice versus fellowship training and identifies economic drivers, which may lead to preferential pursuit of certain subspecialties
