1,276 research outputs found

    A novel penalty-based reduced order modelling method for dynamic analysis of joint structures

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    This work proposes a new reduced order modelling method to improve the computational efficiency for the dynamic simulation of a jointed structures with localized contact friction non-linearities. We reformulate the traditional equation of motion for a joint structure by linearising the non-linear system on the contact interface and augmenting the linearised system by introducing an internal non-linear penalty variable. The internal variable is used to compensate the possible non-linear effects from the contact interface. Three types of reduced basis are selected for the Galerkin projection, namely, the vibration modes (VMs) of the linearised system, static modes (SMs) and also the trial vector derivatives (TVDs) vectors. Using these reduced basis, it would allow the size of the internal variable to change correspondingly with the number of active non-linear DOFs. The size of the new reduced order model therefore can be automatically updated depending on the contact condition during the simulations. This would reduce significantly the model size when most of the contact nodes are in a stuck condition, which is actually often the case when a jointed structure vibrates. A case study using a 2D joint beam model is carried out to demonstrate the concept of the proposed method. The initial results from this case study is then compared to the state of the art reduced order modeling

    Investigating the missing data mechanism in quality of life outcomes: a comparison of approaches

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    Background: Missing data is classified as missing completely at random (MCAR), missing at random (MAR) or missing not at random (MNAR). Knowing the mechanism is useful in identifying the most appropriate analysis. The first aim was to compare different methods for identifying this missing data mechanism to determine if they gave consistent conclusions. Secondly, to investigate whether the reminder-response data can be utilised to help identify the missing data mechanism. Methods: Five clinical trial datasets that employed a reminder system at follow-up were used. Some quality of life questionnaires were initially missing, but later recovered through reminders. Four methods of determining the missing data mechanism were applied. Two response data scenarios were considered. Firstly, immediate data only; secondly, all observed responses (including reminder-response). Results: In three of five trials the hypothesis tests found evidence against the MCAR assumption. Logistic regression suggested MAR, but was able to use the reminder-collected data to highlight potential MNAR data in two trials. Conclusion: The four methods were consistent in determining the missingness mechanism. One hypothesis test was preferred as it is applicable with intermittent missingness. Some inconsistencies between the two data scenarios were found. Ignoring the reminder data could potentially give a distorted view of the missingness mechanism. Utilising reminder data allowed the possibility of MNAR to be considered.The Chief Scientist Office of the Scottish Government Health Directorate. Research Training Fellowship (CZF/1/31

    AGS Position Statement: Making Medical Treatment Decisions for Unbefriended Older Adults

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    Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135987/1/jgs14586_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/135987/2/jgs14586.pd

    The Grizzly, November 23, 1993

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    Dr. Davidson to Receive Cottrel Award • U.C.\u27s Questions are Answered • It\u27s Yes to NAFTA • A Dose of Reality • Touch my Flugel! • Exam Schedule • The Way to a Student\u27s Heart is Through His Stomach • Beer: A Historical Precedent? • A Dissatisfied Sanitation Worker Tells Ursinus to Clean Up its Act • Literary Society • Manning Retires as Soccer Coachhttps://digitalcommons.ursinus.edu/grizzlynews/1326/thumbnail.jp
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