Design of experiments for bivariate binary responses modelled by Copula functions

Optimal design for generalized linear models has primarily focused on univariate data. Often experiments are performed that have multiple dependent responses described by regression type models, and it is of interest and of value to design the experiment for all these responses. This requires a multivariate distribution underlying a pre-chosen model for the data. Here, we consider the design of experiments for bivariate binary data which are dependent. We explore Copula functions which provide a rich and flexible class of structures to derive joint distributions for bivariate binary data. We present methods for deriving optimal experimental designs for dependent bivariate binary data using Copulas, and demonstrate that, by including the dependence between responses in the design process, more efficient parameter estimates are obtained than by the usual practice of simply designing for a single variable only. Further, we investigate the robustness of designs with respect to initial parameter estimates and Copula function, and also show the performance of compound criteria within this bivariate binary setting.

CD73 and CD29 concurrently mediate the mechanically induced decrease of migratory capacity of mesenchymal stromal cells

The assumption that mesenchymal stromal cell (MSC)-based therapies are capable of augmenting physiological regeneration processes has fostered intensive basic and clinical research activities. However, to achieve sustained therapeutic success in vivo, not only the biological, but also the mechanical microenvironment of MSCs during these regeneration processes needs to be taken into account. This is especially important for e.g., bone fracture repair, since MSCs present at the fracture site undergo significant biomechanical stimulation. This study has therefore investigated cellular characteristics and the functional behaviour of MSCs in response to mechanical loading. Our results demonstrated a reduced expression of MSC surface markers CD73 (ecto-5’-nucleotidase) and CD29 (integrin β1) after loading. On the functional level, loading led to a reduced migration of MSCs. Both effects persisted for a week after the removal of the loading stimulus. Specifi c inhibition of CD73/CD29 demonstrated their substrate dependent involvement in MSC migration after loading. These results were supported by scanning electron microscopy images and phalloidin staining of actin fi laments displaying less cell spreading, lamellipodia formation and actin accumulations. Moreover, focal adhesion kinase and Src-family kinases were identified as candidate downstream targets of CD73/CD29 that might contribute to the mechanically induced decrease in MSC migration. These results suggest that MSC migration is controlled by CD73 CD29, which in turn are regulated by mechanical stimulation of cells. We therefore speculate that MSCs migrate into the fracture site, become mechanically entrapped, and thereby accumulate to fulfil their regenerative functions.

Academic skills and scholarship for nurses : building aspiration and preparing nurses for academic success

Academic Skills and Scholarship for Nurses is a pilot programme which addresses academic aspiration and study preparedness of mature aged students. It is a series of four workshops designed and implemented by QUT Library staff in collaboration with Nursing and Midwifery academics, for pre- and post- registration nursing staff within the region of Caboolture, Redcliffe and Kilcoy. The programme extends QUT Library’s learning and study support expertise to the local community. The intended outcomes of the programme are fourfold. Firstly, encourage educational aspirations of mature age students, to establish realistic expectations and practical strategies for beginning tertiary study. Secondly, skills developed will be congruent with lifelong learning principles and continuing professional development requirements of professional nursing bodies. Thirdly, alignment with QUT strategies for widening participation in higher education and finally, strengthen existing relationships between academic and professional staff, and QUT and the local community for the benefit of all stakeholders.

Ensuring student success : the role of support services in improving the quality of the student learning experience

This paper is based on the premise that universities have an obligation to provide adequate student support services, such as learning assistance (that is, assistance with academic writing and other study skills) and that in order to be effective such services must be responsive to the wider policy and social implications of student attrition and retention. The paper outlines briefly some of the factors that have influenced the development of learning assistance practices in Australia and America. This is followed by an account of experiences at one Australian metropolitan university where learning assistance service provision shifted from a decentralised, faculty-based model to a centralised model of service delivery. This shift was in response to concerns about lack of quality and consistency in a support model dependent upon faculty resources yet a follow up study identified other problems in the centralised delivery of learning assistance services. These problems, clustered under the heading contextualised versus decontextualised learning assistance, include the relevance of generic learning assistance services to students struggling with specific course related demands; the apparent tensions between challenging students and assisting students at risk of failure; and variations in the level of collaboration between learning advisers and academic staff in supporting students in the learning environment. These problems are analysed using the theoretical modelling derived from the tools made available through cultural historical activity theory and expansive visibilisation (Engeström & Miettinen, 1999).

Managing knowledge for enterprise systems in indian organizations: Case study insights

Many Enterprise Systems (ES) projects have reported nil or detrimental impacts despite the substantial investment in the system. Having expected positive outcomes for the organization and its functions through the weighty spend, the effective management of ES-related knowledge has been suggested as a critical success factor for these ES projects in ES implementations. This paper suggests theoretical views purporting the importance of understanding on knowledge management for ES success. To explain the complex, dynamic and multifaceted of knowledge management, we adopt the concepts in Learning Network Theory. We then conceptualized the impact of knowledge management on ES by analyzing five case studies in several industries in India, based on the Knowledge-based Theory of the Firm that captures the performance of the system.

Evaluating multivariate volatility forecasts : how effective are statistical and economic loss functions?

Multivariate volatility forecasts are an important input in many financial applications, in particular portfolio optimisation problems. Given the number of models available and the range of loss functions to discriminate between them, it is obvious that selecting the optimal forecasting model is challenging. The aim of this thesis is to thoroughly investigate how effective many commonly used statistical (MSE and QLIKE) and economic (portfolio variance and portfolio utility) loss functions are at discriminating between competing multivariate volatility forecasts. An analytical investigation of the loss functions is performed to determine whether they identify the correct forecast as the best forecast. This is followed by an extensive simulation study examines the ability of the loss functions to consistently rank forecasts, and their statistical power within tests of predictive ability. For the tests of predictive ability, the model confidence set (MCS) approach of Hansen, Lunde and Nason (2003, 2011) is employed. As well, an empirical study investigates whether simulation findings hold in a realistic setting. In light of these earlier studies, a major empirical study seeks to identify the set of superior multivariate volatility forecasting models from 43 models that use either daily squared returns or realised volatility to generate forecasts. This study also assesses how the choice of volatility proxy affects the ability of the statistical loss functions to discriminate between forecasts. Analysis of the loss functions shows that QLIKE, MSE and portfolio variance can discriminate between multivariate volatility forecasts, while portfolio utility cannot. An examination of the effective loss functions shows that they all can identify the correct forecast at a point in time, however, their ability to discriminate between competing forecasts does vary. That is, QLIKE is identified as the most effective loss function, followed by portfolio variance which is then followed by MSE. The major empirical analysis reports that the optimal set of multivariate volatility forecasting models includes forecasts generated from daily squared returns and realised volatility. Furthermore, it finds that the volatility proxy affects the statistical loss functions’ ability to discriminate between forecasts in tests of predictive ability. These findings deepen our understanding of how to choose between competing multivariate volatility forecasts.