Transforming beyond self: fluidity of parent identity in family decision-making

When a household welcomes a new infant a transformation occurs whereby household routines, values and decisions change. This research explores how decision-making is influenced by fluctuating identity subjectivities. We explore longitudinally, using a family identity framework, how the transitioning between self, couple and family self-identities influences the decisions made regarding social issues, in this case infant feeding. Results indicate that decision-making during a period of transformation is not straightforward, relying on a multiplicity of identities that are constantly renegotiated and dependent on other influences. Decisions made conform to the identity-construct-of-the-moment, but are fluid and subject to change, such that pinpointing causal pathways is inappropriate. Implications for influencing the consumption of social behaviors for consumer researchers are one size does not fit all and require an in-depth understanding of the fluidity of decision-making. Consequently, social marketing strategies need to be tailored to constructed identities and flexible across time to remain influential.

The High Court and the Marlboro Man: The plain packaging decision

The High Court of Australia’s ruling on the plain packaging of tobacco products is one of the great constitutional cases of our age. The ruling will resonate throughout the world - as other countries will undoubtedly seek to emulate Australia’s plain packaging regime.

A decision support framework for prioritization of engineering asset management activities under uncertainty

Engineers and asset managers must often make decisions on how to best allocate limited resources amongst different interrelated activities, including repair, renewal, inspection, and procurement of new assets. The presence of project interdependencies and the lack of sufficient information on the true value of an activity often produce complex problems and leave the decision maker guessing about the quality and robustness of their decision. In this paper, a decision support framework for uncertain interrelated activities is presented. The framework employs a methodology for multi-criteria ranking in the presence of uncertainty, detailing the effect that uncertain valuations may have on the priority of a particular activity. The framework employs employing semi-quantitative risk measures that can be tailored to an organisation and enable a transparent and simple-to-use uncertainty specification by the decision maker. The framework is then demonstrated on a real world project set from a major Australian utility provider.

Assessing testamentary and decision-making capacity: Approaches and models

The need for better and more accurate assessments of testamentary and decision-making capacity grows as Australian society ages and incidences of mentally disabling conditions increase. Capacity is a legal determination, but one on which medical opinion is increasingly being sought. The difficulties inherent within capacity assessments are exacerbated by the ad hoc approaches adopted by legal and medical professionals based on individual knowledge and skill, as well as the numerous assessment paradigms that exist. This can negatively affect the quality of assessments, and results in confusion as to the best way to assess capacity. This article begins by assessing the nature of capacity. The most common general assessment models used in Australia are then discussed, as are the practical challenges associated with capacity assessment. The article concludes by suggesting a way forward to satisfactorily assess legal capacity given the significant ramifications of getting it wrong.

Financial Accounting: Reporting, Analysis and Decision Making [5th ed.]

In recent years accounting education has seen numerous changes to the way financial accounting is taught. These changes reflect the demands of an ever-changing business world, opportunities created by new technology and instructional technologies, and an increased understanding of how students learn. The foundation of Financial Accounting is based on a number of unique principles and innovations in accounting education. The objective of Financial Accounting is to provide students with an understanding of those concepts that are fundamental to the preparation and use of accounting information. Most students will forget procedural details within a short period of time. On the other hand, concepts, if well taught, should be remembered for a lifetime. Concepts are especially important in a world where the details are constantly changing. Students learn best when they are actively engaged. The overriding pedagogical objective of Financial Accounting is to provide students with continual opportunities for active learning. One of the best tools for active learning is strategically placed questions. Discussions are framed by questions, often beginning with rhetorical questions and ending with review questions, and our analytical devices, called decision-making toolkits, use key questions to demonstrate the purpose of each.

High threshold of β1 integrin inhibition required to block collagen I-induced membrane type-1 matrix metalloproteinase (MT1-MMP) activation of matrix metalloproteinase 2 (MMP-2)

Background Matrix metalloproteinase-2 (MMP-2) is an endopeptidase that facilitates extracellular matrix remodeling and molecular regulation, and is implicated in tumor metastasis. Type I collagen (Col I) regulates the activation of MMP-2 through both transcriptional and post-transcriptional means; however gaps remain in our understanding of the involvement of collagen-binding ?1 integrins in collagen-stimulated MMP-2 activation. Methods Three ?1 integrin siRNAs were used to elucidate the involvement of ?1 integrins in the Col I-induced MMP-2 activation mechanism. ?1 integrin knockdown was analyzed by quantitative RT-PCR, Western Blot and FACS analysis. Adhesion assay and collagen gel contraction were used to test the biological effects of ?1 integrin abrogation. MMP-2 activation levels were monitored by gelatin zymography. Results All three ?1 integrin siRNAs were efficient at ?1 integrin knockdown and FACS analysis revealed commensurate reductions of integrins ?2 and ?3, which are heterodimeric partners of ?1, but not ?V, which is not. All three ?1 integrin siRNAs inhibited adhesion and collagen gel contraction, however only the siRNA showing the greatest magnitude of ?1 knockdown inhibited Col I-induced MMP-2 activation and reduced the accompanying upregulation of MT1-MMP, suggesting a dose response threshold effect. Re-transfection with codon-swapped ?1 integrin overcame the reduction in MMP-2 activation induced by Col-1, confirming the ?1 integrin target specificity. MMP-2 activation induced by TPA or Concanavalin A (Con A) was not inhibited by ?1 integrin siRNA knockdown. Conclusion Together, the data reveals that strong abrogation of ?1 integrin is required to block MMP-2 activation induced by Col I, which may have implications for the therapeutic targeting of ?1 integrin.

Linear programming for large-scale Markov decision problems

We consider the problem of controlling a Markov decision process (MDP) with a large state space, so as to minimize average cost. Since it is intractable to compete with the optimal policy for large scale problems, we pursue the more modest goal of competing with a low-dimensional family of policies. We use the dual linear programming formulation of the MDP average cost problem, in which the variable is a stationary distribution over state-action pairs, and we consider a neighborhood of a low-dimensional subset of the set of stationary distributions (defined in terms of state-action features) as the comparison class. We propose a technique based on stochastic convex optimization and give bounds that show that the performance of our algorithm approaches the best achievable by any policy in the comparison class. Most importantly, this result depends on the size of the comparison class, but not on the size of the state space. Preliminary experiments show the effectiveness of the proposed algorithm in a queuing application.

Robust iris recognition using decision fusion and degradation modelling

This thesis investigates the use of fusion techniques and mathematical modelling to increase the robustness of iris recognition systems against iris image quality degradation, pupil size changes and partial occlusion. The proposed techniques improve recognition accuracy and enhance security. They can be further developed for better iris recognition in less constrained environments that do not require user cooperation. A framework to analyse the consistency of different regions of the iris is also developed. This can be applied to improve recognition systems using partial iris images, and cancelable biometric signatures or biometric based cryptography for privacy protection.

Effect of decellularization on the load-bearing characteristics of articular cartilage matrix

The application of decellularized extracellular matrices to aid tissue regeneration in reconstructive surgery and regenerative medicine has been promising. Several decellularization protocols for removing cellular materials from natural tissues such as heart valves are currently in use. This paper evaluates the feasibility of potential extension of this methodology relative to the desirable properties of load bearing joint tissues such as stiffness, porosity and ability to recover adequately after deformation to facilitate physiological function. Two decellularization protocols, namely: Trypsin and Triton X-100 were evaluated against their effects on bovine articular cartilage, using biomechanical, biochemical and microstructural techniques. These analyses revealed that decellularization with trypsin resulted in severe loss of mechanical stiffness including deleterious collapse of the collagen architecture which in turn significantly compromised the porosity of the construct. In contrast, triton X-100 detergent treatment yielded samples that retain mechanical stiffness relative to that of the normal intact cartilage sample, but the resulting construct contained ruminant cellular constituents. We conclude that both of these common decellularization protocols are inadequate for producing constructs that can serve as effective replacement and scaffolds to regenerate articular joint tissue.

Director independence or decision bias? An investigation of alternative sources of agency costs in board decision making

Research into boards traditionally focuses on independent monitoring of management, with studies focused on the effect of board independence on firm performance. This thesis aims to broaden the research tradition by consolidating prior research and investigating how agents may circumvent independent monitoring. Meta-analysis of previous board independence-firm performance studies indicated no systematic relationship between board independence and firm performance. Next, a series of experiments demonstrated that the presentation of recommendations to directors may bias decision making irrespective of other information presented and the independence of the decision maker. Together, results suggest that independence may be less important than the agent's motivation to misdirect the monitoring process.

Feasibility of autologous bone marrow mesenchymal stem cell-derived extracellular matrix scaffold for cartilage tissue engineering

Extracellular matrix (ECM) materials are widely used in cartilage tissue engineering. However, the current ECM materials are unsatisfactory for clinical practice as most of them are derived from allogenous or xenogenous tissue. This study was designed to develop a novel autologous ECM scaffold for cartilage tissue engineering. The autologous bone marrow mesenchymal stem cell-derived ECM (aBMSC-dECM) membrane was collected and fabricated into a three-dimensional porous scaffold via cross-linking and freeze-drying techniques. Articular chondrocytes were seeded into the aBMSC-dECM scaffold and atelocollagen scaffold, respectively. An in vitro culture and an in vivo implantation in nude mice model were performed to evaluate the influence on engineered cartilage. The current results showed that the aBMSC-dECM scaffold had a good microstructure and biocompatibility. After 4 weeks in vitro culture, the engineered cartilage in the aBMSC-dECM scaffold group formed thicker cartilage tissue with more homogeneous structure and higher expressions of cartilaginous gene and protein compared with the atelocollagen scaffold group. Furthermore, the engineered cartilage based on the aBMSC-dECM scaffold showed better cartilage formation in terms of volume and homogeneity, cartilage matrix content, and compressive modulus after 3 weeks in vivo implantation. These results indicated that the aBMSC-dECM scaffold could be a successful novel candidate scaffold for cartilage tissue engineering.

A simple Bayesian decision-theoretic design for dose-finding trials

A flexible and simple Bayesian decision-theoretic design for dose-finding trials is proposed in this paper. In order to reduce the computational burden, we adopt a working model with conjugate priors, which is flexible to fit all monotonic dose-toxicity curves and produces analytic posterior distributions. We also discuss how to use a proper utility function to reflect the interest of the trial. Patients are allocated based on not only the utility function but also the chosen dose selection rule. The most popular dose selection rule is the one-step-look-ahead (OSLA), which selects the best-so-far dose. A more complicated rule, such as the two-step-look-ahead, is theoretically more efficient than the OSLA only when the required distributional assumptions are met, which is, however, often not the case in practice. We carried out extensive simulation studies to evaluate these two dose selection rules and found that OSLA was often more efficient than two-step-look-ahead under the proposed Bayesian structure. Moreover, our simulation results show that the proposed Bayesian method's performance is superior to several popular Bayesian methods and that the negative impact of prior misspecification can be managed in the design stage.

Discussion of “Generalized estimating equations: Notes on the choice of the working correlation matrix”

Objective To discuss generalized estimating equations as an extension of generalized linear models by commenting on the paper of Ziegler and Vens "Generalized Estimating Equations. Notes on the Choice of the Working Correlation Matrix". Methods Inviting an international group of experts to comment on this paper. Results Several perspectives have been taken by the discussants. Econometricians have established parallels to the generalized method of moments (GMM). Statisticians discussed model assumptions and the aspect of missing data Applied statisticians; commented on practical aspects in data analysis. Conclusions In general, careful modeling correlation is encouraged when considering estimation efficiency and other implications, and a comparison of choosing instruments in GMM and generalized estimating equations, (GEE) would be worthwhile. Some theoretical drawbacks of GEE need to be further addressed and require careful analysis of data This particularly applies to the situation when data are missing at random.

Decision-theoretic designs for dose-finding clinical trials with multiple outcomes

A decision-theoretic framework is proposed for designing sequential dose-finding trials with multiple outcomes. The optimal strategy is solvable theoretically via backward induction. However, for dose-finding studies involving k doses, the computational complexity is the same as the bandit problem with k-dependent arms, which is computationally prohibitive. We therefore provide two computationally compromised strategies, which is of practical interest as the computational complexity is greatly reduced: one is closely related to the continual reassessment method (CRM), and the other improves CRM and approximates to the optimal strategy better. In particular, we present the framework for phase I/II trials with multiple outcomes. Applications to a pediatric HIV trial and a cancer chemotherapy trial are given to illustrate the proposed approach. Simulation results for the two trials show that the computationally compromised strategy can perform well and appear to be ethical for allocating patients. The proposed framework can provide better approximation to the optimal strategy if more extensive computing is available.

An extension of the continual reassessment method using decision theory

The primary goal of a phase I trial is to find the maximally tolerated dose (MTD) of a treatment. The MTD is usually defined in terms of a tolerable probability, q*, of toxicity. Our objective is to find the highest dose with toxicity risk that does not exceed q*, a criterion that is often desired in designing phase I trials. This criterion differs from that of finding the dose with toxicity risk closest to q*, that is used in methods such as the continual reassessment method. We use the theory of decision processes to find optimal sequential designs that maximize the expected number of patients within the trial allocated to the highest dose with toxicity not exceeding q*, among the doses under consideration. The proposed method is very general in the sense that criteria other than the one considered here can be optimized and that optimal dose assignment can be defined in terms of patients within or outside the trial. It includes as an important special case the continual reassessment method. Numerical study indicates the strategy compares favourably with other phase I designs.