A physical based modeling approach for the dynamic behavior of ultrafine grained structures

This paper discusses some experimental results on the influence of grain refinement on the final mechanical properties of IF and microalloyed steels designed for auto-body components. It shows also some modeling approaches to understanding the dynamic behavior of fine-rained materials. The Zerilli–Armstrong (Z–A) and Khan–Huang–Liang (KHL) models for studied steels were implemented into FEM code in order to simulate the dynamic compression tests with different strain rates.

Understanding Chinese identity in international relations : a critique of Western approaches.

In international relations in the West, two main approaches to Chinese identity have emerged: the capability and the culture approaches. Though each takes a different view of China, they share common epistemological ground. positivism. This paper provides an overview of these two influential schools of thought and attempts to challenge their positivistic and ethnocentric assumptions about the identities of both China and the West. While they endeavour to make sense of China, particularly in the post-Cold War era, they fail to understand identity as a form of representation. From a critical perspective, both ’China’ and the ‘West’ are social constructs: each in part constitutes the other. The relationship between them is always relational and fluid. Posing Chinese identity in positivist terms is not only misleading analytically, but potentially dangerous in practice. It is important, therefore, that alternative critical approaches to the complexities of Chinese identity be further explored.

Allocating risks in public-private partnership projects using a transaction cost economics approach - a case study

Public-private partnership (PPP) projects are often characterised by increased complexity and uncertainty due to their idiosyncrasy in the management and delivery processes such as long-term lifecycle, incomplete contracting, and the multitude of stakeholders. An appropriate risk allocation is particularly crucial to achieving project success. This paper focuses on the risk allocation in PPP projects and argues that the transaction cost economics (TCE) theory can integrate the economics part, which is currently missing, into the risk management research. A TCE-based approach is proposed as a logical framework for allocating risks between public and private sectors in PPP projects. A case study of the Southern Cross Station redevelopment project in Australia is presented to illustrate the approach. The allocation of important risks is put under scrutiny. Lessons learnt are discussed and alternative management approaches drawing on TCE theory are proposed.

Differential reporting policy in a changing financial environment

To contend with the globalisation of capital markets the Financial Reporting Council (FRC) in Australia has embarked on a convergence program with International Financial Reporting Standards (IFRS). The convergence program is a significant departure from present financial reporting policy and will necessitate substantial change by reporting entities. The effectiveness of the existing differential reporting policy is drawn into question in the light of the changes taking place. An evaluation of the perceptions of the effectiveness of the extant differential reporting model is undertaken and alternative policy approaches considered. The findings indicate that certain aspects of the differential reporting model have had inherent problems not necessarily related to the recent policy change and that corrective action needs to be undertaken to maintain its relevance.

Financial reporting policy in a dynamic environment

To cope with the increasing globalisation of capital markets, financial regulators in Australia have embarked on an ambitious program to converge national accounting standards with International Financial Reporting Standards. The convergence program means a significant departure from present financial reporting policy and will necessitate substantial change by reporting entities. The effectiveness of the existing differential reporting policy is drawn into question in the light of the changes taking place. An evaluation of the perceptions of the effectiveness of the extant differential reporting model is undertaken and alternative policy approaches considered. The findings indicate that certain aspects of the differential reporting model have inherent problems not necessarily related to the recent policy change and that corrective action needs to be undertaken to maintain its relevance.

Are we predicting the actual or apparent distribution of temperate marine fishes?

Planning for resilience is the focus of many marine conservation programs and initiatives. These efforts aim to inform conservation strategies for marine regions to ensure they have inbuilt capacity to retain biological diversity and ecological function in the face of global environmental change – particularly changes in climate and resource exploitation. In the absence of direct biological and ecological information for many marine species, scientists are increasingly using spatially-explicit, predictive-modeling approaches. Through the improved access to multibeam sonar and underwater video technology these models provide spatial predictions of the most suitable regions for an organism at resolutions previously not possible. However, sensible-looking, well-performing models can provide very different predictions of distribution depending on which occurrence dataset is used. To examine this, we construct species distribution models for nine temperate marine sedentary fishes for a 25.7 km2 study region off the coast of southeastern Australia. We use generalized linear model (GLM), generalized additive model (GAM) and maximum entropy (MAXENT) to build models based on co-located occurrence datasets derived from two underwater video methods (i.e. baited and towed video) and fine-scale multibeam sonar based seafloor habitat variables. Overall, this study found that the choice of modeling approach did not considerably influence the prediction of distributions based on the same occurrence dataset. However, greater dissimilarity between model predictions was observed across the nine fish taxa when the two occurrence datasets were compared (relative to models based on the same dataset). Based on these results it is difficult to draw any general trends in regards to which video method provides more reliable occurrence datasets. Nonetheless, we suggest predictions reflecting the species apparent distribution (i.e. a combination of species distribution and the probability of detecting it). Consequently, we also encourage researchers and marine managers to carefully interpret model predictions.

Preparing pre-service teachers for classroom practice in a virtual world: A pilot study using Second Life

Many pre-service teachers feel under-prepared to teach students with a diverse range of needs and abilities and continue to be concerned about classroom behaviour management when undertaking practicum experiences. In order to address these concerns, teacher educators have explored alternative pedagogical approaches, including computer based simulations and immersion in virtual worlds. This paper reports on the results of a pilot study conducted with eight pre-service teachers who operated avatars in a virtual classroom created within Second Life (SL)™. The pre-service teachers were able to role-play students with a diverse range of behaviours and engage in reflective discussion about their experiences. The results showed that the pre-service teachers appreciated the opportunity to engage in an authentic classroom experience without impacting on "real" students, but that the platform of SL proved limiting in enacting certain aspects of desired teaching pedagogy. The findings of this pilot study are discussed in relation to improving the preparation of pre-service teachers for practicum.

Bayesian nonparametric extraction of hidden contexts from pervasive honest signals

Hidden patterns and contexts play an important part in intelligent pervasive systems. Most of the existing works have focused on simple forms of contexts derived directly from raw signals. High-level constructs and patterns have been largely neglected or remained under-explored in pervasive computing, mainly due to the growing complexity over time and the lack of efficient principal methods to extract them. Traditional parametric modeling approaches from machine learning find it difficult to discover new, unseen patterns and contexts arising from continuous growth of data streams due to its practice of training-then-prediction paradigm. In this work, we propose to apply Bayesian nonparametric models as a systematic and rigorous paradigm to continuously learn hidden patterns and contexts from raw social signals to provide basic building blocks for context-aware applications. Bayesian nonparametric models allow the model complexity to grow with data, fitting naturally to several problems encountered in pervasive computing. Under this framework, we use nonparametric prior distributions to model the data generative process, which helps towards learning the number of latent patterns automatically, adapting to changes in data and discovering never-seen-before patterns, contexts and activities. The proposed methods are agnostic to data types, however our work shall demonstrate to two types of signals: accelerometer activity data and Bluetooth proximal data. © 2014 IEEE.

Approximation of centroid end-points and switch points for replacing type reduction algorithms

Despite several years of research, type reduction (TR) operation in interval type-2 fuzzy logic system (IT2FLS) cannot perform as fast as a type-1 defuzzifier. In particular, widely used Karnik-Mendel (KM) TR algorithm is computationally much more demanding than alternative TR approaches. In this work, a data driven framework is proposed to quickly, yet accurately, estimate the output of the KM TR algorithm using simple regression models. Comprehensive simulation performed in this study shows that the centroid end-points of KM algorithm can be approximated with a mean absolute percentage error as low as 0.4%. Also, switch point prediction accuracy can be as high as 100%. In conjunction with the fact that simple regression model can be trained with data generated using exhaustive defuzzification method, this work shows the potential of proposed method to provide highly accurate, yet extremely fast, TR approximation method. Speed of the proposed method should theoretically outperform all available TR methods while keeping the uncertainty information intact in the process.

Machine learning approaches for modeling spammer behavior

Spam is commonly known as unsolicited or unwanted email messages in the Internet causing potential threat to Internet Security. Users spend a valuable amount of time deleting spam emails. More importantly, ever increasing spam emails occupy server storage space and consume network bandwidth. Keyword-based spam email filtering strategies will eventually be less successful to model spammer behavior as the spammer constantly changes their tricks to circumvent these filters. The evasive tactics that the spammer uses are patterns and these patterns can be modeled to combat spam. This paper investigates the possibilities of modeling spammer behavioral patterns by well-known classification algorithms such as Naïve Bayesian classifier (Naive Bayes), Decision Tree Induction (DTI) and Support Vector Machines (SVMs). Preliminary experimental results demonstrate a promising detection rate of around 92%, which is considerably an enhancement of performance compared to similar spammer behavior modeling research.

Beyond statutory enforcement - alternative approaches to improving OSH in the temporary agency sector

As with other types of flexible employment, a growing body of international research has found an association between temporary agency work and comparatively poor occupational safety and health outcomes. Temporary agency work has also been found to pose a serious challenge to regulatory regimes, including the activities of inspectors. Government agencies have responded to these challenges in a number of ways. This study examines a project undertaken in the Australian state of Queensland that sought to identify both particular problems and ways of resolving them. The focus of the project was to identify ‘non-regulatory’ solutions based on information collected through focus groups of agency and host employer representatives. However, while a number of policy interventions were identified that fitted this approach, the project also found that both agency firms and hosts believed additional regulatory controls were required. This paper assesses these findings in the context of broader research and policy debates about how to deal with the occupational safety and health problems posed by the global shift to more flexible work arrangements.

Interval-based and fuzzy set-based approaches to modeling of fuzzy inference systems with the local monotonicity property

Even though the importance of the local monotonicity property for function approximation problems is well established, there are relative few investigations addressing issues related to the fulfillment of the local monotonicity property in Fuzzy Inference System (FIS) modeling. We have previously conducted a preliminary study on the local monotonicity property of FIS models, with the assumption that the extrema point(s) (i.e., the maximum and/or minimum point(s)) is either known precisely or totally unknown. However, in some practical situations, the extrema point(s) can be known imprecisely (as an interval or a fuzzy set). In this paper, the imprecise information is exploited to construct an FIS model that fulfills the local monotonicity property. A procedure to estimate the extrema point(s) of a function is devised. Applicability of the findings to a datadriven modeling problem is further demonstrated.

Exploring alternative approaches

This chapter describes a number of alternative approaches to teaching and learning science in secondary schools. The approaches have been trialled in a number of places, but no one approach has been sufficient to capture the needs of the students’ learning or the development of scientific literacy.