Board behaviour and the global financial crisis : time for a holistic approach to governance

The heightened focus on corporate governance in the aftermath of the financial crisis and in particular the failure of boards to protect their corporations indicates the timeliness of this paper. Although corporate governance has been traditionally linked to control and compliance, the complexities of the 21st Century have focused attention on the need for more holistic approaches. This paper picks up these developments and using interpretive research, analyses thirteen in-depth interviews conducted with board members and senior management, before and after the crisis. The longitudinal data provides valuable insight into the role of boards, their behaviour, culture and decision-making structures.

Students use a 'just-in-time' approach to accessing online resources

Lecture notes and recordings, tutorial exercises, readings, study questions, revision quizzes, instructional videos and crosswords were provided online so that students who could not attend classes (flexible learners) were not disadvantaged. D2L's progress viewing tool was used to track student access to resources, class attendance was monitored, and a questionnaire asked about the value of the online resources for completing the unit. While students thought it was important to attend classes, attendance was low and centred on tutorials and practical's, rather than lectures. The main reason for non-attendance was employment. Students accessed most of the online resources immediately prior to tests rather than on a week-by-week basis. Both attenders and flexible learners indicated that lecture notes, readings, revision quizzes and study questions were the most useful resources. Attenders accessed online resources more than flexible learners. There was only a weak correlation between class attendance and successful completion of the unit.

Approach to fault detection of time-delay systems using functional observers

A new methodology is reported for designing functional observers to detect actuator faults of a class of time-delay systems where the matrix pair (A, C) is not observable. First, a generalised state transformation is used to transform the system into new coordinates where the delay term associated with the state vector is injected into the system's output and input. Then, a minimum-order functional observer is designed to construct a residual function that can trigger system faults. The finding is significant as it is now possible to detect faults of time-delay systems where the pair (A, C) is not required to be observable. A numerical example is given to illustrate the effectiveness of the proposed design approach. © The Institution of Engineering and Technology 2014.

Design of state observers for interconnected time-delay systems via a coordinate transformation approach

This paper considers the design of state observers for interconnected time-delay systems using a coordinate transformation method. Through such a transformation, the system that has interconnection and state delays is metamorphosed into a new system that injects time-delay information into its input and output terms, before reintroducing them back into the latter system, effectively coupling the delay terms into the IO injection terms and eliminating the delay values from the state variables. Next, full-order and reduced-order observers are designed based on the transformed system. Finally, the observed states of the transformed system that correspond to the original system is used to deduce the estimates of the original system. A numerical example is provided of an interconnected time-delay system.

Examining the effect of time constraint on the online mastery learning approach towards improving postgraduate students' achievement

Time control plays a critical role within the online mastery learning (OML) approach. This paper examines the two commonly implemented mastery learning strategies – personalised system of instructions and learning for mastery (LFM) – by focusing on what occurs when there is an instructional time constraint. Using a large data set from a postgraduate finance course offered at an Australian university, we explore students' online quiz-completion patterns, then empirically investigate whether the imposition of an instructional time constraint in the OML approach has an impact on their final-examination performance. Our results suggest that the LFM strategy with an instructional time constraint has a positive impact on students' learning behaviour and contributes to better overall academic performance. Further, our findings suggest that facilitators should be encouraged to implement an instructional time constraint when adopting an OML approach.

Dynamical analysis of neural networks with time-varying delays using the LMI approach

This study is concerned with the delay-range-dependent stability analysis for neural networks with time-varying delay and Markovian jumping parameters. The time-varying delay is assumed to lie in an interval of lower and upper bounds. The Markovian jumping parameters are introduced in delayed neural networks, which are modeled in a continuous-time along with finite-state Markov chain. Moreover, the sufficient condition is derived in terms of linear matrix inequalities based on appropriate Lyapunov-Krasovskii functionals and stochastic stability theory, which guarantees the globally asymptotic stable condition in the mean square. Finally, a numerical example is provided to validate the effectiveness of the proposed conditions.

A panel data approach to economic forecasting: the bias-corrected average forecast

In this paper, we propose a novel approach to econometric forecasting of stationary and ergodic time series within a panel-data framework. Our key element is to employ the (feasible) bias-corrected average forecast. Using panel-data sequential asymptotics we show that it is potentially superior to other techniques in several contexts. In particular, it is asymptotically equivalent to the conditional expectation, i.e., has an optimal limiting mean-squared error. We also develop a zeromean test for the average bias and discuss the forecast-combination puzzle in small and large samples. Monte-Carlo simulations are conducted to evaluate the performance of the feasible bias-corrected average forecast in finite samples. An empirical exercise based upon data from a well known survey is also presented. Overall, theoretical and empirical results show promise for the feasible bias-corrected average forecast.