Preparing pre-service physical education teachers for uncertain future(s) : a scenario-based learning case study from Australia

Background: A key aim of a physical education teacher education (PETE) program is to promote wide and deep knowledge, enabling students to establish connections and understand contexts within and beyond education, physical education and their life worlds. Most often PETE programs equip students with content knowledge and pedagogical strategies that help them address current challenges, but less attention is directed to helping the students anticipate future challenges and engage with opportunities they may face as teachers.
Purpose: This paper presents a case study of scenario-based learning as it was implemented in a final year PETE program in an Australian university, as a means of preparing students for their future teaching careers.
Participants and setting: Twenty-five final year pre-service physical education teachers enrolled in the culminating unit of their physical education degree.
Data collection: Scenario-based learning was introduced to the students via class discussion and assigned tasks. Examples of student-written scenarios and reflection on the experience from the lecturer and student perspectives are analysed.
Findings: Although the cohort found the process of scenario-based learning daunting the post-unit questionnaires revealed that it was a valued and valuable means of exploring professional issues they will face in the future. Scenario-based learning was a powerful tool of learning as well as modelling a pedagogy students could use in the upper levels of secondary school.
Conclusion: This paper argues that scenario-based learning should be a key component of forward-looking PETE programs that encourage their graduates to solve problems about issues they may face as beginning teachers.

Robust finite-horizon Kalman filtering for uncertain discrete-time systems

In this note, we propose a design for a robust finite-horizon Kalman filtering for discrete-time systems suffering from uncertainties in the modeling parameters and uncertainties in the observations process (missing measurements). The system parameter uncertainties are expected in the state, output and white noise covariance matrices. We find the upper-bound on the estimation error covariance and we minimize the proposed upper-bound.

Adaptive fast finite-time multiple-surface sliding control for a class of uncertain non-linear systems

This paper concerns the adaptive fast finite-time multiple-surface sliding control (AFFTMSSC) problem for a class of high-order uncertain non-linear systems of which the upper bounds of the system uncertainties are unknown. By using the fast control Lyapunov function and the method of so-called adding a power integrator merging with adaptive technique, a recursive design procedure is provided, which guarantees the fast finite-time stability of the closed-loop system. Further, it is proved that the control input is bounded.

Security in uncertain times: policies for increasing the popularity of life annuities among retirees

Life annuities offer retirees an assured income stream for as long as they live. This makes it surprising that they are unpopular in most markets where their purchase is not compelled by government policy. With the numbers of retirees in the population set to increase dramatically, this low take-up rate of life annuities could exacerbate financial insecurity. Consequently, it is in society’s interest to implement non-coercive policies that increase annuitization levels. Although there is research that has focused on the possible causes of low annuitization rates, much of this research falls short of suggesting comprehensive strategies for persuading retirees to annuitize their savings.

Improved robust Kalman filtering for uncertain systems with missing measurements

In this paper, a novel robust finite-horizon Kalman filter is developed for discrete linear time-varying systems with missing measurements and normbounded parameter uncertainties. The missing measurements are modelled by a Bernoulli distributed sequence and the system parameter uncertainties are in the state and output matrices. A two stage recursive structure is considered for the Kalman filter and its parameters are determined guaranteeing that the covariances of the state estimation errorsare not more than the known upper bound. Finally, simulation results are presented to illustrate the outperformance of the proposed robust estimator compared with the previous results in the literature.

CIRCE: correcting imprecise readings and compressing excrescent points for querying common patterns in uncertain sensor streams

Continuous sensor stream data are often recorded as a series of discrete points in a database from which knowledge can be retrieved through queries. Two classes of uncertainties inevitably happen in sensor streams that we present as follows. The first is Uncertainty due to Discrete Sampling (DS Uncertainty); even if every discrete point is correct, the discrete sensor stream is uncertain – that is, it is not exactly like the continuous stream – since some critical points are missing due to the limited capabilities of the sensing equipment and the database server. The second is Uncertainty due to Sampling Error (SE Uncertainty); sensor readings for the same situation cannot be repeated exactly when we record them at different times or use different sensors since different sampling errors exist. These two uncertainties reduce the efficiency and accuracy of querying common patterns. However, already known algorithms generally only resolve SE Uncertainty. In this paper, we propose a novel method of Correcting Imprecise Readings and Compressing Excrescent (CIRCE) points. Particularly, to resolve DS Uncertainty, a novel CIRCE core algorithm is developed in the CIRCE method to correct the missing critical points while compressing the original sensor streams. The experimental study based on various sizes of sensor stream datasets validates that the CIRCE core algorithm is more efficient and more accurate than a counterpart algorithm to compress sensor streams. We also resolve the SE Uncertainty problem in the CIRCE method. The application for querying longest common route patterns validates the effectiveness of our CIRCE method.

Stability analysis of two-dimensional Markovian jump state-delayed systems in the Roesser model with uncertain transition probabilities

This paper is concerned with the problem of stochastic stability analysis of discrete-time two-dimensional (2-D) Markovian jump systems (MJSs) described by the Roesser model with interval time-varying delays. The transition probabilities of the jumping process/Markov chain are assumed to be uncertain, that is, they are not exactly known but can be estimated. A Lyapunov-like scheme is first extended to 2-D MJSs with delays. Based on some novel 2-D summation inequalities proposed in this paper, delay-dependent stochastic stability conditions are derived in terms of linear matrix inequalities (LMIs) which can be computationally solved by various convex optimization algorithms. Finally, two numerical examples are given to illustrate the effectiveness of the obtained results.