Adaptive behaviour in Australia : what items are essential for assessing independent living?

Adaptive behaviour is important in the assessment of eligibility for intellectual disability services. However, there is some question about which behaviours should be assessed. The purpose of the present study was to clarify which everyday behaviours are considered essential for independent functioning by young adults in the Australian community. Parents, disability workers, and young adults judged the importance of 130 everyday behaviours. Items that assessed safety, health, self-care, functional literacy and numeracy, respecting others' rights, and day-to-day decisionmaking were most frequently rated as essential for independent functioning. Our findings raise important questions about the assessment of adaptive behaviour in Australia, and point to the need for a more valid approach to the measurement of adaptive behaviour for the purpose of eligibility assessment. The research provides a first step towards providing such a scale for use in the Australian context.

Applying the adaptive model of comfort

This note is directed to one major aspect of the comfort of building occupants – namely, thermal comfort. Even though it may be difficult to isolate thermal sensations from the whole of comfort itself, humans have a strong physiological connection with their thermal environment. Our thermal perceptions and sensations often vary greatly, especially between our indoor and outdoor environments. We may be totally comfortable lounging under a shade cloth on a 35°C day with a stiff breeze enveloping our body, but would never tolerate similar conditions indoors. Such divergent perceptions of the same thermal stimulus across differing contexts raise countless questions about just what the determinants of thermal comfort actually are, and how they may be managed against the demands for an environmentally responsive architecture.

An adaptive information grid architecture for recommendation system

This paper presents an adaptive information grid architecture for recommendation systems, which consists of the features of the recommendation rule and a co-citation algorithm. The algorithm addresses some challenges that are essential for further searching and recommendation algorithms. It does not require users to provide a lot of interactive communication. Furthermore, it supports other queries, such as keyword, URL and document investigations. When the structure is compared to other algorithms, the scalability is noticeably better. The high online performance can be obtained as well as the repository computation, which can achieve a high group-forming accuracy using only a fraction of web pages from a cluster.

Supporting adaptive learning with high level timed petri nets

Supporting adaptive learning is one of the key problems for hypertext-based learning applications. This paper proposed a timed Petri Net based approach that provides adaptation to learning activities by controlling the visualization of hypertext information nodes. Simple examples were given while explaining ways to realize adaptive operations. Future directions were also discussed at the end of this paper.

Committing to quality learning through adaptive online assessment

With advances in computer-based technologies and the emergence of e-learning, there are unprecedented opportunities to reconsider assessment of learning (and, axiomatically, of teaching) and how this can be undertaken. One approach is adaptive assessment. Although it has existed in the tertiary environment since the time of the oral examination, advanced technologies allow much fuller exploitation of the possibilities inherent in a dynamic system of testing that responds to the user. Having described the characteristics of adaptive assessment, this paper considers how it can achieve significant pedagogical aims within the sector. The paper differentiates between adaptive assessment to assist learning and adaptive assessment to assess achievement. How adaptive assessment can be put in place and salient issues, such as security and system integrity, when such assessment is used for credit, are then discussed. The paper concludes that the capability exists but it has yet to be exploited within higher education as a viable approach to assessment and as a contributor to quality learning.

Improving the performance of the LMS and RLS algorithms for adaptive equalizer

In this paper, we present the experiment results of three adaptive equalization algorithms: least-mean-square (LMS) algorithm, discrete cosine transform-least mean square (DCT-LMS) algorithm, and recursive least square (RLS) algorithm. Based on the experiments, we obtained that the convergence rate of LMS is slow; the convergence rate of RLS is great faster while the computational price is expensive; the performance of that two parameters of DCT-LMS are between the previous two algorithms, but still not good enough. Therefore we will propose an algorithm based on H2 in a coming paper to solve the problems.

Adaptive blind source separation using constant modulus criterion and signal mutual information

We address the problem of adaptive blind source separation (BSS) from instantaneous multi-input multi-output (MIMO) channels. It is known that the constant modulus (CM) criterion can be used to extract unknown source signals. However, the existing CM based algorithms normally extract the source signals in a serial manner. Consequently, the accuracy in extracting each source signal, except for the first one, depends on the accuracy of previous source extraction. This estimation error propagation (accumulation) causes severe performance degradation. In this paper, we propose a new adaptive separation algorithm that can separate all source signals simultaneously by directly updating the separation matrix. The superior performance of the new algorithm is demonstrated by simulation examples

Adaptive blind channel equalization based on constellation information of MPSK signal

In this paper, we propose a new adaptive algorithm for the blind equalization of an FIR (finite impulse response) channel excited by an M-ary phase shift keying (MPSK) signal. Different from the conventional constant modulus algorithm (CMA), which exploits the amplitude information of the input signal, the proposed algorithm exploits the full constellation information of the input signal. Theoretical analysis shows that the new algorithm has less mean square error (MSE), namely better equalization performance, in steady state than the CMA. Numerical simulations show the effectiveness of the new algorithm

Supporting adaptive learning in hypertext environment : a high level timed Petri net based approach

One problem for hypertext-based learning application is to control learning paths for different learning activities. This paper first introduced related concepts of hypertext learning state space and Petri net, then proposed a high level timed Petri Net based approach to provide some kinds of adaptation for learning activities. Examples were given while explaining ways to realizing adaptive instructions. Possible future directions were also discussed at the end of this paper.

Supporting adaptive learning in hypertext environment : an agent based approach

One problem for hypertext-based learning application is to control learning paths for different learning activities. This paper first introduces related concepts of hypertext learning state space, then proposes an agent based approach used to provide kinds of adaptation for learning activities. Examples are given while explaining ways to realizing adaptive instructions. Possible future directions are also discussed at the end of this paper.