Passive solar systems for heating, daylighting and ventilation for rooms without an equator-facing facade

Spaces without northerly orientations have an impact on the ‘energy behaviour’ of a building. This paper outlines possible energy savings and better performance achieved by different zenithal solar passive strategies (skylights, roof monitors and clerestory roof windows) and element arrangements across the roof in zones of cold to temperate climates typical of the central and central-southern Argentina. Analyses were undertaken considering daylighting, thermal and ventilation performances of the different strategies. The results indicate that heating,ventilation and lighting loads in spaces without an equator-facing facade can be significantly reduced by implementing solar passive strategies. In the thermal aspect, the solar saving fraction reached for the different strategies were averaged 43.16% for clerestories, 41.4% for roof monitors and 38.86% for skylights for a glass area of 9% to the floor area. The results also indicate average illuminance levels above 500 lux for the different clerestory and monitor arrangements, uniformity ratios of 0.66–0.82 for the most distributed arrangements and day-lighting factors between 11.78 and 20.30% for clear sky conditions, depending on the strategy. In addition, minimum air changes rates of 4 were reached for the most extreme conditions.

Designing to maintain human agency in context-aware systems

This paper describes a series of design games, specifically aimed at exploring shifts in human agency, how they are managed, and the impact this will have on the design of future context-aware applications. The games focussed on understanding information handling issues in dental practice with participants from the University of Queensland Dental School playing an active role in the activities. Participatory design activities reveal how technology solution impact on dental practices. By finding methods of representing technological possibilities in ways which can easily be understood we enhance the contribution that dentists can make to the design process.

Interpersonal pattern dynamics and adaptive behavior in multiagent neurobiological systems : conceptual model and data

Ecological dynamics characterizes adaptive behavior as an emergent, self-organizing property of interpersonal interactions in complex social systems. The authors conceptualize and investigate constraints on dynamics of decisions and actions in the multiagent system of team sports. They studied coadaptive interpersonal dynamics in rugby union to model potential control parameter and collective variable relations in attacker–defender dyads. A videogrammetry analysis revealed how some agents generated fluctuations by adapting displacement velocity to create phase transitions and destabilize dyadic subsystems near the try line. Agent interpersonal dynamics exhibited characteristics of chaotic attractors and informational constraints of rugby union boxed dyadic systems into a low dimensional attractor. Data suggests that decisions and actions of agents in sports teams may be characterized as emergent, self-organizing properties, governed by laws of dynamical systems at the ecological scale. Further research needs to generalize this conceptual model of adaptive behavior in performance to other multiagent populations.

Adaptive and phase transition behavior in performance of discrete multi-articular actions by degenerate neurobiological systems

The identification of attractors is one of the key tasks in studies of neurobiological coordination from a dynamical systems perspective, with a considerable body of literature resulting from this task. However, with regards to typical movement models investigated, the overwhelming majority of actions studied previously belong to the class of continuous, rhythmical movements. In contrast, very few studies have investigated coordination of discrete movements, particularly multi-articular discrete movements. In the present study, we investigated phase transition behavior in a basketball throwing task where participants were instructed to shoot at the basket from different distances. Adopting the ubiquitous scaling paradigm, throwing distance was manipulated as a candidate control parameter. Using a cluster analysis approach, clear phase transitions between different movement patterns were observed in performance of only two of eight participants. The remaining participants used a single movement pattern and varied it according to throwing distance, thereby exhibiting hysteresis effects. Results suggested that, in movement models involving many biomechanical degrees of freedom in degenerate systems, greater movement variation across individuals is available for exploitation. This observation stands in contrast to movement variation typically observed in studies using more constrained bi-manual movement models. This degenerate system behavior provides new insights and poses fresh challenges to the dynamical systems theoretical approach, requiring further research beyond conventional movement models.

Information enrichment for quality recommender systems

The explosive growth of the World-Wide-Web and the emergence of ecommerce are the major two factors that have led to the development of recommender systems (Resnick and Varian, 1997). The main task of recommender systems is to learn from users and recommend items (e.g. information, products or books) that match the users’ personal preferences. Recommender systems have been an active research area for more than a decade. Many different techniques and systems with distinct strengths have been developed to generate better quality recommendations. One of the main factors that affect recommenders’ recommendation quality is the amount of information resources that are available to the recommenders. The main feature of the recommender systems is their ability to make personalised recommendations for different individuals. However, for many ecommerce sites, it is difficult for them to obtain sufficient knowledge about their users. Hence, the recommendations they provided to their users are often poor and not personalised. This information insufficiency problem is commonly referred to as the cold-start problem. Most existing research on recommender systems focus on developing techniques to better utilise the available information resources to achieve better recommendation quality. However, while the amount of available data and information remains insufficient, these techniques can only provide limited improvements to the overall recommendation quality. In this thesis, a novel and intuitive approach towards improving recommendation quality and alleviating the cold-start problem is attempted. This approach is enriching the information resources. It can be easily observed that when there is sufficient information and knowledge base to support recommendation making, even the simplest recommender systems can outperform the sophisticated ones with limited information resources. Two possible strategies are suggested in this thesis to achieve the proposed information enrichment for recommenders: • The first strategy suggests that information resources can be enriched by considering other information or data facets. Specifically, a taxonomy-based recommender, Hybrid Taxonomy Recommender (HTR), is presented in this thesis. HTR exploits the relationship between users’ taxonomic preferences and item preferences from the combination of the widely available product taxonomic information and the existing user rating data, and it then utilises this taxonomic preference to item preference relation to generate high quality recommendations. • The second strategy suggests that information resources can be enriched simply by obtaining information resources from other parties. In this thesis, a distributed recommender framework, Ecommerce-oriented Distributed Recommender System (EDRS), is proposed. The proposed EDRS allows multiple recommenders from different parties (i.e. organisations or ecommerce sites) to share recommendations and information resources with each other in order to improve their recommendation quality. Based on the results obtained from the experiments conducted in this thesis, the proposed systems and techniques have achieved great improvement in both making quality recommendations and alleviating the cold-start problem.

A Numerical Solution Using an Adaptively Preconditioned Lanczos Method for a Class of Linear Systems Related with the Fractional Poisson Equation

This study considers the solution of a class of linear systems related with the fractional Poisson equation (FPE) (−∇2)α/2φ=g(x,y) with nonhomogeneous boundary conditions on a bounded domain. A numerical approximation to FPE is derived using a matrix representation of the Laplacian to generate a linear system of equations with its matrix A raised to the fractional power α/2. The solution of the linear system then requires the action of the matrix function f(A)=A−α/2 on a vector b. For large, sparse, and symmetric positive definite matrices, the Lanczos approximation generates f(A)b≈β0Vmf(Tm)e1. This method works well when both the analytic grade of A with respect to b and the residual for the linear system are sufficiently small. Memory constraints often require restarting the Lanczos decomposition; however this is not straightforward in the context of matrix function approximation. In this paper, we use the idea of thick-restart and adaptive preconditioning for solving linear systems to improve convergence of the Lanczos approximation. We give an error bound for the new method and illustrate its role in solving FPE. Numerical results are provided to gauge the performance of the proposed method relative to exact analytic solutions.

A distributed knowledge management infrastructure based on a Topic Map grid

Modern enterprise knowledge management systems typically require distributed approaches and the integration of numerous heterogeneous sources of information. A powerful foundation for these tasks can be Topic Maps, which not only provide a semantic net-like knowledge representation means and the possibility to use ontologies for modelling knowledge structures, but also offer concepts to link these knowledge structures with unstructured data stored in files, external documents etc. In this paper, we present the architecture and prototypical implementation of a Topic Map application infrastructure, the ‘Topic Grid’, which enables transparent, node-spanning access to different Topic Maps distributed in a network.

Statistical analysis and reduction of multiple access interference in MC-CDMA systems

Multicarrier code division multiple access (MC-CDMA) is a very promising candidate for the multiple access scheme in fourth generation wireless communi- cation systems. During asynchronous transmission, multiple access interference (MAI) is a major challenge for MC-CDMA systems and significantly affects their performance. The main objectives of this thesis are to analyze the MAI in asyn- chronous MC-CDMA, and to develop robust techniques to reduce the MAI effect. Focus is first on the statistical analysis of MAI in asynchronous MC-CDMA. A new statistical model of MAI is developed. In the new model, the derivation of MAI can be applied to different distributions of timing offset, and the MAI power is modelled as a Gamma distributed random variable. By applying the new statistical model of MAI, a new computer simulation model is proposed. This model is based on the modelling of a multiuser system as a single user system followed by an additive noise component representing the MAI, which enables the new simulation model to significantly reduce the computation load during computer simulations. MAI reduction using slow frequency hopping (SFH) technique is the topic of the second part of the thesis. Two subsystems are considered. The first sub- system involves subcarrier frequency hopping as a group, which is referred to as GSFH/MC-CDMA. In the second subsystem, the condition of group hopping is dropped, resulting in a more general system, namely individual subcarrier frequency hopping MC-CDMA (ISFH/MC-CDMA). This research found that with the introduction of SFH, both of GSFH/MC-CDMA and ISFH/MC-CDMA sys- tems generate less MAI power than the basic MC-CDMA system during asyn- chronous transmission. Because of this, both SFH systems are shown to outper- form MC-CDMA in terms of BER. This improvement, however, is at the expense of spectral widening. In the third part of this thesis, base station polarization diversity, as another MAI reduction technique, is introduced to asynchronous MC-CDMA. The com- bined system is referred to as Pol/MC-CDMA. In this part a new optimum com- bining technique namely maximal signal-to-MAI ratio combining (MSMAIRC) is proposed to combine the signals in two base station antennas. With the applica- tion of MSMAIRC and in the absents of additive white Gaussian noise (AWGN), the resulting signal-to-MAI ratio (SMAIR) is not only maximized but also in- dependent of cross polarization discrimination (XPD) and antenna angle. In the case when AWGN is present, the performance of MSMAIRC is still affected by the XPD and antenna angle, but to a much lesser degree than the traditional maximal ratio combining (MRC). Furthermore, this research found that the BER performance for Pol/MC-CDMA can be further improved by changing the angle between the two receiving antennas. Hence the optimum antenna angles for both MSMAIRC and MRC are derived and their effects on the BER performance are compared. With the derived optimum antenna angle, the Pol/MC-CDMA system is able to obtain the lowest BER for a given XPD.