Theoretical comparison of innovative window daylighting devices for a sub-tropical climate using radiance

Daylighting in tropical and sub-tropical climates presents a unique challenge that is generally not well understood by designers. In a sub-tropical region such as Brisbane, Australia the majority of the year comprises of sunny clear skies with few overcast days and as a consequence windows can easily become sources of overheating and glare. The main strategy in dealing with this issue is extensive shading on windows. However, this in turn prevents daylight penetration into buildings often causing an interior to appear gloomy and dark even though there is more than sufficient daylight available. As a result electric lighting is the main source of light, even during the day. Innovative daylight devices which redirect light from windows offer a potential solution to this issue. These devices can potentially improve daylighting in buildings by increasing the illumination within the environment decreasing the high contrast between the window and work regions and deflecting potentially glare causing sunlight away from the observer. However, the performance of such innovative daylighting devices are generally quantified under overcast skies (i.e. daylight factors) or skies without sun, which are typical of European climates and are misleading when considering these devices for tropical or sub-tropical climates. This study sought to compare four innovative window daylighting devices in RADIANCE; light shelves, laser cut panels, micro-light guides and light redirecting blinds. These devices were simulated in RADIANCE under sub-tropical skies (for Brisbane) within the test case of a typical CBD office space. For each device the quantity of light redirected and its distribution within the space was used as the basis for comparison. In addition, glare analysis on each device was conducted using Weinold and Christoffersons evalglare. The analysis was conducted for selected hours for a day in each season. The majority of buildings that humans will occupy in their lifetime are already constructed, and extensive remodelling of most of these buildings is unlikely. Therefore the most effective way to improve daylighting in the near future will be through the alteration existing window spaces. Thus it will be important to understand the performance of daylighting systems with respect to the climate it is to be used in. This type of analysis is important to determine the applicability of a daylighting strategy so that designers can achieve energy efficiency as well the health benefits of natural daylight.

Hierarchical Bayesian models for estimating the extent of plant pest invasions

Plant biosecurity requires statistical tools to interpret field surveillance data in order to manage pest incursions that threaten crop production and trade. Ultimately, management decisions need to be based on the probability that an area is infested or free of a pest. Current informal approaches to delimiting pest extent rely upon expert ecological interpretation of presence / absence data over space and time. Hierarchical Bayesian models provide a cohesive statistical framework that can formally integrate the available information on both pest ecology and data. The overarching method involves constructing an observation model for the surveillance data, conditional on the hidden extent of the pest and uncertain detection sensitivity. The extent of the pest is then modelled as a dynamic invasion process that includes uncertainty in ecological parameters. Modelling approaches to assimilate this information are explored through case studies on spiralling whitefly, Aleurodicus dispersus and red banded mango caterpillar, Deanolis sublimbalis. Markov chain Monte Carlo simulation is used to estimate the probable extent of pests, given the observation and process model conditioned by surveillance data. Statistical methods, based on time-to-event models, are developed to apply hierarchical Bayesian models to early detection programs and to demonstrate area freedom from pests. The value of early detection surveillance programs is demonstrated through an application to interpret surveillance data for exotic plant pests with uncertain spread rates. The model suggests that typical early detection programs provide a moderate reduction in the probability of an area being infested but a dramatic reduction in the expected area of incursions at a given time. Estimates of spiralling whitefly extent are examined at local, district and state-wide scales. The local model estimates the rate of natural spread and the influence of host architecture, host suitability and inspector efficiency. These parameter estimates can support the development of robust surveillance programs. Hierarchical Bayesian models for the human-mediated spread of spiralling whitefly are developed for the colonisation of discrete cells connected by a modified gravity model. By estimating dispersal parameters, the model can be used to predict the extent of the pest over time. An extended model predicts the climate restricted distribution of the pest in Queensland. These novel human-mediated movement models are well suited to demonstrating area freedom at coarse spatio-temporal scales. At finer scales, and in the presence of ecological complexity, exploratory models are developed to investigate the capacity for surveillance information to estimate the extent of red banded mango caterpillar. It is apparent that excessive uncertainty about observation and ecological parameters can impose limits on inference at the scales required for effective management of response programs. The thesis contributes novel statistical approaches to estimating the extent of pests and develops applications to assist decision-making across a range of plant biosecurity surveillance activities. Hierarchical Bayesian modelling is demonstrated as both a useful analytical tool for estimating pest extent and a natural investigative paradigm for developing and focussing biosecurity programs.

Grounding action in visuo-haptic space using experience networks

Traditional approaches to the use of machine learning algorithms do not provide a method to learn multiple tasks in one-shot on an embodied robot. It is proposed that grounding actions within the sensory space leads to the development of action-state relationships which can be re-used despite a change in task. A novel approach called an Experience Network is developed and assessed on a real-world robot required to perform three separate tasks. After grounded representations were developed in the initial task, only minimal further learning was required to perform the second and third task.

Digital dawn : a revolution in movie distribution?

How will the digital technology revolution impact the movie business? Hollywood developed a highly successful industrial system that has functioned well for almost a century in the sense that it enabled the Major film studios to largely control and dominate the industry. However, the new digital technology may now be propelling Hollywood toward the biggest technological transition since the creation of the studio system almost a century ago. For example, Major Hollywood studios are already beginning to provide video-on-demand (VOD) digital distribution of movies over the Internet. This article examines what is happening, and why. It sets out the background and the incipient changes already occurring. It makes an argument regarding the fundamental strategic dynamics, that acetate film was the key to the control of the Hollywood system, and speculates about how a shift away from acetate film to digital video may transform that system. The focus is on the impact on how the Major studios release and market their movies, and how new market and marketing opportunities for the low-budget independent filmmaking sector may arise.

Osteoclastic activity begins early and increases over the course of bone healing

Osteoclasts are specialised bone-resorbing cells. This particular ability makes osteoclasts irreplaceable for the continual physiological process of bone remodelling as well as for the repair process during bone healing. Whereas the effects of systemic diseases on osteoclasts have been described by many authors, the spatial and temporal distribution of osteoclasts during bone healing seems to be unclear so far. In the present study, healing of a tibial osteotomy under standardised external fixation was examined after 2, 3, 6 and 9 weeks (n = 8) in sheep. The osteoclastic number was counted, the area of mineralised bone tissue was measured histomorphometrically and density of osteoclasts per square millimetre mineralised tissue was calculated. The osteoclastic density in the endosteal region increased, whereas the density in the periosteal region remained relatively constant. The density of osteoclasts within the cortical bone increased slightly over the first 6 weeks, however, there was a more rapid increase between the sixth and ninth weeks. The findings of this study imply that remodelling and resorption take place already in the very early phase of bone healing. The most frequent remodelling process can be found in the periosteal callus, emphasising its role as the main stabiliser. The endosteal space undergoes resorption in order to recanalise the medullary cavity, a process also started in the very early phase of healing at a low level and increasing significantly during healing. The cortical bone adapts in its outward appearance to the surrounding callus structure. This paradoxic loosening is caused by the continually increasing number and density of osteoclasts in the cortical bone ends. This study clearly emphasises the osteoclastic role especially during early bone healing. These cells do not simply resorb bone but participate in a fine adjusted system with the bone-producing osteoblasts in order to maintain and improve the structural strength of bone tissue.

Modulation of depth-dependent properties in tissue-engineered cartilage with a semi-permeable membrane and perfusion : a continuum model of matrix metabolism and transport

The functional properties of cartilaginous tissues are determined predominantly by the content, distribution, and organization of proteoglycan and collagen in the extracellular matrix. Extracellular matrix accumulates in tissue-engineered cartilage constructs by metabolism and transport of matrix molecules, processes that are modulated by physical and chemical factors. Constructs incubated under free-swelling conditions with freely permeable or highly permeable membranes exhibit symmetric surface regions of soft tissue. The variation in tissue properties with depth from the surfaces suggests the hypothesis that the transport processes mediated by the boundary conditions govern the distribution of proteoglycan in such constructs. A continuum model (DiMicco and Sah in Transport Porus Med 50:57-73, 2003) was extended to test the effects of membrane permeability and perfusion on proteoglycan accumulation in tissue-engineered cartilage. The concentrations of soluble, bound, and degraded proteoglycan were analyzed as functions of time, space, and non-dimensional parameters for several experimental configurations. The results of the model suggest that the boundary condition at the membrane surface and the rate of perfusion, described by non-dimensional parameters, are important determinants of the pattern of proteoglycan accumulation. With perfusion, the proteoglycan profile is skewed, and decreases or increases in magnitude depending on the level of flow-based stimulation. Utilization of a semi-permeable membrane with or without unidirectional flow may lead to tissues with depth-increasing proteoglycan content, resembling native articular cartilage.

Rural activity spaces and transport disadvantage : qualitative analysis of quantitative models integrating time and space

Current knowledge about the relationship between transport disadvantage and activity space size is limited to urban areas, and as a result, very little is known to date about this link in a rural context. In addition, although research has identified transport disadvantaged groups based on their size of activity spaces, these studies have, however, not empirically explained such differences and the result is often a poor identification of the problems facing disadvantaged groups. Research has shown that transport disadvantage varies over time. The static nature of analysis using the activity space concept in previous research studies has lacked the ability to identify transport disadvantage in time. Activity space is a dynamic concept; and therefore possesses a great potential in capturing temporal variations in behaviour and access opportunities. This research derives measures of the size and fullness of activity spaces for 157 individuals for weekdays, weekends, and for a week using weekly activity-travel diary data from three case study areas located in rural Northern Ireland. Four focus groups were also conducted in order to triangulate the quantitative findings and to explain the differences between different socio-spatial groups. The findings of this research show that despite having a smaller sized activity space, individuals were not disadvantaged because they were able to access their required activities locally. Car-ownership was found to be an important life line in rural areas. Temporal disaggregation of the data reveals that this is true only on weekends due to a lack of public transport services. In addition, despite activity spaces being at a similar size, the fullness of activity spaces of low-income individuals was found to be significantly lower compared to their high-income counterparts. Focus group data shows that financial constraint, poor connections both between public transport services and between transport routes and opportunities forced individuals to participate in activities located along the main transport corridors.

Redland Youth Space development: evaluation report

This report provides an evaluation of the Capalaba Youth Space.The evaluation included elements of process and impact evaluation and used a participatory action research approach informed by engagement processes, focus groups, a community survey, interviews and secondary analysis of existing data.

Asset life prediction and maintenance decision-making using a non-linear non-Gaussian state space model

Estimating and predicting degradation processes of engineering assets is crucial for reducing the cost and insuring the productivity of enterprises. Assisted by modern condition monitoring (CM) technologies, most asset degradation processes can be revealed by various degradation indicators extracted from CM data. Maintenance strategies developed using these degradation indicators (i.e. condition-based maintenance) are more cost-effective, because unnecessary maintenance activities are avoided when an asset is still in a decent health state. A practical difficulty in condition-based maintenance (CBM) is that degradation indicators extracted from CM data can only partially reveal asset health states in most situations. Underestimating this uncertainty in relationships between degradation indicators and health states can cause excessive false alarms or failures without pre-alarms. The state space model provides an efficient approach to describe a degradation process using these indicators that can only partially reveal health states. However, existing state space models that describe asset degradation processes largely depend on assumptions such as, discrete time, discrete state, linearity, and Gaussianity. The discrete time assumption requires that failures and inspections only happen at fixed intervals. The discrete state assumption entails discretising continuous degradation indicators, which requires expert knowledge and often introduces additional errors. The linear and Gaussian assumptions are not consistent with nonlinear and irreversible degradation processes in most engineering assets. This research proposes a Gamma-based state space model that does not have discrete time, discrete state, linear and Gaussian assumptions to model partially observable degradation processes. Monte Carlo-based algorithms are developed to estimate model parameters and asset remaining useful lives. In addition, this research also develops a continuous state partially observable semi-Markov decision process (POSMDP) to model a degradation process that follows the Gamma-based state space model and is under various maintenance strategies. Optimal maintenance strategies are obtained by solving the POSMDP. Simulation studies through the MATLAB are performed; case studies using the data from an accelerated life test of a gearbox and a liquefied natural gas industry are also conducted. The results show that the proposed Monte Carlo-based EM algorithm can estimate model parameters accurately. The results also show that the proposed Gamma-based state space model have better fitness result than linear and Gaussian state space models when used to process monotonically increasing degradation data in the accelerated life test of a gear box. Furthermore, both simulation studies and case studies show that the prediction algorithm based on the Gamma-based state space model can identify the mean value and confidence interval of asset remaining useful lives accurately. In addition, the simulation study shows that the proposed maintenance strategy optimisation method based on the POSMDP is more flexible than that assumes a predetermined strategy structure and uses the renewal theory. Moreover, the simulation study also shows that the proposed maintenance optimisation method can obtain more cost-effective strategies than a recently published maintenance strategy optimisation method by optimising the next maintenance activity and the waiting time till the next maintenance activity simultaneously.

XML documents clustering using Tensor Space Model

The traditional Vector Space Model (VSM) is not able to represent both the structure and the content of XML documents. This paper introduces a novel method of representing XML documents in a Tensor Space Model (TSM) and then utilizing it for clustering. Empirical analysis shows that the proposed method is scalable for large-sized datasets; as well, the factorized matrices produced from the proposed method help to improve the quality of clusters through the enriched document representation of both structure and content information.

A grouping model for distributed pipeline assets maintenance decision

Distributed pipeline assets systems are crucial to society. The deterioration of these assets and the optimal allocation of limited budget for their maintenance correspond to crucial challenges for water utility managers. Decision makers should be assisted with optimal solutions to select the best maintenance plan concerning available resources and management strategies. Much research effort has been dedicated to the development of optimal strategies for maintenance of water pipes. Most of the maintenance strategies are intended for scheduling individual water pipe. Consideration of optimal group scheduling replacement jobs for groups of pipes or other linear assets has so far not received much attention in literature. It is a common practice that replacement planners select two or three pipes manually with ambiguous criteria to group into one replacement job. This is obviously not the best solution for job grouping and may not be cost effective, especially when total cost can be up to multiple million dollars. In this paper, an optimal group scheduling scheme with three decision criteria for distributed pipeline assets maintenance decision is proposed. A Maintenance Grouping Optimization (MGO) model with multiple criteria is developed. An immediate challenge of such modeling is to deal with scalability of vast combinatorial solution space. To address this issue, a modified genetic algorithm is developed together with a Judgment Matrix. This Judgment Matrix is corresponding to various combinations of pipe replacement schedules. An industrial case study based on a section of a real water distribution network was conducted to test the new model. The results of the case study show that new schedule generated a significant cost reduction compared with the schedule without grouping pipes.

Body-site distribution of skin cancer, pre-malignant and common benign pigmented lesions excised in general practice

Background Concern about skin cancer is a common reason for people from predominantly fair-skinned populations to present to primary care doctors. Objectives To examine the frequency and body-site distribution of malignant, pre-malignant and benign pigmented skin lesions excised in primary care. Methods This prospective study conducted in Queensland, Australia, included 154 primary care doctors. For all excised or biopsied lesions, doctors recorded the patient's age and sex, body site, level of patient pressure to excise, and the clinical diagnosis. Histological confirmation was obtained through pathology laboratories. Results Of 9650 skin lesions, 57·7% were excised in males and 75·0% excised in patients ≥50years. The most common diagnoses were basal cell carcinoma (BCC) (35·1%) and squamous cell carcinoma (SCC) (19·7%). Compared with the whole body, the highest densities for SCC, BCC and actinic keratoses were observed on chronically sun-exposed areas of the body including the face in males and females, the scalp and ears in males, and the hands in females. The density of BCC was also high on intermittently or rarely exposed body sites. Females, younger patients and patients with melanocytic naevi were significantly more likely to exert moderate/high levels of pressure on the doctor to excise. Conclusions More than half the excised lesions were skin cancer, which mostly occurred on the more chronically sun-exposed areas of the body. Information on the type and body-site distribution of skin lesions can aid in the diagnosis and planned management of skin cancer and other skin lesions commonly presented in primary care.

Crystal growth from undercooling melts under strong gradients of temperatures generating in short-duration microgravity

Crystal growth of bulk CdTe in short-duration microgravity is performed by the unidirectional cooling method. The largest growth grains in microgravity samples are 4X2mm. The cooling profiles indicate undercooling melts in microgravity. Cooling melt samples in microgravity generate strong gradient of temperature due to stop thermal convections. Temperature distribution in the melt is calculated by the one-dimensional equation of heat conduction, and about 100 K-undercooling is considered to occur at the cooling surface.