11 resultados para TSV-DM
em Queensland University of Technology - ePrints Archive
Resumo:
Experience plays an important role in building management. “How often will this asset need repair?” or “How much time is this repair going to take?” are types of questions that project and facility managers face daily in planning activities. Failure or success in developing good schedules, budgets and other project management tasks depend on the project manager's ability to obtain reliable information to be able to answer these types of questions. Young practitioners tend to rely on information that is based on regional averages and provided by publishing companies. This is in contrast to experienced project managers who tend to rely heavily on personal experience. Another aspect of building management is that many practitioners are seeking to improve available scheduling algorithms, estimating spreadsheets and other project management tools. Such “micro-scale” levels of research are important in providing the required tools for the project manager's tasks. However, even with such tools, low quality input information will produce inaccurate schedules and budgets as output. Thus, it is also important to have a broad approach to research at a more “macro-scale.” Recent trends show that the Architectural, Engineering, Construction (AEC) industry is experiencing explosive growth in its capabilities to generate and collect data. There is a great deal of valuable knowledge that can be obtained from the appropriate use of this data and therefore the need has arisen to analyse this increasing amount of available data. Data Mining can be applied as a powerful tool to extract relevant and useful information from this sea of data. Knowledge Discovery in Databases (KDD) and Data Mining (DM) are tools that allow identification of valid, useful, and previously unknown patterns so large amounts of project data may be analysed. These technologies combine techniques from machine learning, artificial intelligence, pattern recognition, statistics, databases, and visualization to automatically extract concepts, interrelationships, and patterns of interest from large databases. The project involves the development of a prototype tool to support facility managers, building owners and designers. This final report presents the AIMMTM prototype system and documents how and what data mining techniques can be applied, the results of their application and the benefits gained from the system. The AIMMTM system is capable of searching for useful patterns of knowledge and correlations within the existing building maintenance data to support decision making about future maintenance operations. The application of the AIMMTM prototype system on building models and their maintenance data (supplied by industry partners) utilises various data mining algorithms and the maintenance data is analysed using interactive visual tools. The application of the AIMMTM prototype system to help in improving maintenance management and building life cycle includes: (i) data preparation and cleaning, (ii) integrating meaningful domain attributes, (iii) performing extensive data mining experiments in which visual analysis (using stacked histograms), classification and clustering techniques, associative rule mining algorithm such as “Apriori” and (iv) filtering and refining data mining results, including the potential implications of these results for improving maintenance management. Maintenance data of a variety of asset types were selected for demonstration with the aim of discovering meaningful patterns to assist facility managers in strategic planning and provide a knowledge base to help shape future requirements and design briefing. Utilising the prototype system developed here, positive and interesting results regarding patterns and structures of data have been obtained.
Resumo:
Experience plays an important role in building management. “How often will this asset need repair?” or “How much time is this repair going to take?” are types of questions that project and facility managers face daily in planning activities. Failure or success in developing good schedules, budgets and other project management tasks depend on the project manager's ability to obtain reliable information to be able to answer these types of questions. Young practitioners tend to rely on information that is based on regional averages and provided by publishing companies. This is in contrast to experienced project managers who tend to rely heavily on personal experience. Another aspect of building management is that many practitioners are seeking to improve available scheduling algorithms, estimating spreadsheets and other project management tools. Such “micro-scale” levels of research are important in providing the required tools for the project manager's tasks. However, even with such tools, low quality input information will produce inaccurate schedules and budgets as output. Thus, it is also important to have a broad approach to research at a more “macro-scale.” Recent trends show that the Architectural, Engineering, Construction (AEC) industry is experiencing explosive growth in its capabilities to generate and collect data. There is a great deal of valuable knowledge that can be obtained from the appropriate use of this data and therefore the need has arisen to analyse this increasing amount of available data. Data Mining can be applied as a powerful tool to extract relevant and useful information from this sea of data. Knowledge Discovery in Databases (KDD) and Data Mining (DM) are tools that allow identification of valid, useful, and previously unknown patterns so large amounts of project data may be analysed. These technologies combine techniques from machine learning, artificial intelligence, pattern recognition, statistics, databases, and visualization to automatically extract concepts, interrelationships, and patterns of interest from large databases. The project involves the development of a prototype tool to support facility managers, building owners and designers. This Industry focused report presents the AIMMTM prototype system and documents how and what data mining techniques can be applied, the results of their application and the benefits gained from the system. The AIMMTM system is capable of searching for useful patterns of knowledge and correlations within the existing building maintenance data to support decision making about future maintenance operations. The application of the AIMMTM prototype system on building models and their maintenance data (supplied by industry partners) utilises various data mining algorithms and the maintenance data is analysed using interactive visual tools. The application of the AIMMTM prototype system to help in improving maintenance management and building life cycle includes: (i) data preparation and cleaning, (ii) integrating meaningful domain attributes, (iii) performing extensive data mining experiments in which visual analysis (using stacked histograms), classification and clustering techniques, associative rule mining algorithm such as “Apriori” and (iv) filtering and refining data mining results, including the potential implications of these results for improving maintenance management. Maintenance data of a variety of asset types were selected for demonstration with the aim of discovering meaningful patterns to assist facility managers in strategic planning and provide a knowledge base to help shape future requirements and design briefing. Utilising the prototype system developed here, positive and interesting results regarding patterns and structures of data have been obtained.
Resumo:
Heart failure is a complex disorder, characterized by activation of the sympathetic nervous system, leading to dysregulated Ca2+ homeostasis in cardiac myocytes and tissue remodeling. In a variety of diseases, cardiac malfunction is associated with aberrant fluxes of Ca2+ across both the surface membrane and the internal Ca2+ store, the sarcoplasmic reticulum (SR). One prominent hypothesis residues is that in heart failure, the activity of the ryanodine receptor (RyR2) Ca2+ release channel in the SR is increased due to excess phosphorylation and that this contributes to excess SR Ca2+ leak in diastole, reduced SR Ca2+ load and decreased contractility (Huke & Bers, 2008). There is controversy over which serine residues in RyR2 are hyperphosphorylated in animal models of heart failure and whether this is via the CaMKII or the PKA-linked signaling pathway. S2808, S2814 and S2030 in RyR2 have been variously claimed to be hyperphosphorylated. Our aim was to examine the degree of phosphorylation of these residues in RyR2 from failing human hearts. The use of human tissue was approved by the Human Research Ethics Committee, The Prince Charles Hospital, EC28114. Left ventricular tissue samples were obtained from an explanted heart of a patient with endstage heart failure (Emery Dreifuss Muscular Dystrophy with cardiomyopathy) and non-failing tissue was from a patient with cystic fibrosis undergoing heart-lung transplantation with no history of heart disease. SR vesicles were prepared as described by Laver et al. (1995) and examined with SDS-Page and Western Blot. Transferred proteins were probed with antibodies to detect total protein phosphorylation, phosphorylation of RyR2 serine residues S2808, S2814, S2030 and for the key proteins calsequestrin, triadin, junctin and FKBP12.6. To avoid membrane stripping artifact, each membrane was exposed to one phosphorylation-specific antibody and signal densities quantified using Bio-Rad Quantity One software. We found no distinguishable difference between failing and healthy hearts in the protein expression levels of RyR2, triadin, junctin or calsequestrin. We found an expected upregulation of total RyR2 phosphorylation in the failing heart sample, compared to a matched amount of RyR2 (quantified using densiometry) in healthy heart. Probing with antibodies detecting only the phosphorylated form of the specific RyR2 residues showed that the increase in total RyR2 phosphorylation in the failing heart was due to hyperphosphorylation of S2808 and S2814. We found that S2030 phosphorylation levels were unchanged in human heart failure. Interestingly, we found that S2030 has a basal level of phosphorylation in the healthy human heart, different from the absence of basal phosphorylation recently reported in rodent heart (Huke & Bers, 2008). Finally, preliminary results indicate that less FKBP 12.6 is associated with RyR2 in the failing heart, possibly as a consequence of PKA activation. In conclusion, residues S2808 and S2814 are hyperphosphorylated in human heart failure, presumably due to upregulation of the CaMKII and/or PKA signaling pathway as a result of chronic activation of the sympathetic nervous system. Such changes in RyR2 phosphorylation are believed to contribute to the leaky RyR2 phenotype associated with heart failure, which increases the incidence of arrhythmia and contributes to the severely impaired contractile performance of the failing heart. Huke S & Bers DM. (2008). Ryanodine receptor phosphorylation at serine 2030, 2808 and 2814 in rat cardiomyocytes. Biochemical and Biophysical Research Communications 376, 80-85. Laver DR, Roden LD, Ahern GP, Eager KR, Junankar PR & Dulhunty AF. (1995). Cytoplasmic Ca2+ inhibits the ryanodine receptor from cardiac muscle. Journal of Membrane Biology 147, 7-22. Proceedings
Resumo:
This paper examines the linkages between diversity management (DM), innovation and high performance in social enterprises. These linkages are explicated beyond traditional framing of DM limited to workforce composition, to include discussions of innovation through networked diversity practices; reconciliation; and funding options. The paper draws upon a UK-based national survey and the case study data. Multiple data collection methods were used, including semi-structured interviews, questionnaires and workshops with participant observation. NVivo and SPSS software packages were utilized in order to analyse the qualitative and quantitative data, respectively. We used thematic coding and cropping techniques in analysing the case studies in the paper. A broad range of conflicting and supporting literature was enfolded into the conversations and discussion. The paper demonstrates that social enterprises exhibit unique characteristics in terms of size and location, as well as their double remit to add value both economically and socially. As a conclusion, we argue for social enterprises to consider options for DM in the interests of maximization of innovation and business performance. We contend that further research is needed to describe how social entrepreneurs draw upon their various ‘diversity resources’ in the process of innovation
Resumo:
The focus of the present research was to investigate how Local Governments in Queensland were progressing with the adoption of delineated DM policies and supporting guidelines. The study consulted Local Government representatives and hence, the results reflect their views on these issues. Is adoption occurring? To what degree? Are policies and guidelines being effectively implemented so that the objective of a safer, more resilient community is being achieved? If not, what are the current barriers to achieving this, and can recommendations be made to overcome these barriers? These questions defined the basis on which the present study was designed and the survey tools developed. While it was recognised that LGAQ and Emergency Management Queensland (EMQ) may have differing views on some reported issues, it was beyond the scope of the present study to canvass those views. The study resolved to document and analyse these questions under the broad themes of: • Building community capacity (notably via community awareness). • Council operationalisation of DM. • Regional partnerships (in mitigation/adaptation). Data was collected via a survey tool comprising two components: • An online questionnaire survey distributed via the LGAQ Disaster Management Alliance (hereafter referred to as the “Alliance”) to DM sections of all Queensland Local Government Councils; and • a series of focus groups with selected Queensland Councils
Resumo:
This thesis investigates profiling and differentiating customers through the use of statistical data mining techniques. The business application of our work centres on examining individuals’ seldomly studied yet critical consumption behaviour over an extensive time period within the context of the wireless telecommunication industry; consumption behaviour (as oppose to purchasing behaviour) is behaviour that has been performed so frequently that it become habitual and involves minimal intentions or decision making. Key variables investigated are the activity initialised timestamp and cell tower location as well as the activity type and usage quantity (e.g., voice call with duration in seconds); and the research focuses are on customers’ spatial and temporal usage behaviour. The main methodological emphasis is on the development of clustering models based on Gaussian mixture models (GMMs) which are fitted with the use of the recently developed variational Bayesian (VB) method. VB is an efficient deterministic alternative to the popular but computationally demandingMarkov chainMonte Carlo (MCMC) methods. The standard VBGMMalgorithm is extended by allowing component splitting such that it is robust to initial parameter choices and can automatically and efficiently determine the number of components. The new algorithm we propose allows more effective modelling of individuals’ highly heterogeneous and spiky spatial usage behaviour, or more generally human mobility patterns; the term spiky describes data patterns with large areas of low probability mixed with small areas of high probability. Customers are then characterised and segmented based on the fitted GMM which corresponds to how each of them uses the products/services spatially in their daily lives; this is essentially their likely lifestyle and occupational traits. Other significant research contributions include fitting GMMs using VB to circular data i.e., the temporal usage behaviour, and developing clustering algorithms suitable for high dimensional data based on the use of VB-GMM.
Resumo:
Recent research has described the restructuring of particles upon exposure to organic vapours; however, as yet hypotheses able to explain this phenomenon are limited. In this study, a range of experiments were performed to explore different hypotheses related to carbonaceous particle restructuring upon exposure to organic and water vapours, such as: the effect of surface tension, the role of organics in flocculating primary particles, as well as the ability of vapours to “wet” the particle surface. The change in mobility diameter (dm) was investigated for a range carbonaceous particle types (diesel exhaust, petrol exhaust, cigarette smoke, candle smoke, particles generated in a heptane/toluene flame, and wood smoke particles) exposed to different organic (heptane, ethanol, and dimethyl sulfoxide/water (1:1 vol%) mixture) and water vapours. Particles were first size-selected and then bubbled through an impinger (bubbler) containing either an organic solvent or water, where particles trapped inside rising bubbles were exposed to saturated vapours of the solvent in the impinger. The size distribution of particles was simultaneously measured upstream and downstream from the impinger. A size-dependent reduction in dm was observed when bubbling diesel exhaust, particles generated in a heptane/toluene flame, and candle smoke particles through heptane, ethanol and a dimethyl sulfoxide/water (1:1 vol %) mixture. In addition, the size distributions of particles bubbled through an impinger were broader. Moreover, an increase of the geometric standard deviation (σ) of the size distributions of particles bubbled through an impinger was also found to be size-dependent. Size-dependent reduction in dm and an increase of σ indicate that particles undergo restructuring to a more compact form, which was confirmed by TEM analysis. However, bubbling of these particles through water did not result in a size-dependent reduction in dm, nor in an increase of σ. Cigarette smoke, petrol exhaust, and wood smoke particles did not result in any substantial change in dm, or σ, when bubbled through organic solvents or water. Therefore, size-dependent reduction in the dm upon bubbling through organic solvents was observed only for particles that had a fractal-like structure, whilst particles that were liquid or were assumed to be spherical did not exhibit any reduction in dm. Compaction of fractal-like particles was attributed to the ability of condensing vapours to efficiently wet the particles. Our results also show that the presence of an organic layer on the surface of fractal-like particles, or the surface tension of the condensed liquid do not influence the extent of compaction.
Resumo:
Background--Pulmonary diffusing capacity for carbon monoxide (Dlco), alveolar capillary membrane diffusing capacity (Dm), and pulmonary capillary blood volume (Vc) are all significantly reduced after exercise. Objective--To investigate whether measurement position affects this impaired gas transfer. Methods--Before and one, two, and four hours after incremental cycle ergometer exercise to fatigue, single breath Dlco, Dm, and Vc measurements were obtained in 10 healthy men in a randomly assigned supine and upright seated position. Results--After exercise, Dlco, Dm, and Vc were significantly depressed compared with baseline in both positions. The supine position produced significantly higher values over time for Dlco (5.22 (0.13) v 4.66 (0.15) ml/min/mm Hg/l, p = 0.022) and Dm (6.78 (0.19) v 6.03 (0.19) ml/min/mm Hg/l, p = 0.016), but there was no significant position effect for Vc. There was a similar pattern of change over time for Dlco, Dm, and Vc in the two positions. Conclusions--The change in Dlco after exercise appears to be primarily due to a decrease in Vc. Although the mechanism for the reduction in Vc cannot be determined from these data, passive relocation of blood to the periphery as the result of gravity can be discounted, suggesting that active vasoconstriction of the pulmonary vasculature and/or peripheral vasodilatation is occurring after exercise.
Resumo:
In this article, we study the security of the IDEA block cipher when it is used in various simple-length or double-length hashing modes. Even though this cipher is still considered as secure, we show that one should avoid its use as internal primitive for block cipher based hashing. In particular, we are able to generate instantaneously free-start collisions for most modes, and even semi-free-start collisions, pseudo-preimages or hash collisions in practical complexity. This work shows a practical example of the gap that exists between secret-key and known or chosen-key security for block ciphers. Moreover, we also settle the 20-year-old standing open question concerning the security of the Abreast-DM and Tandem-DM double-length compression functions, originally invented to be instantiated with IDEA. Our attacks have been verified experimentally and work even for strengthened versions of IDEA with any number of rounds.
Resumo:
We sought to evaluate central corneal thickness (CCT), corneal endothelial cell density (ECD) and intraocular pressure (IOP) in patients with type 2 diabetes mellitus (DM) and to associate potential differences with diabetes duration and treatment modality in a prospective, randomized study. We measured ECD, CCT and IOP of 125 patients with type 2 DM (mean age 57.1¡11.5 years) and compared them with 90 age-matched controls. Measured parameters were analyzed for association with diabetes duration and glucose control modalities (insulin injection or oral medication) while controlling for age. In the diabetic group, the mean ECD (2511¡252 cells/mm2), mean CCT (539.7¡33.6 mm) and mean IOP (18.3¡2.5 mmHg) varied significantly from those the control group [ECD: 2713¡132 cells/mm2 (P,0.0001), CCT: 525.0¡45.3 mm (P50.003) and IOP: 16.7¡1.8 mmHg (P,0.0001)]. ECD was significantly reduced by about 32 cell/mm2 for diabetics with duration of .10 years when compared with those with duration of ,10 years (P,0.05). CCT was thicker and IOP was higher for diabetics with duration of .10 years than those with duration of ,10 years (P.0.05). None of the measured parameters was significantly associated with diabetes duration and treatment modality (P.0.05). In conclusion, subjects with type 2 DM exhibit significant changes in ECD, IOP and CCT, which, however, are not correlated with disease duration or if the patients receive on insulin injection or oral medications.
Resumo:
Background Spatial analysis is increasingly important for identifying modifiable geographic risk factors for disease. However, spatial health data from surveys are often incomplete, ranging from missing data for only a few variables, to missing data for many variables. For spatial analyses of health outcomes, selection of an appropriate imputation method is critical in order to produce the most accurate inferences. Methods We present a cross-validation approach to select between three imputation methods for health survey data with correlated lifestyle covariates, using as a case study, type II diabetes mellitus (DM II) risk across 71 Queensland Local Government Areas (LGAs). We compare the accuracy of mean imputation to imputation using multivariate normal and conditional autoregressive prior distributions. Results Choice of imputation method depends upon the application and is not necessarily the most complex method. Mean imputation was selected as the most accurate method in this application. Conclusions Selecting an appropriate imputation method for health survey data, after accounting for spatial correlation and correlation between covariates, allows more complete analysis of geographic risk factors for disease with more confidence in the results to inform public policy decision-making.