160 resultados para 329.269


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With the increase in complexity of engineering projects and design quality in the construction industry, the traditional two-dimensional "Information Island" approach to design is becoming less able to meet current design needs due to its lack of coordination and information sharing. Collaborative design using a Build Information Modeling (BIM) technology platform promises to provide an effective means of designing and communicating through networking and real-time data sharing. This paper first analyzes the shortcomings of the two-dimensional design process and the potential application of collaborative design. By combining the attributes of BIM, a preliminary BIM-based building design collaborative platform is developed to improve the design approach and support a more collaborative design process. A real-life case is presented to demonstrate the feasibility and validity of the platform and its use in practice. From this, it is shown that BIM has the potential to realize effective information sharing and reduce errors, thereby improving design quality. The BIM-based building design collaborative platform presented is expected to provide the support needed for the extensive application of BIM in collaborative design and promote a new attitude to project management.

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This paper finds evidence for the growing importance of informal interactions between the internal audit function and the audit committee (AC) in Australia – a relatively unexplored topic in the literature – using a survey of Chief Audit Executives (CAEs). It also describes the nature of these informal interactions. The most innovative elements of this paper are the findings that certain personal characteristics of CAEs, the specific knowledge and expertise of the AC chair, as well as some of the AC chair’s personal characteristics are associated with the existence (and increase) of informal interactions.

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This paper proposes a new multi-resource multi-stage scheduling problem for optimising the open-pit drilling, blasting and excavating operations under equipment capacity constraints. The flow process is analysed based on the real-life data from an Australian iron ore mine site. The objective of the model is to maximise the throughput and minimise the total idle times of equipment at each stage. The following comprehensive mining attributes and constraints have been considered: types of equipment; operating capacities of equipment; ready times of equipment; speeds of equipment; block-sequence-dependent movement times of equipment; equipment-assignment-dependent operation times of blocks; distances between each pair of blocks; due windows of blocks; material properties of blocks; swell factors of blocks; and slope requirements of blocks. It is formulated by mixed integer programming and solved by ILOG-CPLEX optimiser. The proposed model is validated with extensive computational experiments to improve mine production efficiency at the operational level. The model also provides an intelligent decision support tool to account for the availability and usage of equipment units for drilling, blasting and excavating stages.

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E. coli does chemotaxis by performing a biased random walk composed of alternating periods of swimming (runs) and reorientations (tumbles). Tumbles are typically modelled as complete directional randomisations but it is known that in wild type E. coli, successive run directions are actually weakly correlated, with a mean directional difference of ∼63°. We recently presented a model of the evolution of chemotactic swimming strategies in bacteria which is able to quantitatively reproduce the emergence of this correlation. The agreement between model and experiments suggests that directional persistence may serve some function, a hypothesis supported by the results of an earlier model. Here we investigate the effect of persistence on chemotactic efficiency, using a spatial Monte Carlo model of bacterial swimming in a gradient, combined with simulations of natural selection based on chemotactic efficiency. A direct search of the parameter space reveals two attractant gradient regimes, (a) a low-gradient regime, in which efficiency is unaffected by directional persistence and (b) a high-gradient regime, in which persistence can improve chemotactic efficiency. The value of the persistence parameter that maximises this effect corresponds very closely with the value observed experimentally. This result is matched by independent simulations of the evolution of directional memory in a population of model bacteria, which also predict the emergence of persistence in high-gradient conditions. The relationship between optimality and persistence in different environments may reflect a universal property of random-walk foraging algorithms, which must strike a compromise between two competing aims: exploration and exploitation. We also present a new graphical way to generally illustrate the evolution of a particular trait in a population, in terms of variations in an evolvable parameter.

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Universal application of evidence-based practice (EBP) is far from a reality with many clinicians feeling ill equipped to adopt this approach in their clinical practice (Melnyk Fineout- Overholt, Feinstein, Sadler, & Green-Hernandez, 2008; Sherriff, Wallis, & Chaboyer, 2007) and, thus, to be an intelligent consumer of evidence (Ciliska, 2005). While recognizing the benefit of EBP, many health professionals have low confidence in their skills for using evidence in clinical settings (Nagy, Lumby, McKinley, &Macfarlane, 2001). Educational initiatives are often recommended for promoting adoption of EBP with much of the focus being on providing knowledge of associated processes. Levin, Melnyk, Fineout-Overholt, Barnes, and Vetter (2011) demonstrated that providing knowledge of EBP process alone does not increase clinicians’ confidence in their ability to apply EBP to their practice...

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Due to their unobtrusive nature, vision-based approaches to tracking sports players have been preferred over wearable sensors as they do not require the players to be instrumented for each match. Unfortunately however, due to the heavy occlusion between players, variation in resolution and pose, in addition to fluctuating illumination conditions, tracking players continuously is still an unsolved vision problem. For tasks like clustering and retrieval, having noisy data (i.e. missing and false player detections) is problematic as it generates discontinuities in the input data stream. One method of circumventing this issue is to use an occupancy map, where the field is discretised into a series of zones and a count of player detections in each zone is obtained. A series of frames can then be concatenated to represent a set-play or example of team behaviour. A problem with this approach though is that the compressibility is low (i.e. the variability in the feature space is incredibly high). In this paper, we propose the use of a bilinear spatiotemporal basis model using a role representation to clean-up the noisy detections which operates in a low-dimensional space. To evaluate our approach, we used a fully instrumented field-hockey pitch with 8 fixed high-definition (HD) cameras and evaluated our approach on approximately 200,000 frames of data from a state-of-the-art real-time player detector and compare it to manually labeled data.

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Long-term systematic population monitoring data sets are rare but are essential in identifying changes in species abundance. In contrast, community groups and natural history organizations have collected many species lists. These represent a large, untapped source of information on changes in abundance but are generally considered of little value. The major problem with using species lists to detect population changes is that the amount of effort used to obtain the list is often uncontrolled and usually unknown. It has been suggested that using the number of species on the list, the "list length," can be a measure of effort. This paper significantly extends the utility of Franklin's approach using Bayesian logistic regression. We demonstrate the value of List Length Analysis to model changes in species prevalence (i.e., the proportion of lists on which the species occurs) using bird lists collected by a local bird club over 40 years around Brisbane, southeast Queensland, Australia. We estimate the magnitude and certainty of change for 269 bird species and calculate the probabilities that there have been declines and increases of given magnitudes. List Length Analysis confirmed suspected species declines and increases. This method is an important complement to systematically designed intensive monitoring schemes and provides a means of utilizing data that may otherwise be deemed useless. The results of List Length Analysis can be used for targeting species of conservation concern for listing purposes or for more intensive monitoring. While Bayesian methods are not essential for List Length Analysis, they can offer more flexibility in interrogating the data and are able to provide a range of parameters that are easy to interpret and can facilitate conservation listing and prioritization. © 2010 by the Ecological Society of America.

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It is traditional to initialise Kalman filters and extended Kalman filters with estimates of the states calculated directly from the observed (raw) noisy inputs, but unfortunately their performance is extremely sensitive to state initialisation accuracy: good initial state estimates ensure fast convergence whereas poor estimates may give rise to slow convergence or even filter divergence. Divergence is generally due to excessive observation noise and leads to error magnitudes that quickly become unbounded (R.J. Fitzgerald, 1971). When a filter diverges, it must be re initialised but because the observations are extremely poor, re initialised states will have poor estimates. The paper proposes that if neurofuzzy estimators produce more accurate state estimates than those calculated from the observed noisy inputs (using the known state model), then neurofuzzy estimates can be used to initialise the states of Kalman and extended Kalman filters. Filters whose states have been initialised with neurofuzzy estimates should give improved performance by way of faster convergence when the filter is initialised, and when a filter is re started after divergence

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The mineral ushkovite has been analyzed using a combination of electron microscopy with EDX and vibrational spectroscopy. Chemical analysis shows the mineral contains P, Mg with very minor Fe. Thus, the formula of the studied ushkovite is Mg32+(PO4)2·8H2O. The Raman spectrum shows an intense band at 953 cm−1 assigned to the ν1 symmetric stretching mode. In the infrared spectra complexity exists with multiple antisymmetric stretching vibrations observed, due to the reduced tetrahedral symmetry. This loss of degeneracy is also reflected in the bending modes. Strong infrared bands around 827 cm−1 are attributed to water librational modes. The Raman spectra of the hydroxyl-stretching region are complex with overlapping broad bands. Hydroxyl stretching vibrations are identified at 2881, 2998, 3107, 3203, 3284 and 3457 cm−1. The wavenumber band at 3457 cm−1 is attributed to the presence of FeOH groups. This complexity is reflected in the water HOH bending modes where a strong infrared band centered around 1653 cm−1 is found. Such a band reflects the strong hydrogen bonding of the water molecules to the phosphate anions in adjacent layers. Spectra show three distinct OH bending bands from strongly hydrogen-bonded, weakly hydrogen bonded water and non-hydrogen bonded water. Vibrational spectroscopy enhances our knowledge of the molecular structure of ushkovite.

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Large volumes of heterogeneous health data silos pose a big challenge when exploring for information to allow for evidence based decision making and ensuring quality outcomes. In this paper, we present a proof of concept for adopting data warehousing technology to aggregate and analyse disparate health data in order to understand the impact various lifestyle factors on obesity. We present a practical model for data warehousing with detailed explanation which can be adopted similarly for studying various other health issues.

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This paper explores how threshold uncertainty affects cooperative behaviors in the provision of public goods and the prevention of public bads. The following facts motivate our study. First, environmental (resource) problems are either framed as public bads prevention or public goods provision. Second, the occurrence of these problems is characterized by thresholds that are interchangeably represented as "nonconvexity," "bifurcation," "bi-stability," or "catastrophes." Third, the threshold location is mostly unknown. We employ a provision point mechanism with threshold uncertainty and analyze the responses of cooperative behaviors to uncertainty and to the framing for each type of social preferences categorized by a value orientation test. We find that aggregate framing effects are negligible, although the response to the frame is the opposite depending on the type of social preferences. "Cooperative" subjects become more cooperative in negative frames than in positive frames, whereas "individualistic" subjects are less cooperative in negative frames than in positive ones. This finding implies that the insignificance of aggregate framing effects arises from behavioral asymmetry. We also find that the percentage of cooperative choices non-monotonically varies with the degree of threshold uncertainty, irrespective of framing and value orientation. Specifically, the degree of cooperation is highest at intermediate levels of threshold uncertainty and decreases as the uncertainty becomes sufficiently large.

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Effectively capturing opportunities requires rapid decision-making. We investigate the speed of opportunity evaluation decisions by focusing on firms' venture termination and venture advancement decisions. Experience, standard operating procedures, and confidence allow firms to make opportunity evaluation decisions faster; we propose that a firm's attentional orientation, as reflected in its project portfolio, limits the number of domains in which these speed-enhancing mechanisms can be developed. Hence firms' decision speed is likely to vary between different types of decisions. Using unique data on 3,269 mineral exploration ventures in the Australian mining industry, we find that firms with a higher degree of attention toward earlier-stage exploration activities are quicker to abandon potential opportunities in early development but slower to do so later, and that such firms are also slower to advance on potential opportunities at all stages compared to firms that focus their attention differently. Market dynamism moderates these relationships, but only with regard to initial evaluation decisions. Our study extends research on decision speed by showing that firms are not necessarily fast or slow regarding all the decisions they make, and by offering an opportunity evaluation framework that recognizes that decision makers can, in fact often do, pursue multiple potential opportunities simultaneously.