Automatic dimensional reduction and meshing of stiffened thin-wall structures

The creation of idealised, dimensionally reduced meshes for preliminary design and optimisation remains a time-consuming, manual task. A dimensionally reduced model is ideal for assessing design changes through modification of element properties without the need to create a new geometry or mesh. In this paper, a novel approach for automating the creation of mixed dimensional meshes is presented. The input to the process is a solid model which has been decomposed into a non-manifold assembly of smaller volumes with different meshing significance. Associativity between the original solid model and the dimensionally reduced equivalent is maintained. The approach is validated by means of a free-free modal analysis on an output mesh of a gas turbine engine component of industrial complexity. Extensions and enhancements to this work are also discussed.     

Structural study of hexane-soluble products from hydroliquefaction of three Yallourn brown coal lithotypes over molten salt catalysts

The hexane-soluble fractions of hydroliquefied products from three Yallourn brown coal lithotypes have been separated into five fractions by combined silica-alumina packed column chromatography. Analyses of various fractions by g.c.-m.s. permitted the identification of ≈50 components in the saturate fraction and 40 components in the diaromatic fraction, together with 30 components in the monoaromatics. The components identified were quite similar among hexane-soluble portions of all three lithotypes. A marked predominance of even carbon number alkane (C₂₃-C₂₉) was observed in the hydrocarbon fractions from pale lithotype over $\text{ZnCl}{}_2\text{KCl}$, and medium light lithotype over both pure ZnCl₂ and $\text{ZnCl}{}_2\text{KCl}$. However, medium dark lithotype over both melt catalysts produced a saturate fraction with an odd carbon number(C₂₂-C₂₈) preference. Based on spectral methods, Soxhlet extracts obtained from untreated lithotypes (hexane and benzene solubles) were characterized as complex mixtures of higher molecular weight(300–1000) aliphatic hydrocarbons, which were supposed to be a precursor of the saturates produced from the corresponding lithotype in the catalytic hydroliquefaction.     

Mössbauer analysis of iron phases in brown coal ash and fireside deposits

The iron phases present in an electrostatic precipitator ash, an uncooled ash deposit and a cooled superheater ash deposit from Hazelwood Power Station, Australia, burning Morwell brown coal has been examined using Mössbauer spectroscopy. The principal iron phase in the precipitator ash and the uncooled ash deposit from a hot gas offtake was calcium aluminoferrite (Ca₂Fe_{2 ? x}Al_xO₅). Minor amounts of hematite (α-Fe₂O₃) and magnetite (Fe₃O₄) were also detected in the precipitator ash. The cooled superheater ash deposit contained a (Mg, Fe, Al) oxide spinel as the primary iron phase; small quantities of hematite were also detected in this deposit close to the heat exchanger interface. The formation of these iron phases has been rationalized on the basis of the average composition of coal delivered to the power station and supplementary ash chemistry data obtained from other techniques. The evidence suggests that the calcium aluminoferrite in the precipitator ash is derived from inorganic constituents (distributed throughout the coal organic matrix) and the hematite and magnetite are of mineral origin (discrete particles).     

第一批人凝血因子Ⅶ浓制剂国家标准品协作标定

目的 协作标定第一批人凝血因子Ⅶ浓制剂国家标准品。方法 以 WHO 97/592批人凝血因子Ⅶ浓制剂国际标准品为对照,采用一期法标定我国首批人凝血因子Ⅶ因家标准品,并将标准品分别置4℃、22℃、37℃保存5个月,用加速破坏试验进行稳定性考查。结果 该批标准品效价为16.0IU/支,稳定性良好。其他项目均达到国家标准品要求。结论 已成功研制第一批人凝血因子Ⅶ浓制剂国家标准品。     

Distribution, relative abundance, and prey demand by double-crested cormorants on Manitoulin Island lakes vs. Lake Huron Coast

An aerial distance sampling survey of double-crested cormorants (Phalacrocorax auritus) was conducted in the northern region of Lake Huron (North Channel; four largest lakes of Manitoulin Island; South Shore of Manitoulin Is. facing the main body of the lake) to assess the relative distribution, abundance and prey demand by cormorants on inland lake vs. coastal habitat. On a per area basis, the density (approx. 1-2 cormorants ? km^− 2) and prey demand (approx. 1.2 kg ha^− 1) of cormorants in the four inland lakes matched that of the North Channel. The South Shore had approximately half the density and prey demand as the other two areas. Cormorants on the inland lakes of Manitoulin Island represented 13% early in the season and a high of 33% of the total population for this region of Lake Huron later in the summer. Estimating regional distributions of cormorants within the Great Lakes basin is important because mapped nest colonies and nest counts are not representative of the actual distribution of foraging cormorants during and after the nesting season. There are two general conclusions to emerge from this survey. First, aquatic productivity from both Great Lakes coast and inland lakes contributes to trends in population and distribution of cormorants in the northern region of Lake Huron and perhaps elsewhere. Second, inland aquatic ecosystems are important throughout a season for foraging cormorants from the Great Lakes and may become more important as Great Lake productivity trends downward.     

Assessing the utility of airborne hyperspectral and LiDAR data for species distribution mapping in the coastal Pacific Northwest, Canada

To effectively manage forested ecosystems an accurate characterization of species distribution is required. In this study we assess the utility of hyperspectral Airborne Imaging Spectrometer for Applications (AISA) imagery and small footprint discrete return Light Detection and Ranging (LiDAR) data for mapping 11 tree species in and around the Gulf Islands National Park Reserve, in coastal South-western Canada. Using hyperspectral imagery yielded producer's and user's accuracies for most species ranging from > 52-95.4 and > 63-87.8%, respectively. For species dominated by definable growth stages, pixel-level fusion of hyperspectral imagery with LiDAR-derived height and volumetric canopy profile data increased both producer's (+ 5.1-11.6%) and user's (+ 8.4-18.8%) accuracies. McNemar's tests confirmed that improvements in overall accuracies associated with the inclusion of LiDAR-derived structural information were statistically significant (p < 0.05). This methodology establishes a specific framework for mapping key species with greater detail and accuracy then is possible using conventional approaches (i.e., aerial photograph interpretation), or either technology on its own. Furthermore, in the study area, acquisition and processing costs were lower than a conventional aerial photograph interpretation campaign, making hyperspectral/LiDAR fusion a viable replacement technology.     

Gremlin: an interactive visualization model for analyzing genomic rearrangements

In this work we present, apply, and evaluate a novel, interactive visualization model for comparative analysis of structural variants and rearrangements in human and cancer genomes, with emphasis on data integration and uncertainty visualization. To support both global trend analysis and local feature detection, this model enables explorations continuously scaled from the high-level, complete genome perspective, down to the low-level, structural rearrangement view, while preserving global context at all times. We have implemented these techniques in Gremlin, a genomic rearrangement explorer with multi-scale, linked interactions, which we apply to four human cancer genome data sets for evaluation. Using an insight-based evaluation methodology, we compare Gremlin to Circos, the state-of-the-art in genomic rearrangement visualization, through a small user study with computational biologists working in rearrangement analysis. Results from user study evaluations demonstrate that this visualization model enables more total insights, more insights per minute, and more complex insights than the current state-of-the-art for visual analysis and exploration of genome rearrangements.     

Visualising Errors in Animal Pedigree Genotype Data

Genetic analysis of a breeding animal population involves determining the inheritance pattern of genotypes for multiple genetic markers across the individuals in the population pedigree structure. However, experimental pedigree genotype data invariably contains errors in both the pedigree structure and in the associated individual genotypes, introducing inconsistencies into the dataset, rendering them useless for further analysis. The resolution of these errors requires consideration of genotype inheritance patterns in the context of the pedigree structure. Existing pedigree visualisations are typically more suited to human pedigrees and are less suitable for large complex animal pedigrees which may exhibit cross generational inbreeding. Similarly, table‐based viewers of genotype marker data can highlight where errors become apparent but lack the functionality and interactive visual feedback to allow users to locate the origin of errors within the pedigree. In this paper, we detail a design study steered by biologists who work with pedigree data, and describe successive iterations through approaches and prototypes for viewing genotyping errors in the context of a displayed pedigree. We describe how each approach performs with real pedigree genotype data and why eventually we deemed them unsuitable. Finally, a novel prototype visualisation for pedigrees, which we term the ‘sandwich view’, is detailed and we demonstrate how the approach effectively communicates errors in the pedigree context, supporting the biologist in the error identification task.     

Learning to transform time series with a few examples

We describe a semi-supervised regression algorithm that learns to transform one time series into another time series given examples of the transformation. This algorithm is applied to tracking, where a time series of observations from sensors is transformed to a time series describing the pose of a target. Instead of defining and implementing such transformations for each tracking task separately, our algorithm learns a memoryless transformation of time series from a few example input-output mappings. The algorithm searches for a smooth function that fits the training examples and, when applied to the input time series, produces a time series that evolves according to assumed dynamics. The learning procedure is fast and lends itself to a closed-form solution. It is closely related to nonlinear system identification and manifold learning techniques. We demonstrate our algorithm on the tasks of tracking RFID tags from signal strength measurements, recovering the pose of rigid objects, deformable bodies, and articulated bodies from video sequences. For these tasks, this algorithm requires significantly fewer examples compared to fully-supervised regression algorithms or semi-supervised learning algorithms that do not take the dynamics of the output time series into account.     

Gaining Confidence in Software Inspection Using a Bayesian Belief Model

In this paper I describe how confidence in a software inspection can be obtained through calculating an estimate of its effectiveness. The method uses a Bayesian Belief Network to model the software inspection process and calculates the inference on how effective a particular inspection was. This technique was selected as it provides a means of initialising the model with inspectors' experience and has the ability to learn and optimise performance. This technique provides answers to some of the questions and limitations raised by current models used to predict inspection effectiveness. The application of the model to a major software project is discussed, covering the initial practitioner survey, model initialisation, model calibration and verification results obtained.