Modelling interacting phenomena during the solidification of metals in the casting process

A variety of interacting complex phenomena takes place during the casting of metallic components. Here molten metal is poured into a mould cavity where it flows, cools, solidifies and then deforms in its solid state. As the metal cools, thermal gradients will promote thermal convection which will redistribute the heat around the component (usually from feeders or risers) towards the solidification front and mushy zone. Also, as the evolving solid regions of the cast component deform they will form gap at the cast-mould interface. This gap may change the rate of solidification in certain parts the casting, hence affecting the manner in which the cast component solidifies. Interaction between a cast component and its surrounding mould will also govern stress magnitudes in both the cast and mould -these may lead to defects such as cracks. This paper presents a multiphysics modelling approach to this complex process. Emphasis will be placed on the interacting phenomena taking place during the process and the modelling strategy used. Comparisons with plant data are also be given.

Everglades R-EMAP 2005

The Everglades R-EMAP project for year 2005 produced large quantities of data collected at 232 sampling sites. Data collection and analysis is an on-going long-term activity conducted by scientists of different disciplines at irregular intervals of several years. The data sets collected for 2005 include bio-geo-chemical (including mercury and hydro period), fish, invertebrate, periphyton, and plant data. Each sampling site is associated with a location, a description of the site to provide a general overview and photographs to provide a pictorial impression. The Geographic Information Systems and Remote Sensing Center(GISRSC) at Florida International University (FIU) has designed and implemented an enterprise database for long-term storage of the project�s data in a central repository, providing the framework of data storage for the continuity of future sampling campaigns and allowing integration of new sample data as it becomes available. In addition GISRSC provides this interactive web application for easy, quick and effective retrieval and visualization of that data.

Soil management systems for sustainable melon cropping in the Submedian of the São Francisco Valley.

Changes in soils management systems, including the application of green manure, are able to increase crop productivity. The aim of this study was to propose a soil management system with the use of green manure to improve the nutritional status and melon productivity in the submedian of the São Francisco Valley. The experiment was installed in Typic Plinthustalf and conducted in split plot. There were two soil tillage systems, tillage (T) and no tillage (NT), and three types of green manure (two vegetal cocktails: VC1- 75% legumes (L) + 25% non-legumes (NL); VC2- 25% L+ 75% NL and spontaneous vegetation (SV)). The experimental design was a randomised block with four replications. Fourteen species of legumes, grasses and oilseeds were used for the composition of the plant cocktails. We evaluated production of the dry shoot and root biomass and carbon and nutrient accumulation by green manures and melon plant. Data were subjected to analysis of variance and the treatment means were compared by Tukey´s test (P<0.05). Shoot biomass production and carbon and nutrient accumulation were higher in plant mixtures compared to spontaneous vegetation. The root system of the plant cocktails added larger quantities of biomass and nutrients to the soil to a depth of 0.60 m when compared to the spontaneous vegetation. The cultivation of plant cocktails with soil tillage, regardless of their composition, is a viable alternative for adding biomass and nutrients to the soil in melon crops in semi-arid conditions, providing productivity increases.

miRPlant : an integrated tool for identification of plant miRNA from RNA sequencing data

Background Small RNA sequencing is commonly used to identify novel miRNAs and to determine their expression levels in plants. There are several miRNA identification tools for animals such as miRDeep, miRDeep2 and miRDeep*. miRDeep-P was developed to identify plant miRNA using miRDeep’s probabilistic model of miRNA biogenesis, but it depends on several third party tools and lacks a user-friendly interface. The objective of our miRPlant program is to predict novel plant miRNA, while providing a user-friendly interface with improved accuracy of prediction. Result We have developed a user-friendly plant miRNA prediction tool called miRPlant. We show using 16 plant miRNA datasets from four different plant species that miRPlant has at least a 10% improvement in accuracy compared to miRDeep-P, which is the most popular plant miRNA prediction tool. Furthermore, miRPlant uses a Graphical User Interface for data input and output, and identified miRNA are shown with all RNAseq reads in a hairpin diagram. Conclusions We have developed miRPlant which extends miRDeep* to various plant species by adopting suitable strategies to identify hairpin excision regions and hairpin structure filtering for plants. miRPlant does not require any third party tools such as mapping or RNA secondary structure prediction tools. miRPlant is also the first plant miRNA prediction tool that dynamically plots miRNA hairpin structure with small reads for identified novel miRNAs. This feature will enable biologists to visualize novel pre-miRNA structure and the location of small RNA reads relative to the hairpin. Moreover, miRPlant can be easily used by biologists with limited bioinformatics skills.

Wastewater treatment plant inspection program, fiscal year 2006 and 2007 data report

This data report includes the results from Alachua County Environmental Protection Department’s inspections of wastewater treatment plants (WWTP) within Alachua County during the 2006 and 2007 fiscal years (October 2005 – September 2007). Groundwater monitoring data provided to the Florida Department of Environmental Protection Department by the WWTP operators is included for those treatment plants that are required to submit this information (PDF has 44 pages.)

Knowledge-elicitation and data-mining: fusing human and industrial plant information

Knowledge-elicitation is a common technique used to produce rules about the operation of a plant from the knowledge that is available from human expertise. Similarly, data-mining is becoming a popular technique to extract rules from the data available from the operation of a plant. In the work reported here knowledge was required to enable the supervisory control of an aluminium hot strip mill by the determination of mill set-points. A method was developed to fuse knowledge-elicitation and data-mining to incorporate the best aspects of each technique, whilst avoiding known problems. Utilisation of the knowledge was through an expert system, which determined schedules of set-points and provided information to human operators. The results show that the method proposed in this paper was effective in producing rules for the on-line control of a complex industrial process. (C) 2005 Elsevier Ltd. All rights reserved.

A method for under-sampled ecological network data analysis: plant-pollination as case study

In this paper, we develop a method, termed the Interaction Distribution (ID) method, for analysis of quantitative ecological network data. In many cases, quantitative network data sets are under-sampled, i.e. many interactions are poorly sampled or remain unobserved. Hence, the output of statistical analyses may fail to differentiate between patterns that are statistical artefacts and those which are real characteristics of ecological networks. The ID method can support assessment and inference of under-sampled ecological network data. In the current paper, we illustrate and discuss the ID method based on the properties of plant-animal pollination data sets of flower visitation frequencies. However, the ID method may be applied to other types of ecological networks. The method can supplement existing network analyses based on two definitions of the underlying probabilities for each combination of pollinator and plant species: (1), pi,j: the probability for a visit made by the i’th pollinator species to take place on the j’th plant species; (2), qi,j: the probability for a visit received by the j’th plant species to be made by the i’th pollinator. The method applies the Dirichlet distribution to estimate these two probabilities, based on a given empirical data set. The estimated mean values for pi,j and qi,j reflect the relative differences between recorded numbers of visits for different pollinator and plant species, and the estimated uncertainty of pi,j and qi,j decreases with higher numbers of recorded visits.

Relationships between plant traits and climate in the Mediterranean region: an analysis based on pollen data

Question: What are the correlations between the degree of drought stress and temperature, and the adoption of specific adaptive strategies by plants in the Mediterranean region? Location: 602 sites across the Mediterranean region. Method: We considered 12 plant morphological and phenological traits, and measured their abundance at the sites as trait scores obtained from pollen percentages. We conducted stepwise regression analyses of trait scores as a function of plant available moisture (α) and winter temperature (MTCO). Results: Patterns in the abundance for the plant traits we considered are clearly determined by α, MTCO or a combination of both. In addition, trends in leaf size, texture, thickness, pubescence and aromatic leaves and other plant level traits such as thorniness and aphylly, vary according to the life form (tree, shrub, forb), the leaf type (broad, needle) and phenology (evergreen, summer-green). Conclusions: Despite conducting this study based on pollen data we have identified ecologically plausible trends in the abundance of traits along climatic gradients. Plant traits other than the usual life form, leaf type and leaf phenology carry strong climatic signals. Generally, combinations of plant traits are more climatically diagnostic than individual traits. The qualitative and quantitative relationships between plant traits and climate parameters established here will help to provide an improved basis for modelling the impact of climate changes on vegetation and form a starting point for a global analysis of pollen-climate relationships