Plankton identified in stomach contents of Oreochromis niloticus (Pisces, Cichlidae) and the water system of Lakes Edward, George, and Kazinga channel - Uganda

The samples were collected from Lake Edward at Rwenshama, Kisenyi and Katwe, and from Lake George at Mahyoro, Kashaka and Kasenyi and in Kazinga Channel at Katunguru. The organisms identified from the water samples obtained irrespective of station or depth were mainly the phytoplankton (diatoms, blue-green algae and green algae). Of the phytoplankton, blue green-algae were the most abundant both in quantity and number of species especially in L. George. In order of importance were Microcystis spp, Planktolyngbya spp and Anabaenopsis spp were the dominant blue greens. Diatoms and green algae were present but less abundant. The estimated proportions of different types of phytoplankton identified in O. niloticus stomach contents indicate that bluegreen algae were the most abundant followed by the diatoms and green algae.

Identification of Materials from Construction Site Images Using Content Based Image Retrieval Techniques

The capability to automatically identify shapes, objects and materials from the image content through direct and indirect methodologies has enabled the development of several civil engineering related applications that assist in the design, construction and maintenance of construction projects. Examples include surface cracks detection, assessment of fire-damaged mortar, fatigue evaluation of asphalt mixes, aggregate shape measurements, velocimentry, vehicles detection, pore size distribution in geotextiles, damage detection and others. This capability is a product of the technological breakthroughs in the area of Image and Video Processing that has allowed for the development of a large number of digital imaging applications in all industries ranging from the well established medical diagnostic tools (magnetic resonance imaging, spectroscopy and nuclear medical imaging) to image searching mechanisms (image matching, content based image retrieval). Content based image retrieval techniques can also assist in the automated recognition of materials in construction site images and thus enable the development of reliable methods for image classification and retrieval. The amount of original imaging information produced yearly in the construction industry during the last decade has experienced a tremendous growth. Digital cameras and image databases are gradually replacing traditional photography while owners demand complete site photograph logs and engineers store thousands of images for each project to use in a number of construction management tasks. However, construction companies tend to store images without following any standardized indexing protocols, thus making the manual searching and retrieval a tedious and time-consuming effort. Alternatively, material and object identification techniques can be used for the development of automated, content based, construction site image retrieval methodology. These methods can utilize automatic material or object based indexing to remove the user from the time-consuming and tedious manual classification process. In this paper, a novel material identification methodology is presented. This method utilizes content based image retrieval concepts to match known material samples with material clusters within the image content. The results demonstrate the suitability of this methodology for construction site image retrieval purposes and reveal the capability of existing image processing technologies to accurately identify a wealth of materials from construction site images.

Content Based Integration of Construction Site Images in Architecture, Engineering, Construction, and Facilities Management (AEC/FM) Model based Systems

This book explores the processes for retrieval, classification, and integration of construction images in AEC/FM model based systems. The author describes a combination of techniques from the areas of image and video processing, computer vision, information retrieval, statistics and content-based image and video retrieval that have been integrated into a novel method for the retrieval of related construction site image data from components of a project model. This method has been tested on available construction site images from a variety of sources like past and current building construction and transportation projects and is able to automatically classify, store, integrate and retrieve image data files in inter-organizational systems so as to allow their usage in project management related tasks. objects. Therefore, automated methods for the integration of construction images are important for construction information management. During this research, processes for retrieval, classification, and integration of construction images in AEC/FM model based systems have been explored. Specifically, a combination of techniques from the areas of image and video processing, computer vision, information retrieval, statistics and content-based image and video retrieval have been deployed in order to develop a methodology for the retrieval of related construction site image data from components of a project model. This method has been tested on available construction site images from a variety of sources like past and current building construction and transportation projects and is able to automatically classify, store, integrate and retrieve image data files in inter-organizational systems so as to allow their usage in project management related tasks.

Content Based Integration of Construction Site Images in AEC/FM Model Based Systems

The Architecture, Engineering, Construction and Facilities Management (AEC/FM) industry is rapidly becoming a multidisciplinary, multinational and multi-billion dollar economy, involving large numbers of actors working concurrently at different locations and using heterogeneous software and hardware technologies. Since the beginning of the last decade, a great deal of effort has been spent within the field of construction IT in order to integrate data and information from most computer tools used to carry out engineering projects. For this purpose, a number of integration models have been developed, like web-centric systems and construction project modeling, a useful approach in representing construction projects and integrating data from various civil engineering applications. In the modern, distributed and dynamic construction environment it is important to retrieve and exchange information from different sources and in different data formats in order to improve the processes supported by these systems. Previous research demonstrated that a major hurdle in AEC/FM data integration in such systems is caused by its variety of data types and that a significant part of the data is stored in semi-structured or unstructured formats. Therefore, new integrative approaches are needed to handle non-structured data types like images and text files. This research is focused on the integration of construction site images. These images are a significant part of the construction documentation with thousands stored in site photographs logs of large scale projects. However, locating and identifying such data needed for the important decision making processes is a very hard and time-consuming task, while so far, there are no automated methods for associating them with other related objects. Therefore, automated methods for the integration of construction images are important for construction information management. During this research, processes for retrieval, classification, and integration of construction images in AEC/FM model based systems have been explored. Specifically, a combination of techniques from the areas of image and video processing, computer vision, information retrieval, statistics and content-based image and video retrieval have been deployed in order to develop a methodology for the retrieval of related construction site image data from components of a project model. This method has been tested on available construction site images from a variety of sources like past and current building construction and transportation projects and is able to automatically classify, store, integrate and retrieve image data files in inter-organizational systems so as to allow their usage in project management related tasks.

Content-Based Search Engines for Construction Image Databases

Images represent a valuable source of information for the construction industry. Due to technological advancements in digital imaging, the increasing use of digital cameras is leading to an ever-increasing volume of images being stored in construction image databases and thus makes it hard for engineers to retrieve useful information from them. Content-Based Search Engines are tools that utilize the rich image content and apply pattern recognition methods in order to retrieve similar images. In this paper, we illustrate several project management tasks and show how Content-Based Search Engines can facilitate automatic retrieval, and indexing of construction images in image databases.

A 5-step reduced mechanism for combustion of CO/H2/H2O/CH4/CO2 mixtures with low hydrogen/methane and high H2O content

In this study a 5-step reduced chemical kinetic mechanism involving nine species is developed for combustion of Blast Furnace Gas (BFG), a multi-component fuel containing CO/H2/CH4/CO2, typically with low hydrogen, methane and high water fractions, for conditions relevant for stationary gas-turbine combustion. This reduced mechanism is obtained from a 49-reaction skeletal mechanism which is a modified subset of GRI Mech 3.0. The skeletal and reduced mechanisms are validated for laminar flame speeds, ignition delay times and flame structure with available experimental data, and using computational results with a comprehensive set of elementary reactions. Overall, both the skeletal and reduced mechanisms show a very good agreement over a wide range of pressure, reactant temperature and fuel mixture composition. © 2012 The Combustion Institute..

Growth and food availability of silver and bighead carps: evidence from stable isotope and gut content analysis

A 2-year investigation of growth and food availability of silver carp and bighead was carried out using stable isotope and gut content analysis in a large pen in Meiliang Bay of Lake Taihu, China. Both silver carp and bighead exhibited significantly higher delta 13C in 2005 than in 2004, which can probably be attributed to two factors: (i) the difference between isotopic compositions at the base of the pelagic food web and (ii) the difference between the compositions of prey items and stable isotopes. The significantly positive correlations between body length, body weight and stable isotope ratios indicated that isotopic changes in silver carp and bighead resulted from the accumulation of biomass concomitant with rapid growth. Because of the drastic decrease in zooplankton in the diet in 2005, silver carp and bighead grew faster in 2004 than in 2005. Bighead carp showed a lower trophic level than silver carp in 2005 as indicated by stable nitrogen isotope ratios, which was possibly explained by the interspecific difference between the prey species and the food quality of silver carp and bighead.

Effects of Arsenate on Microcystin Content and Leakage of Microcystis Strain PCC7806 Under Various Phosphate Regimes

Both arsenic pollution and eutrophication are prominent environmental issues when considering the problem of global water pollution. It is important to reveal the effects of arsenic species on cyanobacterial growth and toxin yields to assess ecological risk of arsenic pollution or at least understand naturally occurring blooms. The sensitivity of cyanobacteria to arsenate has often been linked to the structural similarities of arsenate and phosphate. Thus, we approached the effect of arsenate with concentrations from 10(-8) to 10(-4) M on Microcystis strain PCC7806 under various phosphate regimes. The present study showed that Microcystis strain PCC7806 was arsenate tolerant up to 10(-4) M. And such tolerance was without reference to both content of intra- and extra-cellular phosphate. It seems that arsenate involved the regulation of microcystin synthesis and cellular polyphosphate contributed to microcystin production of Microcystis responding to arsenate, since there was a positive linear correlation of the cellular microcystin quota with the exposure concentration of arsenate when the cells were not preconditioned to phosphate starvation. It is presumed that arsenate could help to actively export microcystins from living Microcystis cells when preconditioned to phosphate starvation and incubated with the medium containing 1 mu M phosphate. This study firstly provided evidence that microcystin content and/or release of Microcystis might be impacted by arsenate if it exists in harmful algal blooms. (C) 2008 Wiley Periodicals, Inc. Environ Toxicol 24:97 94, 2009.

Distribution of microcystins in various organs (heart, liver, intestine, gonad, brain, kidney and lung) of Wistar rat via intravenous injection

The distribution of microcystins (MCs) in various tissues of Wistar rats was studied under laboratory conditions. Rats were injected intravenously (i.v.) with extracted MCs at a dose of 80 mu g MC-LRequivalent/kg body weight. MCs concentrations in various tissues were detected at 1, 2. 4, 6, 12 and 24 h post-injection using liquid chromatography-mass spectrometry (LC-MS). The highest concentration of MCs was found in kidney (0.034-0.295 mu g/g dry weight), followed by lung (0.007-0.067 mu g/g dry weight), stomach (0.010-0.058 mu g/g dry weight) and liver (0.003-0.052 mu g/g dry weight). The maximum MCs content in the whole body of rat, 2.9% of the injected dose, was observed at 2 h post-injection. MCs concentration was higher in kidney than in liver during the experiment, and two peaks of MCs concentration (at 2 and 24 h, respectively) were observed in kidney, indicating that MCs can be excreted directly via kidney of rat. Though heart, intestine, spleen, brain, gonad and stomach contained less than 0.2% of injected MCs during the whole experiment stage, the presence of MCs in these tissues represents potential damage to them. (c) 2008 Elsevier Ltd. All Fights reserved.