Composition and amino acid content of meal from some commercially less important fishes of Maharashtra

Details are given of the yield and composition of dried waste from the filleting wastes of 3 commercially less utilized fish of the Maharashtra coast (Saurida tumbil, Caranx sexfasciatus and Sphyraena jello). The amino acid composition after acid hydrolysis is detailed for the three species.

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..

Temporal Coherence of Chlorophyll a during a Spring Phytoplankton Bloom in Xiangxi Bay of Three-Gorges Reservoir, China

Algal bloom phenomenon was defined as "the rapid growth of one or more phytoplankton species which leads to a rapid increase in the biomass of phytoplankton", yet most estimates of temporal coherence are based on yearly or monthly sampling frequencies and little is known of how synchrony varies among phytoplankton or of the causes of temporal coherence during spring algal bloom. In this study, data of chlorophyll a and related environmental parameters were weekly gathered at 15 sampling sites in Xiangxi Bay of Three-Gorges Reservoir (TGR, China) to evaluate patterns of temporal coherence for phytoplankton during spring bloom and test if spatial heterogeneity of nutrient and inorganic suspended particles within a single ecosystem influences synchrony of spring phytoplankton dynamics. There is a clear spatial and temporal variation in chlorophyll a across Xiangxi Bay. The degree of temporal coherence for chlorophyll a between pairs of sites located in Xiangxi Bay ranged from -0.367 to 0.952 with mean and median values of 0.349 and 0.321, respectively. Low levels of temporal coherence were often detected among the three stretches of the bay (Down reach, middle reach and upper reach), while high levels of temporal coherence were often found within the same reach of the bay. The relative difference of DIN between pair sites was the strong predictor of temporal coherence for chlorophyll a in down and middle reach of the bay, while the relative difference in Anorganic Suspended Solids was the important factor regulating temporal coherence in middle and upper reach. Contrary to many studies, these results illustrate that, in a small geographic area (a single reservoir bay of approximately 25 km), spatial heterogeneity influence synchrony of phytoplankton dynamics during spring bloom and local processes may override the effects of regional processes or dispersal.

FACTORS INFLUENCING CHLOROPHYLL A CONCENTRATION IN THE YANGTZE-CONNECTED LAKES

To determine the environmental factors influencing C, phytoplankton chlorophyll a (Ch1 a), field investigations 4 were conducted in three river-connected lakes (Dongting Lake, Poyang Lake and Shijiu Lake) of the Yangtze floodplain in 2004. Results showed that the average Chi a concentration in these lakes ranged from 2.98 to 3.65 mg m(-3). The major factors influencing Chl a in lentic and lotic regions were total phosphorus (TP) and water velocity (U), respectively. Multiple relationships including total nitrogen (log(10)TN) and water depth (log(10)Z) were established. Further analyses found that the absolute Chi a and slope of log(10)Chl a=f (log(10)TP) in the river-connected lakes were obviously lower than those in the river-isolated lakes. This suggests the river-lake connectivity can significantly modify relationship between TP and chlorophyll a concentration.

Influence of dew on biomass and photosystem II activity of cyanobacterial crusts in the Hopq Desert, northwest China

Dew is an important water source for desert organisms in semiarid and arid regions. Both field and laboratory experiments were conducted to investigate the possible roles of dew in growth of biomass and photosynthetic activity within cyanobacterial crust. The cyanobacteria, Microcoleus vaginatus Gom. and Scytonema javanicum (Kutz.) Born et Flah., were begun with stock cultures and sequential mass cultivations, and then the field experiment was performed by inoculating the inocula onto shifting sand for forming cyanobacterial crust during late summer and autumn of 2007 in Hopq Desert, northwest China. Measurements of dew amount and Chlorophyll a content were carried out in order to evaluate the changes in crust biomass following dew. Also, we determined the activity of photosystem II(PSII) within the crust in the laboratory by simulating the desiccation/rehydration process due to dew. Results showed that the average daily dew amount as measured by the cloth-plate method (CPM) was 0.154 mm during fifty-three days and that the crust biomass fluctuated from initial inoculation of 4.3 mu g Chlorophyll a cm(-2) sand to 5.8-7.3 mu g Chlorophyll a cm(-2) crust when dew acted as the sole water source, and reached a peak value of approximately 8.2 mu g Chlorophyll a cm(-2) crust owing to rainfalls. It indicated that there was a highly significant correlation between dew amounts and crust moistures (r = 0.897 or r = 0.882, all P < 0.0001), but not a significant correlation between dew and the biomass (r = 0.246 or r = 0.257, all P > 0.05), and thus concluded that dew might only play a relatively limited role in regulating the crust biomass. Correspondingly, we found that rains significantly facilitated biomass increase of the cyanobacterial crust. Results from the simulative experiment upon rehydration showed that approximately 80% of PSII activity could be achieved within about 50 min after rehydration in the dark and at 5 degrees C, and only about 20% of the activity was light-temperature dependent. This might mean that dew was crucial for cyanobacterial crust to rapidly activate photosynthetic activity during desiccation and rehydration despite low temperatures and weak light before dawn. It also showed in this study that the cyanobacterial crusts could receive and retain more dew than sand, which depended on microclimatic characteristics and soil properties of the crusts. It may be necessary for us to fully understanding the influence of dew on regulating the growth and activity of cyanobacterial crust, and to soundly evaluate the crust's potential application in fighting desertification because of the available water due to dew. (C) 2009 Published by Elsevier Ltd.

Forecasting Daily Chlorophyll a Concentration during the Spring Phytoplankton Bloom Period in Xiangxi Bay of the Three-Gorges Reservoir by Means of a Recurrent Artificial Neural Network

A recurrent artificial neural network was used for 0-and 7-days-ahead forecasting of daily spring phytoplankton bloom dynamics in Xiangxi Bay of Three-Gorges Reservoir with meteorological, hydrological, and limnological parameters as input variables. Daily data from the depth of 0.5 m was used to train the model, and data from the depth of 2.0 m was used to validate the calibrated model. The trained model achieved reasonable accuracy in predicting the daily dynamics of chlorophyll a both in 0-and 7-days-ahead forecasting. In 0-day-ahead forecasting, the R-2 values of observed and predicted data were 0.85 for training and 0.89 for validating. In 7-days-ahead forecasting, the R-2 values of training and validating were 0.68 and 0.66, respectively. Sensitivity analysis indicated that most ecological relationships between chlorophyll a and input environmental variables in 0-and 7-days-ahead models were reasonable. In the 0-day model, Secchi depth, water temperature, and dissolved silicate were the most important factors influencing the daily dynamics of chlorophyll a. And in 7-days-ahead predicting model, chlorophyll a was sensitive to most environmental variables except water level, DO, and NH3N.

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.