Influence of fat content on the quality and shelf life of cured sardines

The paper presents results of studies on the effect of seasonal variations in the fat content on the quality and shelf life of dry cured, pickle cured and smoke cured oil sardines. The merits and defects of each method of curing during different seasons are discussed.

Seasonal variations in the total cholesterol content of the liver of cat fish Heteropnuestes fossilis (Bloch)

Variations in the total liver cholesterol content of Heteropnuestes fossilis followed well defined seasonal cycle. A decline was recorded during the period when the gonads were passing through the peak ripe stage. The highest value of the cholesterol was however attained before this phase of gonad maturation. Concentration pattern of liver cholesterol seemed related to variations in the cholesterol metabolism of the fish, necessitated, besides other factors, by the demand for sex hormones. A possibility of the influence of feeding intensity on liver cholesterol content has also been indicated.

Comparative study of the nutrient content of fish and shell fish

The amino acid, mineral and proximate composition of mullet (Mugil oeur), mackerel (Rastrelliger kanagurta), crab (Scylla serrata) and prawn (Penaeus indicus) are reported. The data are used for comparing the nutritional quality of the fish and shell fish. Further, the amino acid composition is screened for their adequacy to meet the FAO/WHO recommended pattern of essential amino acids.

The features of synonymous codon bias and GC-content relationship in human genes

728 human genes were divided to four groups according to the GC contents of their coding sequences (from GC<0.43 to GC>0.58). Examination of synonymous-codon bias in the 4 groups show that NTG (N represents any base of T, A, C, G) is most favored and NCG

Effects of dietary supplementation of commercial human probiotic and antibiotic on the growth rate and content of intestinal microflora in ornamental fishes

The effects of dietary supplementation of commercial human probiotic, Lactobacil and antibiotic, oxytetracycline on the growth, survival, disease resistance and content of intestinal microflora in two ornamental fishes, viz., goldfish, Carassius auratus and swordtail, Xiphophorus helleri were studied. The total wet weight gain, food conversion ratio and specific growth rate of C. auratus did not vary significantly (p>0.05) among treatments. While in X. helleri, significant differences existed in the total wet weight gain, survival, food conversion ratio and specific growth rate among treatment groups (p<0.05). The counts of antibiotic resistant bacteria in fish gut increased with days of culture in all the treatments and the increase was more in antibiotic fed fishes. A reduction in the development of antibiotic resistance among the bacterial flora of fish gut was noticed in probiotic fed groups of C auratus and X. helleri. The results of the present study revealed that the effects of human probiotic on the growth, survival and disease resistance of ornamental fishes are variable and difficult to reproduce the similar effect on different species.

Fisheries, marine environment, and integrated coastal management policy processes, content and implementation in the BOBLME countries: regional synthesis and country questionnaires

This paper examines policy processes, policy trends and policy implementation with regards to capture fisheries, the marine environment and Integrated Coastal Management (ICM) in BOBLME countries. Individual country information was analyzed to generate a regional synthesis.

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.