Study on the sustainability of FPC produced from Kilka (Clupeonella engrauliformis, C. grimmi and C. cultriventris) in two vacuum packaging and modified atmosphere packaging at different environmental factors

Fish protein concentrate (FPC) is a healthy, sustainable and high nutritive product which sanitized produced from fishes in which, protein and other nutrients are more concentrated than in fresh fishes. The aim of this research is to study on the sustainability of FPC produced from Kilka (Clupeonella engrauliformis , C. grimmi and C. cultriventris) in two Vaccum Packaging and Modified Atmosphere Packaging at different environmental factors during six months. In our study the analysis of FPC protein showed 91.2%, lipid: 0.5%, ash: 3.6%, moisture: 2.3%, Total Volatile Nitrogen: 10 ml/100gr and peroxide: 5meq/kg. Amino acids and fatty acids were also determined. Bacteria and Fungi were lower than 1000 colony. Samples are kept in different condition of temperature (5, 20 and 35 degree centigrade), humidity (25, 40 and 90 percent) and light and dark environment in six month. Lipid rate in FPC after 6 months in VP and MAP (60% C02, 30 % N2 and 10% O2), packages was decreased but was not significant (P>0.05). It was also detected that increase temperature lead to more decrease in lipid content. Protein rate of FPC was decreased from 91.2% to 73.6% during six months at 35°C in VP Package and from 91.2% to 69.4% in MAP package. These changes were significant (P<0.05). TVN and PV rate in FPC after 6 months in VP and MAP packages was increased but was significant (P<0.05). Amino acids and fatty acids were also determined. But more changes in MAP packages was detected.

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.

Lead exposure through gestation-only caused long-term learning/memory deficits in young adult offspring

Numerous observations in clinical and preclinical studies indicate that the developing brain is particular sensitive to lead (Pb)'s pernicious effects. However, the effect of gestation-only Pb exposure on cognitive functions at maturation has not been studied. We investigated the potential effects of three levels of Pb exposure (low, middle, and high Pb: 0.03%, 0.09%, and 0.27% of lead acetate-containing diets) at the gestational period on the spatial memory of young adult offspring by Morris water maze spatial learning and fixed location/visible platform tasks. Our results revealed that three levels of Pb exposure significantly impaired memory retrieval in male offspring, but only female offspring at low levels of Pb exposure showed impairment of memory retrieval. These impairments were not due to the gross disturbances in motor performance and in vision because these animals performed the fixed location/visible platform task as well as controls, indicating that the specific aspects of spatial learning/memory were impaired. These results suggest that exposure to Pb during the gestational period is sufficient to cause long-term learning/memory deficits in young adult offspring. (C) 2003 Elsevier Inc. All rights reserved.

A study of capacitance-voltage characteristics of lead zirconate titanate ferroelectric thin films and their usage to investigate polarization and coercive field.

Capacitance-voltage (C-V) characteristics of lead zirconate titanate (PZT) thin films with a thickness of 130 nm were measured between 300 and 533 K. The transition between ferroelectric and paraelectric phases was revealed to be of second order in our case, with a Curie temperature at around 450 K. A linear relationship was found between the measured capacitance and the inverse square root of the applied voltage. It was shown that such a relationship could be fitted well by a universal expression of C/A = k(V+V(0))(-1/2) and that this expression could be derived by expanding the Landau-Devonshire free energy at an effective equilibrium position of the Ti/Zr ion in a PZT unit cell. By using the derived equations in this work, the free energy parameters for an individual material can be obtained solely from the corresponding C-V data, and the temperature dependences of both remnant polarization and coercive voltage are shown to be in quantitative agreement with the experimental data.

Magnetic sensor with compensation for lead and coil resistance

A measurement system for magnetic fields or electric currents uses a single-core fluxgate, magneto-inductive or magneto-impedance device driven from a radio frequency excitation source. Flux nulling feedback circuitry is provided to maintain the core of the sensor at substantially zero net flux and improve the linearity and dynamic response of the sensor system. A high pass filter is provided for reducing the dc effects of the ohmic resistance of the coil and lead wires on the effectiveness of the flux nulling feedback.

Impacts of concurrency, iteration, design review, and problem complexity on design project lead time and error generation

A case study of an aircraft engine manufacturer is used to analyze the effects of management levers on the lead time and design errors generated in an iteration-intensive concurrent engineering process. The levers considered are amount of design-space exploration iteration, degree of process concurrency, and timing of design reviews. Simulation is used to show how the ideal combination of these levers can vary with changes in design problem complexity, which can increase, for instance, when novel technology is incorporated in a design. Results confirm that it is important to consider multiple iteration-influencing factors and their interdependencies to understand concurrent processes, because the factors can interact with confounding effects. The article also demonstrates a new approach to derive a system dynamics model from a process task network. The new approach could be applied to analyze other concurrent engineering scenarios. © The Author(s) 2012.

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.