Study on fish hatchery and nurseries in Mymensingh, Bangladesh

In generally, fish hatchery and nursery owners having both hatchery and nursery facilities were financially stronger, well-educated and well-trained than only nursery ponds owners in Mymensingh aquaculture region. On the other hand, only nursery pond owners were more experienced in fish seed business than only hatchery owners. Most of the owners were satisfied with existing communication facilities. Lack of technical knowledge was one of the major constraints which could be solved by ensuring proper training. This business can be made more profitable providing loan to poor farmers and improving law and order situation.

What needs to be done?: Guide in mangrove reforestation

The article presents a two-part guideline in mangrove reforestation. The first part is zonation, which is the process of determining what species are particularly suited to plant in a particular site. While, plantation establishment is the second part, it includes guides in the identification of species, selection of planting site, preparation of the planting sites, seed collection, handling and transporting of seeds, and planting.

Impact of sedimentalogical processes on mangrove ecosystem of the Indus Delta

A detailed sedimentalogical study concerning the depletion of mangrove in the Indus Delta due to the marked decrease in the supply of silt was undertaken. Thirty one stations were established for sampling in a selected area of 12000 hectares between Korangi creek and Wad do Khuddi creek. Seventy one samples of soil were collected from 6cm and 1m depth, out of which fifty one samples were selected for sedimentalogical studies. It was inferred from this study that the marine depositional processes are distinctly dominating over the fluvial processes, which is major cause in decreasing the growth of mangrove. It was also inferred that among the sampled stations the sites having clayey silt (with silt 60%-70% and clay 25%-30%) are most favourable for mangrove plantation.

The socio-economic and cultural structures of the fisherfolk communities

Fishing communities that have exploited the resource for generations constitute the main stakeholder groups in the fisheries of Lake Victoria. Several studies have examined Uganda's Lake Victoria fishing communities and characterised key stakeholders at community level over the last decade (SEDAWOG 1999a and b; Geheb 1997; FeSEP 1997; Kitakule 1991). The communities are made up of scattered settlements at the shores and on islands. The categories of people living in these communities include fishers who consist primarily of large numbers of male youths who provide labour to boat and gear owners. There are resident and non-resident fish traders who after securing their supplies at the beaches, depart for their market destinations. In addition, there are fish processors, mostly operating traditional and improved smoking kilns. Many other people, dealing in provisions and supplies also stay at the beaches, their activities depending on the level of fish catch. The fishing communities of Lake Victoria, Uganda, include auxiliary livelihood activities such as boat building, net repairing and transportation; bait supply and beachside kiosks, video halls and retail shop business. Other economic activities are brick making, charcoal burning/wood trade, farming and livestock keeping.

Assessment of the performance of beach management units (BMUs) on Lakes Victoria, Albert, Kyoga and George

Until the late 1990s the fisheries of Ugandan lakes had been managed by government where stakeholders were excluded from the decision-making process. In order to involve other stakeholders, co-management was adopted. Operationalising Co-management on landing sites has led to the formation of BMUs at gazetted landing sites. A BMU is made up of a BMU assembly and the BMU committee that it elects. A BMU committee should be: 30% boat owners; 30% boat barias 30% including fish processors, boat makers, local gear makers and repairers, fishing input dealers and managers and 10% fish mongers/traders; and if possible, 30% women. To operate at a particular landing site, one must be registered with the BMU. The BMU assembly is the supreme organ of a BMU empowered to elect, approve and remove the BMU committee

Final report of the baseline data reconstruction for the Quality Assurance for Fish Marketing Project

A baseline survey for the project which had been conducted in 2009 had gaps that could not allow assessment of project performance in the outcome and impact indicators to be made. This study was, therefore, commissioned to reconstruct the baseline data, aligned to the impact and outcome indicators on the project logframe and results framework, against which project achievements could be assessed. The purpose and scope of the study was to reconstruct the baseline data and analysis describing the situation prior to QAFM Project inception, taking 2008 as the baseline year, which was aligned to the project logframe outcome and impact indicators; to collect data on current status to compare project outcome (and where possible impact) in improved fish handling sites in comparison with the baseline as well as with comparable non-improved fish landing sites as control group. The study was conducted through secondary data search from sources at NaFIRRI, DFR and ICEIDA. Field data collection was carried out using a sample survey covering 312 respondents including boat and gear owners, crew members, processors and traders at eight project and two control landing sites. Key Informant Interviews were conducted with DFOs and BMU leaders in the study districts and landing sites respectively.

A note on fishery development on the Kafue Floodplain

The annual fish catch from the Kafue floodplain varies between 2,000 and 11,000 tons with a median of 5,500 tons valued at about K 500,000. It is believed that over 1,500 persons fish full time in the area. Fishermen's earnings can vary from a ew ngwee per day to a period in June 1970 when in one day four owners of drawnets in the Maala area, after hauling 13 times, caught 395 kg of fish valued at K 43.45 (i.e. about K 10 each per day). (I Kwacha=IOO Ngwee=US $1.40 Fresh fish is bought by traders with five to ten ton trucks more commonly in the upstream Namwala sector than in the central and downstream sectors of the floodplain (where consignments tend to weigh less than 125 kg). Over 25 tons (fresh weight equivalent) may pass through a fish market such as Busangu each month. Dried fish is destined for a greater range of towns than fresh fish.

Detecting air pockets for architectural concrete quality assessment using visual sensing

Air pockets, one kind of concrete surface defects, are often created on formed concrete surfaces during concrete construction. Their existence undermines the desired appearance and visual uniformity of architectural concrete. Therefore, measuring the impact of air pockets on the concrete surface in the form of air pockets is vital in assessing the quality of architectural concrete. Traditionally, such measurements are mainly based on in-situ manual inspections, the results of which are subjective and heavily dependent on the inspectors’ own criteria and experience. Often, inspectors may make different assessments even when inspecting the same concrete surface. In addition, the need for experienced inspectors costs owners or general contractors more in inspection fees. To alleviate these problems, this paper presents a methodology that can measure air pockets quantitatively and automatically. In order to achieve this goal, a high contrast, scaled image of a concrete surface is acquired from a fixed distance range and then a spot filter is used to accurately detect air pockets with the help of an image pyramid. The properties of air pockets (the number, the size, and the occupation area of air pockets) are subsequently calculated. These properties are used to quantify the impact of air pockets on the architectural concrete surface. The methodology is implemented in a C++ based prototype and tested on a database of concrete surface images. Comparisons with manual tests validated its measuring accuracy. As a result, the methodology presented in this paper can increase the reliability of concrete surface quality assessment

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.

Comparison of manual and user guided methodologies for the classification and retrieval of construction site images

The amount of original imaging information produced yearly during the last decade has experienced a tremendous growth in all industries due to the technological breakthroughs in digital imaging and electronic storage capabilities. This trend is affecting the construction industry as well, where digital cameras and image databases are gradually replacing traditional photography. Owners demand complete site photograph logs and engineers store thousands of images for each project to use in a number of construction management tasks like monitoring an activity's progress and keeping evidence of the "as built" in case any disputes arise. So far, retrieval methodologies are done manually with the user being responsible for imaging classification according to specific rules that serve a limited number of construction management tasks. New methods that, with the guidance of the user, can automatically classify and retrieve construction site images are being developed and promise to remove the heavy burden of manually indexing images. In this paper, both the existing methods and a novel image retrieval method developed by the authors for the classification and retrieval of construction site images are described and compared. Specifically a number of examples are deployed in order to present their advantages and limitations. The results from this comparison demonstrates that the content based image retrieval method developed by the authors can reduce the overall time spent for the classification and retrieval of construction images while providing the user with the flexibility to retrieve images according different classification schemes.

Defects Detection and Assessment in Concrete Surfaces

Aside from cracks, the impact of other surface defects, such as air pockets and discoloration, can be detrimental to the quality of concrete in terms of strength, appearance and durability. For this reason, local and national codes provide standards for quantifying the quality impact of these concrete surface defects and owners plan for regular visual inspections to monitor surface conditions. However, manual visual inspection of concrete surfaces is a qualitative (and subjective) process with often unreliable results due to its reliance on inspectors’ own criteria and experience. Also, it is labor intensive and time-consuming. This paper presents a novel, automated concrete surface defects detection and assessment approach that addresses these issues by automatically quantifying the extent of surface deterioration. According to this approach, images of the surface shot from a certain angle/distance can be used to automatically detect the number and size of surface air pockets, and the degree of surface discoloration. The proposed method uses histogram equalization and filtering to extract such defects and identify their properties (e.g. size, shape, location). These properties are used to quantify the degree of impact on the concrete surface quality and provide a numerical tool to help inspectors accurately evaluate concrete surfaces. The method has been implemented in C++ and results that validate its performance are presented.

Automated Detection of Exposed Reinforcement in Post-Earthquake Safety and Structural Evaluations

Post-earthquake structural safety evaluations are currently performed manually by a team of certified inspectors and/or structural engineers. This process is time-consuming and costly, keeping owners and occupants from returning to their businesses and homes. Automating these evaluations would enable faster, and potentially more consistent, relief and response processes. In order to do this, the detection of exposed reinforcing steel is of utmost significance. This paper presents a novel method of detecting exposed reinforcement in concrete columns for the purpose of advancing practices of structural and safety evaluation of buildings after earthquakes. Under this method, the binary image of the reinforcing area is first isolated using a state-of-the-art adaptive thresholding technique. Next, the ribbed regions of the reinforcement are detected by way of binary template matching. Finally, vertical and horizontal profiling are applied to the processed image in order to filter out any superfluous pixels and take into consideration the size of reinforcement bars in relation to that of the structural element within which they reside. The final result is the combined binary image disclosing only the regions containing rebar overlaid on top of the original image. The method is tested on a set of images from the January 2010 earthquake in Haiti. Preliminary test results convey that most exposed reinforcement could be properly detected in images of moderately-to-severely damaged concrete columns.

Material-Based Construction Site Image Retrieval

The technological advancements in digital imaging, the widespread popularity of digital cameras, and the increasing demand by owners and contractors for detailed and complete site photograph logs have triggered an ever-increasing growth in the rate of construction image data collection, with thousands of images being stored for each project. However, the sheer volume of images and the difficulties in accurately and manually indexing them have generated a pressing need for methods that can index and retrieve images with minimal or no user intervention. This paper reports recent developments from research efforts in the indexing and retrieval of construction site images in architecture, engineering, construction, and facilities management image database systems. The limitations and benefits of the existing methodologies will be presented, as well as an explanation of the reasons for the development of a novel image retrieval approach that not only can recognize construction materials within the image content in order to index images, but also can be compatible with existing retrieval methods, enabling enhanced results.

Comprehensive Property Retrieval and Measurement of Concrete Spalling Using Machine Vision for Post-Earthquake Safety Assessments

Active vibration control (AVC) is a relatively new technology for the mitigation of annoying human-induced vibrations in floors. However, recent technological developments have demonstrated its great potential application in this field. Despite this, when a floor is found to have problematic floor vibrations after construction the unfamiliar technology of AVC is usually avoided in favour of more common techniques, such as Tuned Mass Dampers (TMDs) which have a proven track record of successful application, particularly for footbridges and staircases. This study aims to investigate the advantages and disadvantages that AVC has, when compared with TMDs, for the application of mitigation of pedestrian-induced floor vibrations in offices. Simulations are performed using the results from a finite element model of a typical office layout that has a high vibration response level. The vibration problems on this floor are then alleviated through the use of both AVC and TMDs and the results of each mitigation configuration compared. The results of this study will enable a more informed decision to be made by building owners and structural engineers regarding suitable technologies for reducing floor vibrations.

Cement bentonite cutoff walls for polluted sites

Cement-bentonite (CB) cutoff walls have long been used to control ground water flow and contaminant migration at polluted sites. Hydraulic conductivity and unconfined compressive strength are two short-term properties often used by industry and owners in CB specification and are important parameters discussed in this paper. For polluted sites, long-term compatibility is also an important issue. These properties are coupled to a number of external factors including the mix design, construction sequence, presence/absence of contaminants at the site. Additional short-term properties for engineering assessment include the stressstrain characteristics in both drained and undrained shear in both with and without confinement as well as one-dimensional consolidation properties. Long term CB properties are affected by aging, reaction chemistry, drying, in situ stress state, and interaction with the polluted environment. © 2013 Taylor & Francis Group.