Economic impacts of globalisation at the district, landing site and sub-sector enterprise levels on Lakes Kyoga, Albert and Victoria

The Globalisation and fish utilisation and marketing study is a collaboration between the Fisheries Resources Research Institute (FIRRI) and the Mike Dillon Associates Limited , with funding from the Department for International Development (DFID) of the Government of the United Kingdom. The study is designed to examine the impact of the development of the export fishery on the fish producers, processors, traders and consumers in the artisanal fishery in Uganda. FIRRI 's role is to collect field data relating to the livelihoods of artisanal fish producers, processors, traders and consumers. in particular data relating to income and revenue flow. The initial focus is on the eccnomic structure of fish landing sites. The purpose of this paper is to review the progress in implementation of the project and present the interim findings for discussion. During the first quarter, namely April to June, 2002, work was carried out on Lakes Kyoga and Albert and a report produced. During the second quarter, July to September, 2002, Lake Victoria was covered. In both phases, the focus has been on the economic structure of fish landings.

Report of the baseline survey undertaken on the Chinese cage site in Napoleon Gulf, Northern Lake Victoria, 12 March 2012

The government of the People's Republic of China through a 2007 agreement with the Government of the Republic of Uganda, has establishment of an Agricultural Technology Demonstration Center (ATDC). The first phase covering the building of aquaculture infrastructure at Kajjansi ARDC is complete and the second operation phase has started in which facilities for cage culture have been set up in the Napoleon gulf, northern Lake Victoria near Jinja. The cage facility is aimed at boosting fish farming within the lake as a diversification to the traditional pond fish culture technology. NaFIRRI scientists as well as Chinese experts undertook a baseline survey in the chosen cage site on 12 March 2012. The survey covered determination of water depth, water transparency, measurement of selected physical-chemical parameters (temperature,dissolved oxygen, conductivity and pH; determination of the nutrient status and study of algae, invertebrate and fish communities at the site. Materials and methodologies used in the survey were based on the Standard Operating Procedures (SOPs) of NaFIRRI. The study area was divided into three study sites. Site 1 (upstream) was at 8.9 metre depth while site 2 (proposed cage site) and site 3 (downstream) were 6 and 4.3 metres deep respectively. Water transparency was lowest at site 1 (1.58 m) and highest at site 3 (1.64 m). Dissolved oxygen at the three sites ranged from 6.0 to 8 mg/I. Water temperature profiles fluctuated within narrow limits between 26.5 and 27.5 DC. Measurements of pH were between 7 (neutral) and 8 (alkaline) while electrical conductivity was between 98 and 101 uS/em. These observed physical-chemical parameters at the study site were considered suitable for cage fish rearing purposes. Nitrite-nitrogen levels varied within narrow limits from 0.043 to 0.0453 mgtl. Similarly, Ammonia-nitrogen varied between 0.015 and 0.0185 mg/1. Soluble reactive phosphorus (SRP) level was highest at site 3 (O.012mgll) compared to that at sites 1 and 2 (0.009mgll). Total suspended solids (TSS) were higher at site 1 (83.3mgll), thereafter decreasing to lower levels at sites 2 (24.8mgtl) and 3 (19.8mgl) respectively. The nutrient level results observed here all fall below the maximum permissible limits by NEMA and therefore the site is recommended for cage culture The algal community was constituted by four major groups: Blue greens,Greens, Cryptophytes, and Diatoms with blue greens as the common and dominant group. High algal biomass (19944961 ugtL) of the dominant blue green algae was observed at site 1 compared site 2 and 3 (58655.2 & 27487. 7 ugtL) respectively. Occurrence of toxicin producing algae: microsytis and cylindrospermopsis in the proposed cage area was considered to be of not much significance as their concentrations were below harmful levels. However, monitoring their presence, biomass and seasonality will be critical in order to follow when and where they occur and at what time of the year for ease of management of the cages

Monthly patterns in physico-chemical conditions and fish catch in the immediate vicinity of the Bujagali hydropower construction site 29th September - 28th October 2007

The specific objectives were to: WATER QUALITY 1. To measure the water physical variables as indicators of environmental conditions in the upstream and downstream transects of Kalange (1) and Buyala (2), respectively, 2. To determine the concentrations of total suspended solids as a major constituent likely to be released into the waters at any time during the construction activities, by comparing the concentrations at the two transects. FISH CATCH 1. To follow up trends in fish catch as construction activity progresses, and to precision of the estimate; 2. To estimate the prevailing fish catch rates, total fish catches and the total value of the fish catch to the local fishers at the two transects.

Molecular docking and 3D-QSAR study of pyranmycin derivatives against 16S rRNA A site

To understand pharmacophore properties of pyranmycin derivatives and to design novel inhibitors of 16S rRNA A site, comparative molecular field analysis (CoMFA) approach was applied to analyze three-dimensional quantitative structure-activity relationship (3D-QSAR) of 17 compounds. AutoDock 3.0.5 program was employed to locate the orientations and conformations of the inhibitors interacting with 16S rRNA A site. The interaction mode was demonstrated in the aspects of inhibitor conformation, hydrogen bonding and electrostatic interaction. Similar binding conformations of these inhibitors and good correlations between the calculated binding free energies and experimental biological activities suggest that the binding conformations of these inhibitors derived from docking procedure were reasonable. Robust and predictive 3D-QSAR model was obtained by CoMFA with q(2) values of 0.723 and 0.993 for cross-validated and noncross-validated, respectively. The 3D-QSAR model built here will provide clear guidelines for novel inhibitors design based on the Pyranmycin derivatives against 16S rRNA A site. (c) 2005 Elsevier B.V. All rights reserved.

System-of-system Approaches and Challenges for Multi-Site Manufacturing

In the multi-site manufacturing domain, systems-of-systems (SoS) are rarely called so. However, there exist a number of collaborative manufacturing paradigms which closely relate to system-of-system principles. These include distributed manufacturing, dispersed network manufacturing, virtual enterprises and cloud manufacturing/manufacturing-as-a-service. This paper provides an overview of these terms and paradigms, exploring their characteristics, overlaps and differences. These manufacturing paradigms are then considered in relation to five key system-of-systems characteristics: autonomy, belonging, connectivity, diversity and emergence. Data collected from two surveys of academic and industry experts is presented and discussed, with key challenges and barriers to multi-site manufacturing SoS identified.

Comparative study of vision tracking methods for tracking of construction site resources

Vision tracking has significant potential for tracking resources on large scale, congested construction sites, where a small number of cameras strategically placed around the site could replace hundreds of tracking tags. The correlation of vision tracking 2D positions from multiple views can provide the 3D position. However, there are many 2D vision trackers available in the literature, and little information is available on which one is most effective for construction applications. In this paper, a comparative study of various vision tracker categories is carried out, to identify which one is most effective in tracking construction resources. Testing parameters for evaluating categories of trackers are identified, and benefits and limitations of each category are presented. The most promising trackers are tested using a database of construction operations videos. The results indicate the effectiveness of each tracker in relation to each parameter of the test, and the most suitable tracker needed to research effective 3D vision trackers of construction resources.

Automated Computation of the Fundamental Matrix for Vision-Based Construction Site Applications

Estimating the fundamental matrix (F), to determine the epipolar geometry between a pair of images or video frames, is a basic step for a wide variety of vision-based functions used in construction operations, such as camera-pair calibration, automatic progress monitoring, and 3D reconstruction. Currently, robust methods (e.g., SIFT + normalized eight-point algorithm + RANSAC) are widely used in the construction community for this purpose. Although they can provide acceptable accuracy, the significant amount of required computational time impedes their adoption in real-time applications, especially video data analysis with many frames per second. Aiming to overcome this limitation, this paper presents and evaluates the accuracy of a solution to find F by combining the use of two speedy and consistent methods: SURF for the selection of a robust set of point correspondences and the normalized eight-point algorithm. This solution is tested extensively on construction site image pairs including changes in viewpoint, scale, illumination, rotation, and moving objects. The results demonstrate that this method can be used for real-time applications (5 image pairs per second with the resolution of 640 × 480) involving scenes of the built environment.

Construction site image retrieval based on material cluster recognition

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. This capability is a product of the technological breakthroughs in the area of image processing that has allowed for the development of a large number of digital imaging applications in all industries. In this paper, an automated and content based construction site image retrieval method is presented. This method is based on image retrieval techniques, and specifically those related with material and object identification and matches known material samples with material clusters within the image content. The results demonstrate the suitability of this method 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.

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.

Shape Recognition of Linear Construction Entities from Construction Site Images

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. This capability is a product of the technological breakthroughs in the area of Image Processing that has allowed for the development of a large number of digital imaging applications in all industries. In this paper, an automated and content based shape recognition model is presented. This model was devised to enhance the recognition capabilities of our existing material based image retrieval model. The shape recognition model is based on clustering techniques, and specifically those related with material and object segmentation. The model detects the borders of each previously detected material depicted in the image, examines its linearity (length/width ratio) and detects its orientation (horizontal/vertical). The results emonstrate the suitability of this model for construction site image retrieval purposes and reveal the capability of existing clustering technologies to accurately identify the shape of a wealth of materials from construction site images.

Automated On-Site Retrieval of Project Information

Among several others, the on-site inspection process is mainly concerned with finding the right design and specifications information needed to inspect each newly constructed segment or element. While inspecting steel erection, for example, inspectors need to locate the right drawings for each member and the corresponding specifications sections that describe the allowable deviations in placement among others. These information seeking tasks are highly monotonous, time consuming and often erroneous, due to the high similarity of drawings and constructed elements and the abundance of information involved which can confuse the inspector. To address this problem, this paper presents the first steps of research that is investigating the requirements of an automated computer vision-based approach to automatically identify “as-built” information and use it to retrieve “as-designed” project information for field construction, inspection, and maintenance tasks. Under this approach, a visual pattern recognition model was developed that aims to allow automatic identification of construction entities and materials visible in the camera’s field of view at a given time and location, and automatic retrieval of relevant design and specifications information.