Techniques to improve the shear strength of impacted bone graft: the effect of particle size and washing of the graft.

BACKGROUND: When fresh morselized graft is compacted, as in impaction bone-grafting for revision hip surgery, fat and marrow fluid is either exuded or trapped in the voids between particles. We hypothesized that the presence of incompressible fluid damps and resists compressive forces during impaction and prevents the graft particles from moving into a closer formation, thus reducing the graft strength. In addition, viscous fluid such as fat may act as an interparticle lubricant, thus reducing the interlocking of the particles. METHODS: We performed mechanical shear testing in the laboratory with use of fresh-frozen human femoral-head allografts that had been passed through different orthopaedic bone mills to produce graft of differing particle-size distributions (grading). RESULTS: After compaction of fresh graft, fat and marrow fluid continued to escape on application of normal loads. Washed graft, however, had little lubricating fluid and better contact between the particles, increasing the shear resistance. On mechanical testing, washed graft was significantly (p < 0.001) more resistant to shearing forces than fresh graft was. This feature was consistent for different bone mills that produced graft of different particle-size distributions and shear strengths. CONCLUSIONS: Removal of fat and marrow fluid from milled human allograft by washing the graft allows the production of stronger compacted graft that is more resistant to shear, which is the usual mode of failure. Further research into the optimum grading of particle sizes from bone mills is required.

Techniques to improve the shear strength of impacted bone graft: The effect of particle size and washing of the graft

Background: When fresh morselized graft is compacted, as in impaction bone-grafting for revision hip surgery, fat and marrow fluid is either exuded or trapped in the voids between particles. We hypothesized that the presence of incompressible fluid damps and resists compressive forces during impaction and prevents the graft particles from moving into a closer formation, thus reducing the graft strength. In addition, viscous fluid such as fat may act as an interparticle lubricant, thus reducing the interlocking of the particles. Methods: We performed mechanical shear testing in the laboratory with use of fresh-frozen human femoral-head allografts that had been passed through different orthopaedic bone mills to produce graft of differing particle-size distributions (grading). Results: After compaction of fresh graft, fat and marrow fluid continued to escape on application of normal loads. Washed graft, however, had little lubricating fluid and better contact between the particles, increasing the shear resistance. On mechanical testing, washed graft was significantly (p < 0.001) more resistant to shearing forces than fresh graft was. This feature was consistent for different bone mills that produced graft of different particle-size distributions and shear strengths. Conclusions: Removal of fat and marrow fluid from milled human allograft by washing the graft allows the production of stronger compacted graft that is more resistant to shear, which is the usual mode of failure. Further research into the optimum grading of particle sizes from bone mills is required. Clinical Relevance: Understanding the mechanical properties of milled human allograft is important when impaction grafting is used for mechanical support. A simple means of improving the mechanical strength of graft produced by currently available bone mills, including an intraoperative washing technique, is described.

Compression techniques applied to multiple speech recognition systems

Speech recognition systems typically contain many Gaussian distributions, and hence a large number of parameters. This makes them both slow to decode speech, and large to store. Techniques have been proposed to decrease the number of parameters. One approach is to share parameters between multiple Gaussians, thus reducing the total number of parameters and allowing for shared likelihood calculation. Gaussian tying and subspace clustering are two related techniques which take this approach to system compression. These techniques can decrease the number of parameters with no noticeable drop in performance for single systems. However, multiple acoustic models are often used in real speech recognition systems. This paper considers the application of Gaussian tying and subspace compression to multiple systems. Results show that two speech recognition systems can be modelled using the same number of Gaussians as just one system, with little effect on individual system performance. Copyright © 2009 ISCA.

Comparison of Civil Infrastructure Optical-based Spatial Data Acquisition Techniques

Infrastructure spatial data, such as the orientation and the location of in place structures and these structures' boundaries and areas, play a very important role for many civil infrastructure development and rehabilitation applications, such as defect detection, site planning, on-site safety assistance and others. In order to acquire these data, a number of modern optical-based spatial data acquisition techniques can be used. These techniques are based on stereo vision, optics, time of flight, etc., and have distinct characteristics, benefits and limitations. The main purpose of this paper is to compare these infrastructure optical-based spatial data acquisition techniques based on civil infrastructure application requirements. In order to achieve this goal, the benefits and limitations of these techniques were identified. Subsequently, these techniques were compared according to applications' requirements, such as spatial accuracy, the automation of acquisition, the portability of devices and others. With the help of this comparison, unique characteristics of these techniques were identified so that practitioners will be able to select an appropriate technique for their own applications.

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.