A biomechanical comparison of two cephalomedullary nails; one using a single lag screw with antirotator blade and a nail using two lag screws for unstable intertrochanteric fractures

Background: Implant choice for fixation of intertrochanteric fractures remains controversial despite being one of the most commonly performed operations. Although use of sliding hip screws is still considered a gold standard in treatment of these fractures, there is a wide tendency in using cephalomedullary nails because of their biomechanical superiority over sliding hip screws. This trial was initiated in order to compare the biomechanical properties of two different cephalomedullary nails, aPFN and the PROFIN under axial loading, based on the questions that can a single lag screw with an antirotator blade render better rotational stability? Is there a difference between one lag screw or two lag screws with respect to superior migration or cut-out of the screws? And do different nail designs cause different types of failure and what are the pros and cons of classical and new designs from the view point of biomechanical aspects?Methods: Ten pairs of third generation synthetic bone models simulating unstable intertrochanteric fracture were used for biomechanical testing.Results: No posterior displacement of screws was recorded in both groups suggesting rotational unstability.  There was not a significant difference between forces values loaded at the time of failure.Conclusions: Although there was no statistically significant difference between compressive strengths at the time of failure, aPFN may provide equal rigid fixation with less possible cut-out which may have an important consequences in real clinical applications

A Spatial Investigation of Wolf-Human Conflict in Turkey Based on MaxEnt Model

Large carnivores around the world are under increasing threat due to the human-induced factors according to the recent IUCN report. Through their high dispersal capability, large carnivores generally occupy extensive range of areas and dominate different type of habitats. On the other hand, decreasing habitat quality and available prey density compel this group of animals to inhabit semi-natural habitats and this phenomenon gives rise to human-carnivore conflict to occur. Being one of the most widespread large carnivores of the world, wolves normally prefer natural habitats and hesitate from human settlements similar to the other animals who belong to this group. However, wolves have become one of the iconic species of human-carnivore conflict in the regions where they exist. Co-existence of gray wolf and human in a region triggers the conflict via direct attacks on humans, or damage on livestock and shepherd dogs. In Turkey, despite the direct attacks on individuals are very rare, human perception of wolves is generally negative due to the economic cost of wolf-related livestock depredation. This situation is the main struggle for conservation efforts which are directed towards the gray wolves in Turkey. In this study, in order to evaluate the wolf-human conflict, which is one of the top priority issues to be resolved for sustainable management and to take protective measures for the wolf populations, the possible triggering parameters and conflict records have been examined. In this context, we have constructed a MaxEnt model using 12 parameters as predictive variables and n=309 locations of wolf attacks gathered from whole Turkey as conflict records. As a result of the study we established a risk map for Turkey and it has been revealed which factors triggered the conflict at the countrywide scale. The  obtained output showed that the wolf-human conflict in Eastern Anatolia has reached high risk values and the risk of conflict depends largely on altitude, land use pattern and road network density whereas the livestock density were contributed model output at almost insignificant level surprisingly. It is envisaged that the outputs of this study will contribute to the implementations carried out on the gray wolf population within the scope of species conservation and action plans conducted to manage wolf-human conflict.peerReviewe

Semantic Information Retrieval on Peer-to-Peer Networks

For the last few years peer-to-peer (p2p) networks have become widely used tools for sharing any kind of information from multimedia data to text documents. The vast amount of shared information leads issues on finding relevant information over p2p networks. Existing p2p file search and information retrieval techniques are based on the name of files, which is insufficient when searching relevant documents. In this paper we present a method to perform semantic information retrieval over p2p networks. Our method semantically inspects the content of shared data in peers to generate conceptual information about documents and general information about the peer

Anomaly Based Target Detection in Hyperspectral Images via Graph Cuts

The studies on hyperspectral target detection until now, has been treated in two approaches. Anomaly detection can be considered as the first approach, which analyses the hyperspectral image with respect to the difference between target and the rest of the hyperspectral image. The second approach compares the previously obtained spectral signature of the target with the pixels of the hyperspectral image in order to localize the target. A distinctive disadvantage of the aforementioned approaches is to treat each pixel of the hyperspectral image individually, without considering the neighbourhood relations between the pixels. In this paper, we propose a target detection algorithm which combines the anomaly detection and signature based hyperspectral target detection approaches in a graph based framework by utilizing the neighbourhood relations between the pixels. Assuming that the target signature is available and the target sizes are in the range of anomaly sizes, a novel derivative based matched filter is first proposed to model the foreground. Second, a new anomaly detection method which models the background as a Gaussian mixture is developed. The developed model estimates the optimal number of components forming the Gaussian mixture by means of utilizing sparsity information. Finally, the similarity of the neighbouring hyperspectral pixels is measured with the spectral angle mapper. The overall proposed graph based method has successfully combined the foreground, background and neighbouring information and improved the detection performance by locating the target as a whole object free from noises