In the next generation of heterogeneous wireless networks (HWNs), a large number of different radio access technologies (RATs) will be integrated into a common network. In this type of networks, selecting the most optimal and promising access network (AN) is an important consideration for overall networks stability, resource utilization, user satisfaction, and quality of service (QoS) provisioning. This paper proposes a general scheme to solve the access network selection (ANS) problem in the HWN. The proposed scheme has been used to present and design a general multicriteria software assistant (SA) that can consider the user, operator, and/or the QoS view points. Combined fuzzy logic (FL) and genetic algorithms (GAs) have been used to give the proposed scheme the required scalability, flexibility, and simplicity. The simulation results show that the proposed scheme and SA have better and more robust performance over the random-based selection.

Background Dental students are future dentists. Continuous assessment and improving of the educational curricula will ensure excellent academic performance of dental students and thus providing the community with the best treatment modalities. The aim of this study was to evaluate the root canal filling quality performed in extracted teeth by preclinical undergraduate Yemeni dental students. Methods Root canal treatment was performed by undergraduate preclinical dental students on 331 extracted human teeth including 741 roots. The teeth were then collected and evaluated radiographically based on three criteria of quality (length, density, and taper). Cohen’s Kappa test was used to assess the agreement between the examiners and Chi-squared test was used for the association between the study variables. The level of significant was set at α < 0.05. Results The results of the study revealed that the overall quality of roots canals fillings was poor. However, more than half of the study sample (53.4%) had adequate length, 13.1% had adequate density, and 14.2% had adequate taper. Anterior as well as single-rooted teeth had significantly better quality than posterior and multi-rooted teeth, respectively. The root canal fillings quality mandibular teeth was better than of maxillary teeth with no significant difference (P > 0.05). Conclusion The findings of the study emphasize the need of improving the endodontic course in the preclinical level and more advanced techniques and instruments should be incorporated.

Abstract-Image retrieval is still an active research topic in the computer vision field. There are existing several techniques to retrieve visual data from large databases. Bag-of-Visual Word (BoVW) is a visual feature descriptor that can be used successfully in Content-based Image Retrieval (CBIR) applications. In this paper, we present an image retrieval system that uses local feature descriptors and BoVW model to retrieve efficiently and accurately similar images from standard databases. The proposed system uses SIFT and SURF techniques as local descriptors to produce image signatures that are invariant to rotation and scale. As well as, it uses K-Means as a clustering algorithm to build visual vocabulary for the features descriptors that obtained of local descriptors techniques. To efficiently retrieve much more images relevant to the query, SVM algorithm is used. The performance of the proposed system is evaluated by calculating both precision and recall. The experimental results reveal that this system performs well on two different standard datasets.

The security in cognitive radio networks (CRNs) has been attracting continuously growing attention, due to the open and dynamic nature of cognitive radio architecture. In this

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