Due to the enormous volume of unstructured information available on the Web and inside organization, finding an answer to the knowledge need in a short time is difficult. For this reason, beside Search Engines which don’t consider users individual characteristics, Recommender systems were created which use user’s previous activities and other individual characteristics to help users find needed knowledge. Recommender systems usage is increasing every day. Expert finder systems also by introducing expert people instead of recommending information to users have provided this facility for users to ask their questions form experts. Having relation with experts not only causes information transition, but also with transferring experiences and inception causes knowledge transition. In this paper we used university professors academic resume as expert people profile and then proposed a new expert finding model that recommends experts to users query. We used Term Correlation Matrix, Vector Space Model and PageRank algorithm and proposed a new hybrid model which outperforms conventional methods. This model can be used in internet environment, organizations and universities that experts have resume dataset.

The analysis of cross-Correlations is extensively applied for understanding of interconnections in stock markets. Variety of methods are used in order to search stock cross- Correlations including the Random Matrix Theory (RMT), the Principal Component Analysis (PCA) and the Hierachical Structures. In this work, we analyze cross-crrelations between price uctuations of 20 company stocks of Iran by using RMT. We find the eigenvalues and eigenvectors of the matrices of the cross-Correlations related to these stocks. The results show some eigenvalues do not fall within the bounds of RMT eigenvalues, that indicate the Correlations of stocks in usual and critical ucatutions.

The aim of the present study was the histological evaluation of Enamel Matrix Derivative (EMD) effectiveness for regeneration of periodontal defects. EMD activates cementum synthesis, PDL and bone during the maturation stage of follicole. In this research, EMD was used in surgical defects of premolar teeth in four adult sheep. Muccoperiosteal flap was reflected in buccal site of teeth. The buccal bone plate was removed from mesial to distal in 4 mm depth. After eliminating the cementum by bur and its etching, EMD was applied on exposed dentine and flap was sutured. In opposite sites of those teeth (control sites) the same process was performed without etching. After 100 days, sheep were sacrificed and histological study through light microscopic was performed on black sections of operation sites. The results showed that in test sites, regeneration of cementum and bone was 62/5% and 42/5-50% respectively. But in control sites regeneration of cementum and bone was 37.5% and 32/5-42/5% respectively. Also the migration of junctional epithelium in control sites was 8-10% more than test sites. The important point is that in test sites, cementum was completely attached to undermining dentine. But, in control sites, the gap between cementum and dentine was visible. As a result, this study suggests that EMD promotes periodontal regeneration, and EMD application is a successful achievement in regenerative periodontal therapy.

One of the fundamental methods used in collaborative filtering systems is Correlation based on K-nearest neighborhood. These systems rely on historical rating data and preferences of users and items in order to propose appropriate recommendations for active users. These systems do not often have a complete Matrix of input data. This challenge leads to a decrease in the accuracy level of recommendations for new users. The exact Matrix completion technique tries to predict unknown values in data matrices. This study is to show how the exact Matrix completion can be used as a preprocessing step to tackle the sparseness problem. Compared to application of the sparse data Matrix, selection of neighborhood set for active user based on the completed data Matrix leads to achieving more similar users. The main advantages of the proposed method are higher prediction accuracy and an explicit model representation. The experiments show significant improvement in prediction accuracy in comparison with other substantial methods.

Different ossificant materials have been used used for induction of bone repair in many studies, and bone Matrix gelatin which contains bone morphogenic proteins one of the best ones.In present study we evaluated the role of this mat erial in acceleration of bone repair in rabbit tibia.A hole of 305mm diameter was made on right tibia of 10 and 12 rabbits as study and control group respectively, In the experiment group, in addittion to Bone Wax, we applied bone Matrix glatin in the hole. Radiographic images were taken in days 0, 20, 40 and 53 after operation In 6 rabbits of each group, photographic pictures were also taken after exposure of entire bone. In 6 controls less degree of restoration were seen on day 53. In 4 experimental animals restoration were completed by this time and in 2 speciments repair processing were better then controls. This results shows that bone Matrix gelatin can be used as an acclerator of bone repair.