Purpose: In order to enhance information retrieval performance, new approaches have been developed. One of the most crucial approaches is the existing documents Term Weighting in information databases. The mentioned approach is used for ranking the documents. The purpose of this research was to deTermine the role of Weighting in the improvement of retrieval systems.Methodology: the research method was documentary research (library research).Findings: the complicated Weighting methods have better results in information retrieval, compared to the simple method that are based on the presence of the Terms in the text. So in Term Weighting, researchers are seeking methods with higher efficiency that practically improve document retrieval. Conclusion: Term Weighting improves the performance of information retrieval systems. Though it is not the only way for improving their performance and it is affected by many other factors.

Sentiment analysis automatically classifies the opinions, which are expressed in a document, usually as positive or negative. A review document in general, reflects its author’s opinion about the objects mentioned in the text. Therefore, it can have many useful applications such as opinionated web search and automatic analysis of reviews. Although sentiment analysis is a kind of text classification problem, structures of review documents are different from texts like news, articles, or web pages; so that techniques applied for text classification are needed to be re-experimented for the sentiment analysis. Assigning appropriate weights to features is important to the performance of sentiment analysis so that important features can receive higher weights for the feature vectors. Feature selection reduces feature vector size by eliminating redundant or irrelevant features to improve classification accuracy. In this study, our aim is to examine the effects of Term Weighting methods on newly proposed Query Expansion Ranking (QER) feature selection method and also compare the classification results with one of the well-known feature selection method namely Chi-square statistic. We use three popular Term Weighting methods (i.e., Term presence, Term frequency, Term frequency and inverse document frequency- tf*idf) and perform experiments using multinomial Naïve Bayes classifier. The experimental results show that when QER feature selection method is used with tf*idf Term Weighting method, the classification performance improves in Terms of F-score.

Introduction: Juvenile Myoclonic Epilepsy (JME) is a generalized epileptic syndrome. Age of onset is usually between 12 to 18 years. JME consists of myoclonic jerks, generalized tonic-clonic seizures (GTCs) and typical absence attacks. EEG shows characteristic changes in JME. Long Term video-electroencephalography monitoring (VEM) is a helpful diagnostic procedure in the diagnosis of patient with unclear history or EEG findings. In the current study, we aimed to evaluate the role of VEM in diagnosis of refractory epileptic patients.Materials and Methods: This study is retrospective and descriptive on patients of Epilepsy Monitoring Unit of Razavi Hospital, Mashhad, Iran between March 2011 and March 2012. Telephone interview was scheduled 6-18 months after discharge to evaluate results of VEM on the frequency of seizures, the therapeutic regimes and patients’ quality of life.Results: 24 cases with diagnosis of JME were chosen among 250 patients who were admitted with refractory epilepsy. Fourteen of them were female. The average age of patients was 24 years old and the average duration of the seizure attacks was 12.97 years. The mean frequency of GTCs was 2.76 attacks per month and after VEM and proper treatment, it decreased to 0.27 attacks per month.Conclusion: VEM is a helpful diagnostic procedure for evaluating of refractory JME epileptic patients.

Let G = (V (G), E(G)) be a simple, finite and undirected graph of order n. A k-vertex Weighting of a graph G is a mapping w: V (G) → {1, . . ., k}. A k-vertex Weighting induces an edge labeling fw: E(G) → N such that fw(uv) = w(u) + w(v). Such a labeling is called an edge-coloring k-vertex Weighting if fw(e) ̸ = fw(e′ ) for any two adjacent edges e and e′ . Denote by μ ′ (G) the minimum k for G to admit an edge-coloring k-vertex Weighting. In this paper, we deTermine μ ′ (G) for some classes of graphs.

One of the important issues in the production of concrete is environmental variability that affects the concrete and elements constructed from this material. Due to the importance of different characteristics of concrete in short- and long-Term conditions, this article aims to investigate the influence of initial temperature conditions of self-compacting concrete on its long-Term characteristics and deTermine the initial optimal temperature of concrete. For these purposes, several experiments under three temperature conditions of 5, 20, and 40 degree-of-Celsius were conducted to evaluate the rheology and mechanical properties of fresh concrete in the range of 7 and 28 days. In these experiments, alternative mineral admixtures of cement such as micro-silica, fly ash, and zeolite were used to build the concrete specimens. Results demonstrate that the mechanical properties of the specimens increase with increasing initial temperature in the short Term, while such properties significantly decrease in the long Term compared to the specimens constructed under lower initial temperature. Moreover, the fluidity of self-compacting concrete increases with increasing initial temperature.