Background and Aim: Discoloration is among the most common problems of composite restorations. Color change over time compromises the main advantage of composite resins namely their high esthetics. In such cases, the restoration needs to be replaced. .The aim of this in-vitro study was to evaluate the effect of accelerated Artificial aging (AAA) on the color stability of three composite resins (Filtek Z250, Filtek Z250XT, and Filtek Supreme). Materials and Methods: In this experimental study, 7 composite specimens with equal dimensions were fabricated of each composite resin. The initial color of specimens was measured using a spectroradiometer according to the CIE L*a*b* system. The specimens were then submitted to AAA for 384h and underwent color assessment again. Before and after aging, the surface roughness of one specimen from each group was determined by Atomic Force Microscopy (AFM). The obtained color parameters were analyzed by one-way ANOVA and Tukey’s test. Results: The color change of Filtek Z250 was significantly lower than that of Filtek Z250XT and Filtek Supreme (P≤0.05). No statistically significant differences were found between Z250XT and Supreme in this respect (P>0.05 ). Conclusion: All composite resins showed color change above the clinically acceptable threshold. Z250 microhybrid composite was more color stable than nano-composites (Z250XT and Supreme). AAA increased the surface roughness in all groups but it was within the clinically acceptable range.

In this article, the pricing of option contracts is discussed using the Mikhailov and Nogel model and the Artificial neural network method. The purpose of this research is to investigate and compare the performance of various types of activator functions available in Artificial neural networks for the pricing of option contracts. The Mikhailov and Nogel model is the same model that is dependent on time. In the design of the Artificial neural network required for this research, the parameters of the Mikhailov and Nogel model have been used as network inputs, as well as 700 data from the daily price of stock options available in the Tehran Stock Exchange market (in 2021) as the net-work output. The first 600 data are considered for learning and the remaining data for comparison and conclusion. At first, the pricing is done with 4 commonly used activator functions, and then the results of each are com-pared with the real prices of the Tehran Stock Exchange to determine which item provides a more accurate forecast. The results obtained from this re-search show that among the activator functions available in this research, the ReLU activator function performs better than other activator functions.

This study was an attempt to evaluate the progress of capital market efficiency in Iran. Optimal resource allocation and micro and macro investments play a key role in the capital market. The capital market's main task is to circulate capital and allocate resources efficiently and optimally. The main task of this market is to flow capital and allocate resources efficiently and optimally. Is there a regular pattern for determining the stock price? market efficiency gains significance as it is important to know what factor or factors are effective in determining the price of the stock in the stock market or whether there is a regular pattern for determining the price of a stock. Thus, this study examined the efficiency of the capital market in Iran. In this regard, the researchers used the daily data of the total index of the Tehran Stock Exchange for 2008-2017. Artificial neural network and time series training tests were used to perform the test. The test results showed weak efficiency in the Tehran Stock Exchange and this inefficiency did not change significantly compared to the first period. In other words, in the Tehran Stock market, one can predict returns using Artificial intelligence.

market segmentation by Artificial neural networks has no deep root in the history. Generally, this ever developing approach has started since several years ago, and developed to other marketing areas. Now, beside statistical techniques, it is considered as one of the most popular methods in Custamer classification.In Due to-the-necessity of recognizing the target market for a specific company, a need for the usage of an effective approach for customers grouping was recognized, in the Present research, and finally cluster analysis with SOM neural networks, was selected, and used for customers clustering.Firstly, beneficent criteria for market segmentation were identified, and then a proper, questionnaire was designed. After gathering the questionnaires and collecting the data, using Artificial neural networks, the customers were clustered, and the obtained, results were analyzed. At the end, the Findings of this method were compared with those of the traditional methods for clustering using K-means.

Congestion management is one of the major tasks performed by system operators in deregulated environment to ensure the secure operation of transmission system. Congestion should be alleviated as fast as possible since it may lead to tripping of overloaded lines, consequential tripping of other lines, and in some cases to voltage stability problem. This paper proposes an intelligent technique based on neural network for on line congestion management in a pool based electricity market. The control action strategies to limit line loading to the security limits are by means of minimal adjustments in generations from the initial market clearing values. The training data are generated by solving the proposed congestion management problem for wide range of real and reactive power of loads with critical line outage using Differential Evolution (DE) as an optimization tool. The effectiveness of the proposed method is tested under different loading conditions with contingency in IEEE 30 Bus system. Test results validate the potential of the proposed method.

Stock market plays an important role in the world economy. Stock market customers are interested in predicting the stock market general index price, since their income depends on this financial factor; Therefore, a reliable forecast in stock market can be extremely profitable for stockholders. Stock market prediction for financial markets has been one of the main challenges in forecasting financial time series, in recent decades. This challenge has increasingly attracted researchers from different scientific branches such as computer science, statistics, mathematics, and etc. Despite a good deal of research in this area, the achieved success is far from ideal. Due to the intrinsic complexity of financial data in stock market, designing a practical model for this prediction is a difficult task. This difficulty increases when a wide variety of financial factors affect the stock market index. In this paper, we attempt to investigate this problem and propose an effective model to solve this challenge. Tehran’ s stock market has been chosen as a real-world case study for this purpose. Concretely, we train a regression model by several features such as first and second market index in the last five years, as well as other influential features including US dollar price, universal gold price, petroleum price, industry index and floating currency index. Then, we use the trained system to predict the stock market index value of the following day. The proposed approach can be used by stockbrokers-trading companies that buy and sell shares for their clients to predict the stock market value. In the proposed method, intelligent nonlinear systems such as Artificial Neural Networks (ANNs) and Adaptive Network-based Fuzzy Inference System (ANFIS) have been exploited to predict the daily stock market value of Tehran’ s stock market. At the end, the performance of these models have been measured and compared with the linear classical models, namely, ARIMA and SARIMA. In the comparison phase, these time series data are imposed to non-linear ANN and ANFIS models; then, feature selection is applied on data to extract the more influencing features, by using mutual information (MI) and correlation coefficient (CC) criteria. As a result, those features with greater impact on prediction are selected to predict the stock market value. This task eliminates irrelevant data and minimizes the error rate. Finally, all models are compared with each other based on common evaluation criteria to provide a big picture of the exploited models. The obtained results approve that the feature selection by MI and CC methods in both ANFIS and ANN models increases the accuracy of stock market prediction up to 55 percentage points. Furthermore, ANFIS could outperform ANN in all five evaluation criteria.

Stock market timing is a very difficult task because of the complexity of the market. Since there are various factors affecting the market and therefore it is not a simple task to predict future stock price and its trend. This paper aims to apply advanced tools and algorithms such as the Artificial neural networks (ANN) to model nonlinear processes and predict future stock price and its trend. More specifically, this study explores the abilities of the ANN to enhance the effectiveness of the technical analysis indicators to predict stock trend signals. Using a sample of 50 companies in the Tehran Stock Exchange (TSE), the results indicate that the ANN is capable to predict the direction of the short term movement in the future stock price. After considering the transaction costs, the results confirm that there is not significant difference among the returns gained from the ANN method, buy and hold strategy, and the most profitable technical indicators in the market when the trend is increasing. While, the ANN model yields higher returns compared to buy and hold strategy in the market when the trend is decreasing. Nevertheless, in the case of decreasing trend, the finding confirms the trend indicators (moving averages) achieve the highest returns.

Introduction: Discoloration is one of the most common reasons for replacement of resin composites. The purpose of this study was to compare the color stability of three Methacrylate-based Resin Composites (Filtek Z250, Filtek Z250x, Filtek Z350xt) with Silorane-based Resin Composites (Filtek P90) after Accelerated Artificial Aging (AAA).Materials & Methods: In this in vitro study 56 composite discs were prepared (N=14). CIE L*a*b* parameters of each specimen were measured by a reflectance spectrophotometer after 24h and 384 h of AAA. Then Color change (DE) of each composite was calculated. Data were analyzed by one way ANOVA, Tukey and paired t-test at the significance level of 0.05.Results: DE values of Filtek Z250, Filtek Z250x, Filtek Z350xt and Filtek P90 were 7.77, 5.86, 8.95 and 8, respectively. One way ANOVA demonstrated a significant difference between DE values of composites (P˂0.001). Tukey’s test revealed that DE value of Filtek Z250xt was significantly lower than those of other composites (P˂0.05).Conclusion: Silorane and methacrylate based composites showed a color change more than the clinically acceptable level (DE˃3.3) after AAA. Filtek Z250xt showed the lowest color change and other composites showed relatively similar color change.