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This research is aimed to investigate and compare the effects of different feature selection methods on auditors' opinion type prediction. To do so, this research compares the performance of feature selection methods (including correlation-based, t-test, stepwise discriminate analysis, relief and factor analysis). Two classifiers used in this study are support vector machine and neural networks. The sample includes 1214 firms-years listed in the Tehran Stock Exchange in the period from 2008 to 2015. The results confirm the effectiveness of feature selection methods and significant differences among their performance. In other wordsc, using the feature selection methods increases the mean of accuracy and reduces type I and type II errors. Furthermore, the results indicate that support vector machine outperforms the neural networks.

Despite the importance of predictive variable in prediction, in most of the research in the field of auditors’ opinion the purpose was rendering the suitable models. Meanwhile, less attention was paid to the selection of optimal predictive variable and appropriate models of these selection. Therefore, in most of these research the predictive variables were chosen randomly and according to the prior research. The process of selecting variables could be used as a preprocess for omitting irrelevant variables and selecting optimal variables before creating the model. In this regard, this study investigates the usefulness of Correlation-Based Features Selection (CFS) in auditors’ opinion prediction of listed companies in Tehran Stock Exchange. The classifiers including Artificial Neural Networks (ANN) and logistic regression were used. In overall, the experimental results of investigating 1214 firms-years from 2008 to 2015, confirmed the usefulness of CFS Method in predicting auditors' opinion. In other words, the application of the CFS method, increases the mean of accuracy in comparison with using all variables, and reduces the occurrence of type I and type II errors. Furthermore, the results indicated that ANN outperforms the logistic regression.

Background: Noise is a known physical stress in workplace, which induces alterations of various physiological responses in exposed individuals. Aim of this study was to explore the status of oxidant and antioxidant parameters in blood and liver tissue of rats exposed to noise.Materials and Methods: Twenty-one adult albino wistar male rats seven in each of the following groups were used (N=7): 1- Control group, 2- Noise exposure (100 dB, 700-5700 Hz, 8 h/day, 8 days), 3- Noise exposure (100 dB,700-5700 Hz, 8 h/day, 14 days). The animals were anesthetized by CO2 and after decapitation, blood and liver samples were collected and processed for estimation of biochemical parameters (MDA and GSH levels) in control and exposed groups.Results: The present research findings showed significant decrease in liver weight and liver/body weight ratio in noise exposed groups compared to the control group (p

Bioaerosol concentration was measured in wastewater treatment units in south of Tehran, the largest wastewater treatment plant in the Middle East. Active sampling was carried out around four operational units and a point as background. The results showed that the aeration tank with an average of 1016 CFU/m(3) in winter and 1973 CFU/m(3) in summer had the greatest effect on emission of bacterial bioaerosols. In addition, primary treatment had the highest impact on fungal emission. Among the bacteria, Micrococcus spp. showed the widest emission in the winter, and Bacillus spp. was dominant in summer. Furthermore, fungi such as Penicillium spp. and Cladosporium spp. were the dominant types in the seasons. Overall, significant relationship was observed between meteorological parameters and the concentration of bacterial and fungal aerosols.

In this paper, we illustrate the design and simulation of photonic crystal (PhC) nano-pressure sensor with high sensitivity and resolution. The sensor is based on two-dimensional photonic crystal (2D-PhC) and has hexagonal lattice of air holes in Si. A waveguide is directly coupled to nanocavity and is configured by eliminating one line of air holes. The nanocavity is formed by modifying the radius of one air hole in the center of lattice. This sensor is designed to use in 1450–1550nm wavelength. Simulation results show that resonant wavelength of nanocavity shifts to longer wavelength with increasing the pressure. The sensitivity of sensor and minimum detectable pressure are respectively obtained as 11.7nm/GPa and 13nN which is improved compared to previous researches.

Tens of thousands of universities are today educating students with different methods for a vast variety of majors. This paper aims to introduce Knowledge Tree (KT) as an integrated virtual structure to be used in the educational systems. A collaborative structure is utilized to define a universal curriculum for various university majors worldwide to maximize e-learning capabilities. Then, a collaborative competitive approach is introduced to enrich the contents of course topics offered based on the mentioned structure. The results based on both educational facts fetched from the search engines and a questionnaire filled by a group of academics from different universities show that this system performs well in educational systems in terms of time, effort and cost.

In this article, a universal collaborative and competitive approach is introduced for deployment of digital collections in an ideal Digital Library (DL) for future's educational system. The collaborative and open-source aspects of the system guarantee its growth and the competitive aspects guarantee the accuracy.

The authors present a new design of high resolution and wide dynamic range photonic crystal pressure sensor. This sensor is based on two-dimensional photonic crystal with square array of silicon rods surrounded by air. The sensor consists of a photonic crystal waveguide which is coupled to a photonic crystal nanocavity. The waveguide is configured by removing one row of Si rods and nanocavity is formed by modifying the radius of one Si rod. The sensor is designed for 1300 nm–1400 nm wavelengths. Simulation results show that resonant wavelength of nanocavity is linearly shifted to larger wavelengths by increasing the pressure. The designed sensor has a linear behavior between 0.1 GPa to 10 GPa of applied pressure and 8 nm/GPa of pressure sensitivity.