Background and Objective: Due to the negative effects caused by the inappropriate use of land and land use changes, it is necessary to be aware about the variability process in the environmental impacts assessment witch arising from different developments to have the be More
Background and Objective: Due to the negative effects caused by the inappropriate use of land and land use changes, it is necessary to be aware about the variability process in the environmental impacts assessment witch arising from different developments to have the best planning and sustainable management of land. This study was conducted to check the effects of landscape changes in the city of Malayer. Malayer city due to various factors such as population growth, modernization of suburban neighborhoods and etc. is experiencing changes in land cover but among them the most effective factor is policies managers.
Method: For achieving the studies objectives first step was mapping land cover change analysis, Landsat satellite images were used in the period between 2000 and 2014 and metrics were: class area (CA), number of patches (NP), patch density (PD), largest patch index (LPI) and landscape shape index (LSI).
Findings: Findings like landscape metrics analysis shows wide replacement of open lands in the area with green and built lands. This means that from 2000 to 2014 green space is nearly doubled and open land also have decreased about 50%. Also the increase of number of patches was more than double that shows the microlithic landscape in the region.
Discussion and Conclusion: The final results show that the changes of green lands are promising and improving and behind of this improvement there is nothing except management focus on green space which should continue in the future for sustainable development.
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The purpose of this research is Monte Carlo Markov chain simulation under Bayesian inference to identify the parameters affecting earning quality measurement. In this regard, in order to predict the earning behavior of companies and to derive the exact parameters of the More
The purpose of this research is Monte Carlo Markov chain simulation under Bayesian inference to identify the parameters affecting earning quality measurement. In this regard, in order to predict the earning behavior of companies and to derive the exact parameters of the model from the Bayesian Markov Monte Carlo (MCMC) technique, which takes cross-sectional heterogeneity into account, an analysis was done by coding in Python. In this research, the earning signals extracted from the financial statements on a quarterly basis for a period of 5 years (2018-2022), for 104 companies admitted to the Tehran Stock Exchange, were collected and analyzed using a new measure of earning quality. Auxiliary variables of accounting comparability, financial leverage, operating cycle, and sales volatility were used to achieve more accurate results, and several statistical performance measures (R2, RMSE, and MSE) were used to evaluate the effectiveness of Bayesian-based forecasting models. The results showed that the proposed criterion of the present study derived from the Bayesian model for training and testing data is well able to predict the quality of earning. The evidence shows that the results of the proposed model are superior to the conventional accrual earning management model, which suggests an error rate of MSE=0.0188 and RMSE=0.1369, respectively. The results of the present research can be used to analyze the portfolio and predict the quality of future earnings of companies using historical data. It can also be used to study factors affecting investment performance.
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Sand dunes are one of the most important facies of wind erosion. Our understanding of the complex interactions of sand dunes is often limited by the lack of accurate morphological data. The erosion and sedimentation process is very important and there is currently a lac More
Sand dunes are one of the most important facies of wind erosion. Our understanding of the complex interactions of sand dunes is often limited by the lack of accurate morphological data. The erosion and sedimentation process is very important and there is currently a lack of field data for executive projects, study plans and validation of erosion and sedimentation models. Images of the study area were taken using a Phantom 4 Pro UAV at an altitude of 60 meters on September 22, 2019. This type of UAV, which is small and light, with its 20-megapixel camera and GPS, can provide high quality images. After separating the dunes, a three-dimensional area in terms of square meters and volume in terms of cubic meters were obtained. And the product of a small amount of bulk density of soil in terms of cubic centimeters and the volume of dunes in terms of cubic meters The weight of the dunes was obtained in terms of tons.In the study, the slope percentage and sediment height were determined based on the windward part and the wind shelter of the dune The highest wind area was slope 10-20% And the maximum sediment is in the slope of 70-100% And in the wind shelter, the maximum area was on a slope of 30-50% And the highest sediment was determined in the slope of 70-100% .
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Selecting optimal features based on nature of the phenomenon and high discriminant ability is very important in the data classification problems. Since it doesn't require any assumption about stationary condition and size of the signal and the noise in Recurrent Quantif More
Selecting optimal features based on nature of the phenomenon and high discriminant ability is very important in the data classification problems. Since it doesn't require any assumption about stationary condition and size of the signal and the noise in Recurrent Quantification Analysis (RQA), it may be useful for epileptic seizure Detection. In this study, RQA was used to discriminate ictal EEG from the normal EEG where optimal features selected by combination of algorithm genetic and Bayesian Classifier. Recurrence plots of hundred samples in each two categories were obtained with five distance norms in this study: Euclidean, Maximum, Minimum, Normalized and Fixed Norm. In order to choose optimal threshold for each norm, ten threshold of ε was generated and then the best feature space was selected by genetic algorithm in combination with a bayesian classifier. The results shown that proposed method is capable of discriminating the ictal EEG from the normal EEG where for Minimum norm and 0.1˂ε˂1, accuracy was 100%. In addition, the sensitivity of proposed framework to the ε and the distance norm parameters was low. The optimal feature presented in this study is Trans which it was selected in most feature spaces with high accuracy.
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