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One of the main issues in financial management is choosing the best way of utilizing investment. Investors would like to invest their capitals in a way to minimize their risks. Bankruptcy is one of the risk factors which affect the decision of investors. Prediction of bankruptcy can help investors to reduce the risks in the capital markets and recognize the best opportunities for alternative investment. This study aims to predict the bankruptcy of companies by using the technique of Artificial Neural Network (ANN). Moreover, discriminant Analysis and logestic regression techniques are employed to compare the results. The data used in this study covers the firms in the Kerman Province of Iran over the period 1975- 2007. The results show that ANN model perfom much better than the discriminant analysis and logestic regression techniques. Moreover, the results confirm that the accuracy of ANN model is higher than the discriminant analysis and logestic regression techniques for predicting of bankruptcy. The analysis also shows that none of the firms will bankrupt in the year after the period covered in this study.

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Aim The aim of this study was to investigate the ecological factors affecting the distribution of the rare, endangered and medicinal species of Vaccinium arctostaphylos L. in ecotone rangelands of Namin County in 2019.
Materials & methods Sampling were performed from the presence and absence locations of V. arctostaphylos in eight habitats. Soil samples were taken from a depth of 0-30 cm and physiographic variables in were recorded. Moreover, a digital elevation model, slope and aspect map was derived. Rainfall and temperature gradient maps, was derived using gradient equations, and the values for sampling points were extracted. Data analysis performed by independent t-test and discriminant analysis test.
Findings Results showed the Stream Power Index (SPI) (p<0.05), pH, EC, lime, soluble sodium, organic carbon, soil texture and species density (p<0.01) are significantly different between the presence and absence of the species. Results of discriminant analysis showed the three functions explained 86.4, 10.7, and 2.8% of the total data variance, respectively. Generally, 19 variable including elevation, slope, aspect, precipitation, Topographic Wetness Index (TWI), plan curvature index (PC), SPI, pH, EC, lime, phosphorus, soluble potassium, soluble sodium, magnesium, organic carbon, bicarbonate solution, saturation percentage (SP), sand percentage and species density were identified as the important factors affecting the distribution of V. arctostaphylos. SP was the most important factor in the presence and absence of species.
Conclusion Generally, by identifying the most effective ecological factors on the distribution of V. arctostaphylos, effective steps have been taken to improve the habitat of the rare species.

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The fusion of valuable spectral and spatial features can significantly improve the performance of high resolution hyperspectral images classification. In this paper, we propose a spectral and spatial feature extraction method based on discriminant analysis. To increase the class discrimination, we maximize the between-class scatters and minimize the within-class scatters. To include the spatial information in the feature extraction process, we estimate the spatial scatters in a spatial neighborhood window with multi-scale fashion. We compare our proposed method, which is called spectral-spatial discriminant analysis (SSDA), with some spatial-spectral feature extraction methods included original spectral bands plus Gabor filters, gray level co-occurance matrix (GLCM), and morphology profiles and also with some popular spectral feature extraction methods such as nonparametric weighted feature extraction (NWFE) and locality preserving projection (LPP). Moreover, we compare SSDA with some recently proposed spectral-spatial classification approaches. The experimental results on two real hyperspectral images show the good performance of SSDA compared to the competitor methods.

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Classification of statistical elements is one of the challenging areas in management science. This subject has changed to an interesting research areas. Although methods of cluster analysis and discriminant analysis are used as common methods in the classification, there is a doubt about their application due to high statistical errors of the methods. In this paper, it is tried to combine analysis approach of statistical discrimination and OR technique and a new method titled goal discriminant analysis is developed. Four discriminant analysis methods titled FLDF, FG, GP1 and GP2 are applied in this paper. In order to evaluate its efficiency in management science area, the fourfold technique has been employed in 5 managerial case studies. The results show that the FLDF method, which is a discriminant analysis method, is more efficient than other methods. Moreover, goal discriminant methods have more efficiency in management classification with over two groups.

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*Mahmoud mousavi shiri,accounting department , Payame Noor University,Tehran,I.R.of IRAN Mohamad reza Tabarestani, Islamic Azad University of mashad In this research Multivariate Discriminant Analysis used as an effective model for pridiction of financial distress and noticed to ability of efficiency in improvement of model. At the first financial distress prediction model were designed base on Multivariate Discriminant Analysis by financial ratios variables of sample corporations in 1999 to 2008 years. Then for specifying ability of efficiency score variable in prediction of financial distress, we were designed financial distress prediction model base on Multivariate Discriminant Analysis with financial ratios and efficiency score. The results show that models are able to predict financial distress of production corporations in Tehran stock exchange two years prior to its occurrence. The results of two models and compare of them show that although efficiency score variable can better the results of financial distress prediction model but the different between accuracy predictions of financial distress in two models is not important.

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Production firms select and design their proper production system by attention market and resources. Production systems or production process be introduced by series of variables. The one of their most famous is product-process matrix of Hayes and wheelwright. It intoduce production process by using product adult and process adult variables. In this research, after survey previous studies, be recognized new dimentions to intoduce production systems. These dimentions defind by structural and infra-structral manufacturing decisions. A Sample with size 245 be selected from production firms in iran. Multiple discriminant analysis used to analys data. Results show that could introduce Iranian production systems on “Technology-Source” and “Quality plans”dimentions. Finnaly, represent conclutions and recommends for future researches.

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Global warming and predictions of climatic changes additionally put breeding for drought tolerance in the focus of breeding programmes for maize. Extensive studies on the existing gene bank collection of the Maize Research Institute “Zemun Polje“ have been performed with the aim to identify and form initial sources for the development of maize inbreds more tolerant to drought. All accessions (about 6,000) were exposed to controlled drought stress in Egypt. Out of this number, approximately 8% of the tested genotypes were selected. In this study attention was given to 321 selected Western Balkan maize landraces, adapted to temperate climate growing conditions and the day length. Data derived from morphological characterization according to CIMMYT/IBPGR descriptors for maize, along with the application of numerical classification methods, were used to define homogeneous landraces groups based on morphological similarities. Results obtained from hierarchical and non-hierarchical analyses revealed the formation of 11 divergent groups. According to the obtained grain yield and visually scored stalk lodging and stay green, approximately 15% of the accessions from each of 11 groups were selected. Further investigations are towards defining their heterotic patterns and their possible utilization in developing and improving synthetic populations. 

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This paper proposes a two phase strategy for proportional myoelectric control of Surena 3 humanoid robot which benefits from strength of two common myoelectric control methods, Pattern recognition base and simultaneous proportional control, for improving joint angle estimation. The aim of this research is to present a human-robot interface to create a mapping between electrical activities of muscles known as electromyogram (EMG) signals and kinematics of corresponding motion. First phase concerns with motion classification using Quadratic Discriminant Analysis (QDA) and Majority Voting (MV). Several common motion classification algorithms and feature vectors including time domain and frequency domain futures were investigated which lead to QDA and a superior feature vector with more than 97% classification accuracy. The second phase concerns with continuous angle estimation of shoulder joint motion classes using Time Delayed Artificial Neural Network (TDANN) with overall accuracy of 90% R2. QDA serves as a high level controller which decides between four TDANN correspond to each shoulder motion classes. QDA and TDANN models trained with several sets of offline data and were tested with online dataset. Online and offline data estimation accuracy and model robustness against disturbances show a significant improvement compared to similar methods in this field.

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In recent years, the most important philosophy agreed upon by organizations has been to create both economic, social, and environmental value in the form of the concept of sustainable management. Circular economy is a new concept to account for the sustainable management. The main purpose of this study is to analyze the performance of manufacturing industries from the perspective of the circular economy components. This research was conducted in the second half of 2020. The statistical population of this study includes the food industries in Bushehr province, which due to the limited size of the population, all of them were selected as statistical sample. The data collection tool of this research is a researcher-made questionnaire whose validity was checked by face content analysis method and its reliability was checked by Cronbach's alpha method. In this research, first, by studying the theoretical foundations and empirical background, effective components in circular economics were identified. Then, using the k-means algorithm, clustering of selected food industries was performed. The results showed that the food industries are in two industrial clusters: circular and linear. Next, the species detection function was obtained. It is suggested that linear industrial cluster managers pay more attention to energy efficiency, water consumption management, and sales of recyclable materials in order to transition to a circular economy. This research is of innovative nature in terms of developing the theoretical concept of circular economy and applying it to improve the performance of the food industry.