Discriminant ANALYSIS is a way for classification of one object or a group to one or more separate groups that are known or unknown. in scientific researches we often use linear or quadratic functions for classification. But in this paper, we suggest a nonlinear discrimination method that uses two nonparametric.regression methods, namely multivariate adaptive regression splines and adaptive additive model in a simulation study, we investigate the application way of proposed methods and comparing them with the ordinary nonlinear discrimination methods via their means of error rates.

Introduction:Planning and management of water resources in Iran are affected by tempo-spatial distribution of rainfall. Therefore, study of precipitation can be a vital variable for evaluating water resources in different parts of Iran. Since climatic data are increasing in bulk study the individual places takes, therefore using of multivariate regionalization methods are inevitable. Recently, multivariate methods of cluster ANALYSIS has been used widely in climatic regionalization and it provides desirable results. Another method of multivariate is polynomial ANALYSIS that is less frequently used. In the latter if  a number of classes are used the result of regionalization can be tested. In present study precipitation of Northwest of Iran is classified using eight characteristics of precipitation using cluster ANALYSIS and were tested by discriminant ANALYSIS methods.

Introduction: Optimal social functioning and having healthy peer relationships are considered as two important conditions for children' s development. Many children who have difficulty in social relations are prone to behavioral disorders and cannot do well in their academic studies (Parker & Asher, 1987, qtd. in Yousefi & Khayyer, 2002). It could be argued that the main cause of social problems may be attributed to improper functioning of social behaviors such as interaction with the peer group (Dodge, 1985 and Gresham, 1986, qtd.in Deboo & Prins, 2006). Relation with others is multifaceted. Some of the obvious manifestations are acceptance or non-acceptance.

Garra rufa is one of the 73 Garra species of the family Cyprinidae. Scales are used more commonly thanany other structure in population dynamic studies, because of their transparency, ease of sampling, and the minimal injuriescaused to the fish during their removal. To DISCRIMINATE fish from different rivers including Bashar, Behbahan, Gerdab, Kheirabad, Kooshk-e-Bahram, Mazoo, Palangan, Sendegan, Shoor-Abad, and Sirvan Rivers using geometricmorphometric analyses of the scale shape, landmark based geometric morphometrics method was used. Seven landmarkpoints were depicted on each scale using the software TpsDig2. After procrustes analyses, landmark data were exposed to principle component ANALYSIS (PCA), CVA, and cluster analyses using the past software. The results showed that there were significant scale shape differences among some populations, but most of the samples showed overlapping shape distribution and could not be separated robustly. Among the samples which were analyzed, only the Sendegan and Gerdab river samples did not show significant difference in scale shape compared to all other samples.

Credibility assessment screening by a small system and receiving optimum result in minimum time is a basic need in critical gates, Therefore the aim of this research is automatic detection of stress in guilty persons through skin conductance response and photoplethysmograph signals which are convenient and ease-of-use sensors, In this paper, a set of database with interview protocol (including control and relevant questions) in mock crime (Stealing jewels) is provided, 40 subjects participated in the experiments, 28 time-frequency features are extracted from two mentioned signals, The function of dimension reduction algorithms including principal component ANALYSIS, Kernel based PCA, linear discriminant ANALYSIS, cluster based LDA is evaluated to select optimum features, Support Vector Machine, Bayesian and AdaBoost are used as classifiers, The evaluation of algorithms on database is based on LOO method, Maximum accuracy (81, 08%) is obtained through principal components ANALYSIS as feature selection method and Bayesian as classifier,

Brain as the most complex organ in the human body has been investigated from various aspects. The greatest origin of this complexity is due to the fact that, despite the fixed architecture of brain structure (physical connections), the functional connectivity is in a constantly changing state, resulting to different behaviors. In many mental diseases, both brain structural and functional connectivities and their relationship are changed and cause different symptoms. Investigation of brain connectivity variations in the disease may help to better understanding of the relationship between brain structure and function. One of the most severe and debilitating brain disorders is Schizophrenia in which both brain structure and function are involved. Among all available methods, multimodal ANALYSIS of data has been recently gained great interest to provide the capability of extracting association between separate neuroimaging data. However, due to their voxel based viewpoint, relationship between brain connectivities cannot be inferred. In this study, the joint independent component ANALYSIS (jICA) has been proposed to investigate the relationship between brain functional and structural connectivity. We applied the suggested approach to combine functional and structural connectivity, in order to assess abnormalities underlying schizophrenic patients relative to healthy people. The findings suggest that the correspondence between brain function and structure is not necessarily one-to-one. The results also indicated that variations in several structural fibers, such as superior longitudinal fasciculus and inferior longitudinal fasciculus, are associated with functional changes in the temporal and frontal lobes. Besides, analyzing the nodal strength and shortest path length in the obtained subnetworks demonstrates that the functional subnetworks efficiency in parallel information transfer in schizophrenic patients is reduced. Overall, the outcomes point out the capability of the proposed method to better understanding of brain functional and structural connectivity association and its variations in brain disorders.

BACKGROUND AND OBJECTIVE: Tibia fracture is one of the most common long bone fractures in body and primary treatment of open fracture always worries surgeons. The aim of this study was to evaluate the treatment of open tibia fractures by FLEXIBLE medullary nailing.METHODS: This descriptive-analytical study was performed on 44 patients suffering from open tibial fractures treated with FLEXIBLE medullary nails in Shahid Beheshti and Yahyanejad hospitals from 1998-2001. In this study with limited incision, fixation of open fractures (gustilo type I to type IIIA) was done by 2-3 nails (3mm or 3.5mm). Demographic information associated with variables such as infection, for 1.5 years followed up in patients and data was analyzed by ANOVA test.FINDINGS: Mean age of patients was 29.8 years and 37(84.1%) patients were male. The average time of union was 6.1 months. There were 2(4.5%) patients with deep infection and 7(15.9%) with superfacial infection. Delayed union was seen in 4(9.1%) patients and malunion was seen in 1(2.3%) patient and there was not non-union.CONCLUSION: Results of this study showed that FLEXIBLE medullary nailing is a reliable method for treatment of open tibia fractures.

Protection of soil organic carbon (SOC), as most important soil quality indicator, is the main factor in sustainable agriculture and soil ecosystem conservation. SOC distribution is mainly affected by soil management status. This research was conducted to investigate the effects of physical and management variables on SOC variations and to quantify the relative importance of these variables on SOC distribution in a rainfed watershed by use of canonical DISCRIMINATE ANALYSIS (CDA) and stepwise DISCRIMINATE ANALYSIS techniques. SOC quantities in soil sampling points were classified in four quality categories as: poor, low, medium and high. Then the effects of 30 physical and management variables on prediction of SOC classes were evaluated. Results indicated that among predicting models with physical exploratory variables, model with soil characteristics as independent variables including TNV, SP, gravel, clay and sand was able to reasonably predict optimum SOC class. But all models with management exploratory variable were able to predict optimum SOC class by use of first linear combination of canonical functions at a<0.0001. Model M5 showed highest canonical correlation with the first linear combination. All the variable combinations in significant models were able to predict poor and low SOC classes, precisely. Only M5 model had highest ability to distinguish high class of SOC. Among management variables, tillage system scenario and its components had highest impacts on SOC variability in this rainfed watershed. Stepwise DISCRIMINATE ANALYSIS was able to distinguish the effects of winter fallow system on SOC status improvement.