Introduction: Personality disorders have a wide impact on people's personal AND social life. Aim: The present study aimed to determine the degree of differentiation of CLUSTER B personality disorders based on interpersonal problems. Method: The design of the current research was descriptive AND of a comparative causal type. The statistical population included all people with CLUSTER B personality disorder in the 5 districts of Arak city in 2022. Based on the number of research groups AND variables, 120 people were selected from among those who referred to the psychology AND addiction treatment centers of Arak city using the available method AND responded to the Clinical Multiaxial Scales of Milon version 4 (2015) AND Interpersonal Problems (1996), people were also evaluated by Structured Clinical Interview for Personality Disorders (2016). To analyze the data, the DISCRIMINANT analysis method was used in SPSS software version 26. Results: The results indicate a significant difference between the components of sociable (P=0. 001), assertive (P=0. 001), aggressive (P=0. 001), caring (P=0. 001), dependent (P=0. 008), supportive (P=0. 001), AND involved (P=0. 001) in the studied groups, but did not show a significant difference between the studied groups (P=0. 14). In total, based on the evaluated components, 68. 3% of the participants were correctly placed in their groups. Conclusion: Considering that in the present study, most of the dimensions of interpersonal problems were significant in people with CLUSTER B personality disorders, as a result, this variable can be used to differentiate the personality disorders of this CLUSTER AND also the intervention plan.

Enough knowledge of genetic variation AND germplasm classification is necessary to select suitable parents for breeding purposes. In this study, the data derived from measurements of important agronomic traits was used to classify several Iranian AND foreign rice varieties AND their crosses. Here, four local cultivars were crossed with five pure lines with IRRI source in a line×tester approach. In the next year, parents AND their progenies arranged in rANDomized block design with three replications AND planted at the research field of rice research institute of Iran. Some agronomical AND morphological traits such as yield AND its components were recorded. Analysis of variance showed significant differences between genotypes for all traits. The result of factor analysis based on principle component showed that three factors accounted %77.72 of total variance. These three factors were named as morphological characteristics, yield AND its components AND phonological factor. In yield AND its components were some important traits such as grain yield, number of filled seed, number of empty seed AND seed weight that correlation among these characteristics with grain yield was significant. CLUSTER analysis by UPGMA method divided genotypes to nine groups.

In order to DISCRIMINANT ANALYSES of efficient indexes on production of apiaries in East Azerbaijan to define the special relationship between some of the important factors affecting the production AND the amount of future production of hive, analytical test done by providing AND completing a questionnaire form in apiaries in both breeding AND harvesting stages. As the two apiaries producing group i.e. high production group (higher than the average production) AND low production group (under average production) using specific parameters were divided AND tested. By examining the results AND using the statistical analysis methods, the role AND the impact of some of the factors affecting production were defined as a linear relationship. This formula makes it possible to predict the amount of apiary production before harvest, as by comparison of the resulting value for an apiary based on intended parameters with the obtained indicator, we can predicted its production AND in the case of low .performance, beekeeper urged to fix existing bugs.

The hyperspectral images allow us to discriminate between different classes with more details. There are lots of spectral bANDs in hyperspectral images. On the other hAND, the limited number of available training samples causes difficulties in classification of high dimensional data. Since the gathering of training samples is hard AND time consuming, feature reduction can considerably improve the performance of classification. So, feature extraction is one of the most important preprocessing steps in analysis AND classification of hyperspectral images. Feature extraction methods such as LDA have not good efficiency in small sample size situation. A supervised feature extraction method is proposed in this paper. The proposed method, which is called CLUSTER space linear DISCRIMINANT analysis (CSLDA), without obtaining the label of testing samples AND just with doing a CLUSTERing on testing data, finds the relationship between training AND testing samples. Then, it uses the power of unlabeled samples together with training samples for estimation of within-class AND between-class scatter matrices. The CSLDA improves the classification accuracy particularly in multimodal hyperspectral data. The experimental results on urban AND agriculture hyperspectral images show the better performance of CSLDA compared to popular feature extraction methods such as LDA, GDA, AND NWFE using limited number of training samples.

The objective of the research is to study the main factors affecting profitability of botton mushroom firms in Tehran province. The data were collected through published data AND questionnaire from all firms operative in Tehran province on 2005. The required information on total cost AND total revenue of the firms analyzed. In order to analyze profitability as well as the factors affecting profitability, so me indicators such as TR/TC, CLUSTER analysis AND DISCRIMINANT analysis were employed for economic evaluation of the mushroom producing units. Using K-mean CLUSTER analysis, units were divided into two groups, namely, less successful AND successful ones. The mean of TR/TC for the successful group AND for the less successful group were 2.72 AND 1.26, respectively. The results of DISCRIMINANT analysis indicated that the main variables that caused the distinction between these two groups were labour productivity, nutritional conversion coefficient AND the distance of firms to market, respectively.

UnderstANDing the physical characteristics of any particular regional climate features can play major role in lAND use planning. Climatic zoning of each region to identify possible environment AND to exploit them, AND to know the limitations AND hazards in order to anticipate, is essential. According to the environmental AND religious diversity, of climate on the northern AND north-west of Iran, in this study the climatic zoning of the area was carried out. For this purpose, the data from annual average of 18 elements in 34 synoptic stations in the climate region with a common 21-year period (1985-2005) were used. The methods of factor AND CLUSTER ANALYSES were used for this study. The factor analysis with principal components method, 18 elements in 5 regional climatic factors were summarized. These factors, in order of importance the factors humidity-precipitation, temperature, wind, thunder AND dust. A total of 93.35% of these factors explain the behavior of the local climate. After determining factor, using CLUSTER analysis method based on the integration, AND the measure of distance, as well as regional stations were grouped according to the operating characteristics. The station had parallels in a climate group AND thus 10 different climatic areas in the North AND North-west of Iran were identified.

In this research we used multivariable statistical methods (CLUSTER AND discriminative analysis) with the purpose of the recognition of spatiotemporal differences of precipitation in similar areas. We used monthly precipitation of 35 synoptic, climatic, AND rain-gauging station data records of Northern Iran including three provinces of Golestan, Guilan, AND MazANDaran for 1995-2007 periods. For grouping AND homogenizing the stations, we initially applied Ward CLUSTER analysis method. Then we used discriminative analysis AND Wilk’s Lambda for finding out the validity of CLUSTER analysis calculations. Results obtained from CLUSTER analysis with Euclid interval method indicated that 4 major CLUSTERs can be drawn according to the amount AND the location of the precipitation in the study area. Discriminate analysis showed that 82.3% of the CLUSTERs in our analysis were valid AND about 17.7% were incorrect. The Wilk’s Lambda method also proved the differences between the means.

Thermodynamically, the surface waters in mountain environments are unstable AND their chemical composition is a reflection of water-rock interactions. Few studies have also been undertaken on the geochemistry of the surface waters in mountain environments. Hence, this paper deals with the source of elements as well as the chemistry of the stream which drains from the caldera of Bidkhan volcano.