As the most complex manufactured structures, cities face excessive population growth. Their expansion has intensified on high-risk sites, and the available evidence also indicates the continuous increase of all types of natural crises in terms of intensity and frequency. Scientific and experimental findings show that the best way to deal with danger is to promote the resilience of settlements in different dimensions (social, economic-livelihood, physical-spatial and institutional); in other words, resilience in both human and environmental dimensions comprehensively. It decreases and increases. This research has evaluated and analyzed the components of resilience in Sari. The method of the present study is applied in terms of purpose and descriptive-analytical and field in nature. The statistical population in this research includes citizens living in the four districts of Sari, and the sample size was determined based on Cochran's formula of 383 people, who were selected from among the statistical population by stratified sampling. The questionnaire is the method of collecting library and field information and its most important tool. For data analysis, descriptive and inferential statistics (one-sample t-test and structural equation modeling) were used by SPSS and Smart PLS software, and entropy and SAW models were exerted. The research results indicate that the situation of the four regions of Sari regarding social components has better conditions than other dimensions of resilience. In terms of institutional components, they have a vulnerable state. According to the entropy model, among the components of resilience, the institutional dimension has the most weight, and the economic dimension has the least weight. Moreover, according to the SAV model, Region 1 ranks first, and Region 3 of Sari ranks last in having the components of resilience dimensions.

principal components analysis is a well-known statistical method in dealing with large dependent data sets. It is also used in functional data for both purposes of data reduction as well as variation representation. On the other hand “handwriting” is one of the objects, studied in various statistical fields like pattern recognition and shape analysis. Considering time as the argument, the handwriting would be an infinite dimensional data; a functional object. In this paper we try to use thefunctional principal components analysis(FPCA) to the Persian handwriting data, analyzing the word Mehrwhich is the Persian term for Love.

Recognition and understanding the genetic control of traits, combining ability and genetic structure are directly related to the success of breeding programs. For this purpose, a 7 × 7 one-way diallel design was conducted in a randomized complete block design with three replications. The measured traits were included plant height, height to the first capsule, number of days to 50% and 90% of flowering, number of capsules per plant, number of seeds per capsule, number of days to physiological ripening, number of branches, leaves number and length, 1000-seeds weight, capsule weight, length and width, chlorophyll a, b and total chlorophyll, biological andeconomic yields, harvest index, oil and protein percentage. analysis of variance showed that there was a significant difference between genotypes and diallel analysis showed that the additive variance of all traits and dominant variance of all traits except height to the first fruit-bearing capsule were significant. The oltan cultivar was the best and Ardestan genotype was the worst genotype in terms of general combining ability. Sabzevar×TS-3 and Sirjan×Fars were the best hybrids in most traits. The general heritability was between 0.90 to 0.96 for biologic yield and number of branches, respectively and narrow heritability was between 0.36 to 0.91 for the number of branches and harvest index, respectively. The analysis of variance by Hayman method confirmed the results of Griffing analysis.

Purpose: This paper aimed to create a composite index for environmental quality. Since the results of empirical studies on economic-environmental variables nexus, considering different environmental indicators, are not consistent with each other, it seems necessary to use a comprehensive index that includes all dimensions of environmental pollution. Methodology: Using 6 environmental indicators related to two groups of selected OPEC and OECD countries for the period 2010 to 2019 and using three methods including principal component analysis, kernel-based principal component analysis, fuzzy robust principal component analysis, the creation of a composite environmental quality index is examined. Findings: The results showed that FRPCA method has more efficiency and ability in weighting environmental indicators than other methods due to having the lowest error criteria. Therefore, using this method, the composite index was calculated. Moreover, the results showed that along with the upward trend of economic growth, the quality of the environment follows a downward trend in OPEC countries and an upward trend in OECD countries. Originality/Value: Based on the results, policy recommendations as well as new perspectives and suggestions for future studies are presented.

The analysis of spatio-temporal series is crucial but a challenge in different sciences. Accurate analyses of spatio-temporal series depend on how to measure their spatial and temporal relation simultaneously. In this article, one-sided dynamic principal components (ODPC) for spatio-temporal series are introduced and used to model the common structure of their relation. These principal components can be used in the data set, including many spatio-temporal series. In addition to spatial relations, trends, and seasonal trends, the dynamic principal components reflect other common temporal and spatial factors in spatio-temporal series. In order to evaluate the capability of one-sided dynamic principal components, they are used for clustering and forecasting in spatio-temporal series. Based on the precipitation time series in different stations of Golestan province, the efficiency of the principal components in the clustering of hydrometric stations is investigated. Moreover, forecasting for the SPI index, an essential indicator for detecting drought, is conducted based on the one-sided principal components.

When data are in the form of continuous functions, they may challenge classical methods of data analysis based on arguments in finite dimensional spaces, and therefore need theoretical justification. Infinite dimensionality of spaces that data belong to, leads to major statistical methodologies and new insights for analyzing them, which is called functional data analysis (FDA).Dimension reduction in FDA is mandatory, and is partly done by using principal components analysis (PCA). Similar to classical PCA, functional principal components analysis (FPCA) produces a small number of constructed variables from the original data that are uncorrelated and account for most of the variation in the original data set. Therefore, it helps us to understand the underlying structure of the data.Temperature and amount of precipitation are functions of time, so they can be analyzed by FDA. In this paper, we have treated Iranian temperature and precipitation in 2005, extract patterns of variation, explore the structure of the data, and that of correlation between the two phenomena. The data, collected from the weather stations across the country, were discrete and associated with the monthly mean of temperature and precipitation recorded at each station. However, we have first fitted appropriate curves to them in which we have taken smoothing methods into account. Then, we have started analyzing the data using FPCA, and interpreting the results. When estimating the eigenvalues, we have found that the first estimated eigenvalue (q1^) shows a strong domination of its associated variation on all other kinds. Furthermore, the first two eigenvalues explain more than 98% of the total variation, in which their contributions individually were 93.7 and 4.3 percent, respectively. Contributions from others, however, were less than 2 percent. Thus, we have only considered the first two components. The first estimated principal component (PC) shows that the majority of variability among the data can be attributed to differences between summer and winter temperatures. The second PC shows regularity of temperature when moving from winter to summer. In other words, it reflects the variation from the average of the difference between the winter and summer temperatures.Furthermore, bootstrap confidence bands for eigenvalues and eigenfunctions of the real data were obtained. They contain both individual and simultaneous confidence intervals for the eigenvalues. We have also obtained single and double bootstrap bands for the first two eigenfunctions, and seen that they are extremely close to each other, reflecting the high degree of accuracy of the bands that are obtained by the single bootstrap methods.

Due to its special natural and geographical conditions, the city of Tonekabon is susceptible to many shocks, including earthquakes and floods, which brings the need to pay attention to urban resilience. The present study was conducted to analyze the state of urban neighborhoods from the perspective of urban resilience components. This research is applied in terms of purpose and descriptive-analytical method. The research's statistical population was comprised of citizens living in Tonekabon city. Using Cochran's formula, the statistical sample size was estimated to be 384 people. The data collection tool was a questionnaire, the validity of which was verified in the form of face and face validity, as well as divergent validity and reliability of the questionnaire using Cronbach's alpha and composite reliability. analysis of data and information was done using SPSS and PLS software programs. The findings of this research showed that the overall resilience of Tonekabon city is in an unfavorable situation. In such a way, the average experimental value obtained for the overall resilience of the city and its dimensions was lower than the average value of 3. Among the localities of the studied area, Karim Abad neighborhood, in which the overall average obtained was equal to 2.78, was in a better condition than other localities, and Tonekabon neighborhood, according to the average (2.39), was in an unfavorable condition among the studied localities. Among the other research findings, among the components of urban resilience, the physical factor with a path coefficient of 0.490 has the most significant impact and was ranked first, followed by the economic factor with a path coefficient of 0.348. In third place is the administrative, institutional factor with a path coefficient of 0.327 and in fourth place is the social dimension with a path coefficient of 0.264.