جستجوی مقالات مرتبط با کلیدواژه « خوشه بندی » در نشریات گروه « علوم پایه »

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.

This research presents a new method of coarse-tuning the fuzzy reinforcement learning architecture using agent-environment interactive data. This method solves two main challenges the fuzzy reinforcement learning: the low-speed training process and determining the input membership functions of the fuzzy structure. First, the agent interacts with the environment to gather training data. Then, the transition probability matrix and the expected return matrix are calculated by applying a clustering algorithm due to the continuous space. Each cluster is a state of the environment, and an approximation of the transition probability from one cluster to another is calculated using the gathered data. Finally, the parameters of the fuzzy system are adjusted using the modified value iteration method of dynamic programming for the continuous space. The proposed method is fully described with an example. This method increases the learning speed and adjusts the input membership functions of the fuzzy system.

In this paper, a new method for identifying outlier time series based on GARCH model by exponential distance approach is presented in three steps: first fuzzy and hard clustering methods are implemented on time series, then the outlier time series are detected and removed from the dataset. After removeing outlying time series, clustering algorithms are applied for dataset again. The 30 stocks of the top active, lucrative and profitable stocks in the Iranian stock market are used to evaluate the presented methods. By computing the Silhouette and Xe-Beni indexes, the accuracy of the clustering methods are compared and finally, it is shown that by removong the outlier time series, the GARCH model based on the exponential distance approach has the best performans.

Urban river valleys are natural corridors and aquatic habitats which provide an enlivening landscape for the cities and also they play an important role towards environmental, social, and cultural issues for urban residents and development in urban resident's life and development in their area. At the present time, structure and function of these valuable elements have been ruined due to non-planning growth and regardless to environmental subjects in municipal management and landscape planning that they have been converted to the runoff canal network or effluent. Several projects including restoration and rehabilitation of urban rivers have been defined to enhance environment of the city in Tehran. One of the weaknesses of urban management is distinguished just one dimension view towards development and even restoration. There are some solutions that can almost provide an ecologically sustainable condition for the river although the ability to return to the original state has been deprived. Implementation of engineering and managerial methods in compound and consideration of various dimensions of management are the main targets of this study, so land-use planning for the area around the river is applying as a suitable tool to restore and return it from a flooding area to a precious natural resource and servicing system. Of course, the objectives will obviously be reached if the key elements comprising ecology, economic, socio-culture, land-use, and executive and their subset indices have been considered in an integrating manner. In order to apply compound methods and presenting subjective results, Farahzad River Valley was selected as a case study.

Geographic Information System (GIS) and Urban River Survey (URS) were used to illustrate and classify sustainable ecological indices, respectively. At first, river area divided to 8 stretch in 50 meters then initial environmental indices including materials, habitat features, and flora were combined together and the features of each stretch were identified. Using cluster analysis, the values of initial indices were classified and entered in SPSS software. Therefore, the stretches were ranked from very good to poor in the various classes.

In overall, Farahzad River area were zoned in 4 qualitative classes, very good, good, moderate, and under moderate for landscape design. In the northern section of Farahzad River which is in the Kan Basin, about 32 percent of Farahzad River was situated in the very good class in terms of environmental indices. The very good class area was located in the mountain region. Because of the green spaces and gardens, approximately 11 percent was distinguished in good class. Both very good and good areas showed the potential for restoration and management of the river in these sectors. Nearly 12 percent of river stretch was classified in moderate level and other parts belong to moderate level. In the southern section of Farahzad River area which is located in the central catchment, the whole area was assessed in the under moderate level. That means probably it is possible for to define suitable land-uses gradually and design future landscape, with low changes.

Ultimately, sustainable recreation with river rehabilitation approach was distinguished as the best land-use of Farahzad River Valley Stretch that can considerably be implemented in the northern parts of the river and green space areas. Then, a thorough planning based on the holistic and integrated approach is required an account of river protection and rehabilitation if it is possible for the downstream parts, after Western Flood Control Canal where is under pressure by urban development.

Given that the current era is the era of information explosion, so the clustering of existing data and information is inevitable that must be done. Since in many cases we encounter widespread uncertainties in the available data, the best way to use clustering techniques is to combine them with fuzzy mathematics. Researchers have developed a variety of clustering algorithms for data clustering, some of which have fuzzy versions. The basic idea in fuzzy clustering is to assume that each cluster is a set of elements, then by changing the definition of element membership in this set from a state where an element can only be a member of a cluster, to a state where each element can To rank different memberships within several clusters, provide more realistic categories. The fuzzy mean c clustering algorithm (FCM) is the most common fuzzy clustering method. Various forms of FCM have been proposed so far. In this paper, based on FCM algorithm and similarity criterion, an efficient and robust clustering method for fuzzy data is proposed. This is the method. The similarity criterion proposed in this paper is based on α-sections and gives the decision maker the ability to make decisions at different levels. At the end, two numerical examples and a practical example are presented to show the efficiency of the proposed method.

Graphical mixture models provide a powerful tool to visually depict the conditional independence relationships between high-dimensional heterogeneous data. In the study of these models, the distribution of the mixture components is mostly considered multivariate normal with different covariance matrices. The resulting model is known as the Gaussian graphical mixture model. The nonparanormal graphical mixture model has been introduced by replacing the limiting normal assumption with a semiparametric Gaussian copula, which extends the nonparanormal graphical model and mixture models. This study proposes clustering based on the nonparanormal graphical mixture model with two forms of $ell_1$ penalty function (conventional and unconventional), and its performance is compared with the clustering method based on the Gaussian graphical mixture model. The results of the simulation study on normal and nonparanormal datasets in ideal and noisy settings, as well as the application to breast cancer data set, showed that the combination of the nonparanormal graphical mixture model and the penalty term depending on the mixing proportions, both in terms of cluster reconstruction and parameters estimation, is more accurate than the other model-based clustering methods.

Rivers, as the living artery of the watershed, have played a decisive establishment of civilizations. But today, large amounts of pollutants enter the aquatic ecosystems, due to overpopulation and the expansion of industrial and agricultural activities. Among these, heavy elements are of special importance due to their stability, non-degradability, toxicity and bioaccumulation potential. Therefore, in this study, potential toxic elements ( AS, Cd, Cr, Ni, Cu, Pb, Zn) were investigated in order to determine the concentration, contamination factor and degree of contamination, geo-accumulation index, Enrichment and to detect the level of pollution caused by them. For this purpose, 21 surface sediment samples were collected based on the distribution of point and non-point pollution sources and river morphology. Samples were conditioned and concentration of toxic elements and sulfur were measured using ICP-MS at dry weight with accuracy of mg/kg and percentage of total organic carbon. Using enrichment factor and sediment quality standards (ISQGs, PEL, SEL), the level of contamination caused by toxic elements was detected. Hierarchical clustering analysis (AHC) with pearson similarity coefficient was used to identify metal elements with similar behavior in river sedimentary environments and the most important cationic adsorbent such as organic matter and clay particles using XLSTAT2018 software

Asmari carbonate formation in Qale Nar oil field is one of the most important oil reservoirs in Dezful embayment. This reservoir has a lot of heterogeneity due to intensive changes in the sedimentary environment and diagenetic processes, which makes it very difficult to predict the petrophysical properties of the rock in reservoir. Creating a logical connection between sedimentary and electrical facieses, it can be effective in better understanding the reservoir and reducing uncertainties. In this study, using petrographic data and core description in six wells, 7 limy facies associations and one coarse crystalline dolomite were identified. Based on the petrophysical well logs of gamma, neutron, density, acoustic and photoelectric, and using Geolog software by MRGC algorithm, 5 electrofacieses (from 1 to 5) of the reservoir were determined. Electrofacies 1 has the best and electrofacies 5 has the worst reservoir quality. Examination of the frequency of electrofacies shows that good and very good electrofacieses make 30% of the Asmari reservoir of Qale Nar oilfield.

Plant community’s classification provides an organization framework for design of future studies of the Functional features of communities or systems and the relationship between species and the environment of the studied region and since the plant species growing in an area are affected by environmental and biological factors on these regions, Therefore, identification and classification of vegetation distribution and climatic, soil and geomorphometry properties are necessary. In this research, vegetation Sampling was done by random-systematic method to place plots along four transects. In each plot, vegetation Cover information (name of plant species and percentage cover) was recorded, and at the beginning and end of each transect was digging the soil profile. Then, climatic and geomorphometry data were extracted. In order clustering regions based on climatic, soil, geomorphometry factors, was used hierarchical method and to calculate distance between clusters was used Ward's method. The results of regional clustering based on environmental factors showed that these results correspond with reality and areas with a zero percentage coverage of Artemisia sieberi in a cluster and areas with a moderate to high level of A. sieberi were found in other clusters. Generally, it can be concluded that there is a relationship between the environmental factors and the distribution pattern of the plants.

Various sources and methods used to identify different zones of a field that can investigate different zones and layers of the reservoir. The purpose of this study is to determine electrofacies and HFUs in the Kangan and upper Dalan Formations in one of the southern Iranian gas fields. The importance of determining and investigate the facies with the well quality and separation of this facies from other facies causes better knowledge of quality zones and also due to the use of available information can save time and money. In this study use well log, the concept of clustering and the MRGC method in Geolog software, the log petrophysical data were clustered and finally, 5 electrofacies were identified. Among these identified facies, electrofacies No. 4 with dolomite lithology was identified as the best reservoir zone due to high effective porosity and low shale volume. After determining electrofacies by using the aforementioned methods, HFUs were identified with core data. Finally, after determining the HFUs, to investigate the relationship between electrofacies and HFUs, the cross sections of wells and HFUs are placed next to the designated electrofacies. After examining depth to depth, a high correlation was observed between them. This result shows that, this electrofacies model can be used in all wells in this field.

The amphipods of genus Niphargus Schiödte, 1847 inhabit in subterranean waters and constitute a substantial part of the groundwater biodiversity. In the last decades, there have been many developments for using of modelling algorithms, although not exclusive to the specific case of subterranean habitats. In this study, ecological variables were used to investigate the effect of environmental factors on Niphargus genus distribution. The occurrence points of the genus in north, west and northwest provinces were obtained from previous studies. Then, climatic variables, topography (slope, elevation, and moisture) were used, as well as temperature and moisture data at depths of 0 to 10 cm and 10 to 40 cm. In order to evaluate the similarity between habitat areas, k-mean clustering were used. Then, species distribution modeling was performed using Maximum Entropy Method at the country scale. The results showed that among all the variables used, only Bio14 had no normal distribution (P-value>0/05). Clustering results also showed that, the occurrence points of genus members is placed in two clusters, cluster 1 in the north of Iran and cluster 2 along of Zagros mountain range. There is similarities between two clusters. Two variables include the annual rainfall and soil temperature at depths of 10 to 40 cm have the most effect on the distribution of genus Niphargus. The results also showed that due to distance from the ground surface and the environmental variables, the same conditions exist in most occurrence points of this taxon.

The study of temporal and spatial variations and the estimation of groundwater fluctuations in researching and planning water resources management are important for continuation or development of exploitation.So far, various models have been used to estimate the groundwater level parameter. Stochastic models have been used more than the other models for hydrological studies. The most common stochastic model is autoregressive integrated moving average model (ARIMA). This model is based on the Markov chain theory.In previous studies, one or a few wells have been studied using stochastic models but in this research, 46 selected stations with the monthly data of the Tabriz catchment were used to analyze temporal and spatial variations of groundwater level.In this case study, there was a good correlation between the values of the groundwater level of wells near each other than the other wells, thus to avoid the effects of closed wells fluctuations, at first the whole stations are divided into seven sections using the clustering process, thenspatial studies done just for selected station in every section. The results indicate that the first, third and seventh clusters didn't have acceptable accuracy in simulation due to the throw data and four other clusters had good accuracy. Also, the second and first clusters had the best and worst modeling with maximum R-Square and minimum RMSE, respectively.The use of the Arima model on various stations in the Tabriz catchment area revealed that the accuracy of the model is reduced when the time series have more fluctuations with trends in the mean and variance that lead to unsteady series.

Geomechanical studies have important applications in various topics such as wellbore stability, well completion, well orientation, hydraulic fracturing plans and operations, sand production and hydrocarbon fields subsidence. It is necessary to prepare earth mechanical model of the well in the field. In addition, one of the necessary subjects to prepare mechanical earth model (MEM) is providing continuous rock mechanical parameters in the well. Rock mechanical parameters change by any variation in lithology. In this study, rock mechanical parameters are provided in continuous form, for Faraghan, Zakeen and Sarchahan formations for a field in the Persian Gulf and these parameters are clustered. Clustering resulted in recognizing six clusters with various rock mechanical characteristics. Petrographic study (i.e. determining facies, cementation and diagenesis) recognized five facies with different petrographic and cementation characteristics. These facies include quartz arenite and arkosic sandstones, shales, red mudstone and carbonates. A correlation between sedimentary and geomechanical facies was found. According to petrographic and geomechanical studies, the studied interval was divided into 7 sections. Accordingly, variation of rock mechanical parameters with regard to change in lithology was investigated. In addition, the impact of rock composition, cementation and compaction changes on rock mechanical parameters were evaluated as well.

The current morphometric research was conducted collecting Rhamnus cathartica and Rhamnus pallasii from the natural habitats in North and West of Iran. In order to clarify the relationships in studied species and understand taxonomicall values, 58 morphological characters were employed (26 quantititative and 32 qualitative characters) using numerical taxonomy, Principal Component Analysis (CPA), and cluster analysis. In this study, leaf shape, color fruit have been found to significantly contribute to the determination of the studied species (Rhamnus pallasii & Rhamnus cathartica). The results confirmed more diversity in Rh. pallasii and three sub-species. But, in Rh. Cathartica, diversity features in the two varieties were not effective. Rh. × spathulifolia were collected from sandy hill of North of Iran which was determined by morphometric features e.g., leaflet, abaxial, adaxial leaflet trichrome, and fruit color. Overall, Rh. × spathulifolia can not be considered.

A biological network represents the interaction between a set of macromolecules to drive a particular biological process. In a biological environment, abnormalities happen not only in one molecule but also through a biological network. One of the most effective methods to detect anomaly is the comparison between healthy and diseased networks. In this regard, biological network alignment is one of the most efficient ways to find the difference between healthy and diseased cells. This problem, protein-protein interaction network alignment, has been raised in two main types: Local network alignment and Global network alignment. According to the NP-completeness of this problem, different non-deterministic approaches have been proposed to tackle the Global network alignment problem. Recently, NetAl has been introduced as a common algorithm to align two networks. Although this algorithm can align two networks at the appropriate time, it does not consider biological features. In this study, we present a new framework called PRAF to improve the results of network alignment algorithms such as NetAl by considering some biological features like gene ontology (GO).

Pterocarya fraxinifolia is an ancient tree in north of Iran that have been recently reported two small stand of this species in west of Iran, Lorestan and Ilam province. In this study, population diversity and genetic differentiation of west populations and their vulnerability were assessed using ISSR markers. Eight to ten trees were selected from two west population as well as four populations of north of Iran, DNA extracted from leaf and genetic diversity was evaluated. AMOVA result indicated that the value of intra and inter population diversity is 88% and 12%, respectively. Shannon index is 0.157 and mean of heterozygosity is variable 0.064 to 0.119. Based on neighbor joining algorithm, the under study populations were split to two main groups that the west populations (Ilam and Lorestan) as well as Savadkouh were comprised the main group 1. Also, PCoA analysis showed clearly differentiation of Lorestan population from other under study populations.

Extreme cold and ice ages Are The major risks of climate Usually annually in damages to crops and plants as well. Synoptic statistical analysis to predict the occurrence of frost and before the correct decision, and it helps them to deal with. The study aims to identify and analyze the West Synoptic cold waves, collect data, daily minimum temperature stations during the statistical period 1961 to 2010 for the cold months (December to March) was. Using statistical indicators of long-term mean daily minimum temperature, Numerical threshold was cold waves and cold waves were identified on the basis of the 56-day combined. Cluster analysis of data and the level of 500 hPa SLP for 56 days, three patterns of atmospheric circulation there. In each model, the main cause of heat waves in the sea level pressure with a central pressure of 1032 hPa core exposure to the northwest, southeast of the North West and West of the country.
The anticyclonic circulation over the top of his loss very cold air over the region. At the level of 500 hPa deep trough is located in the western part of the region which fall within the northern cold air down through the long, cold waves is the main factor. Meridian flow westerly winds and currents of the North Important role in the occurrence of cold temperatures, resulting in the loss of cold air blowing across the top of the West. In most stations gust of cold temperatures aggravated by the high altitude region.