Hierarchical Cluster Analysis Method Research Articles

Comparative Study of Hydrochemical Classification Based on Different Hierarchical Cluster Analysis Methods.

Traditional methods for hydrochemical analyses are effective but less diversified, and are constrained to limited objects and conditions. Given their poor accuracy and reliability, they are often used in complement or combined with other methods to solve practical problems. Cluster analysis is a multivariate statistical technique that extracts useful information from complex data. It provides new ideas and approaches to hydrogeochemical analysis, especially for groundwater hydrochemical classification. Hierarchical cluster analysis is the most widely used method in cluster analysis. This study compared the advantages and disadvantages of six hierarchical cluster analysis methods and analyzed their objects, conditions, and scope of application. The six methods are: The single linkage, complete linkage, median linkage, centroid linkage, average linkage (including between-group linkage and within-group linkage), and Ward’s minimum-variance. Results showed that single linkage and complete linkage are unsuitable for complex practical conditions. Median and centroid linkages likely cause reversals in dendrograms. Average linkage is generally suitable for classification tasks with multiple samples and big data. However, Ward’s minimum-variance achieved better results for fewer samples and variables.

Contribution of GIS techniques and pollution indices in the assessment of metal pollution in agricultural soils irrigated with wastewater: case of the Day River, Beni Mellal (Morocco)

Wastewater irrigation and intensive agricultural practices are two of the major sources of soil pollution throughout the world. The aim of this study was to evaluate the levels of metal pollution in ground samples collected in the city of Beni Mellal, Morocco due to wastewater in the Day River. We collected 35 samples of agricultural soils at a sampling depth of 0–30 cm. As a first step, all samples were analyzed to determine some of the physicochemical characteristics of the soil samples (organic matter [OM], clay mineralogy [CaCO3], pH, electrical conductivity, texture) and the levels of specific metallic elements (As, Cr, Cu, Cd, Ni, Pb, Zn, and Fe). The results revealed that the analyzed soils had a clayey to silty-clay texture and alkaline pH; were rich in OM and carbonates; and had elevated levels of metallic elements (in mg/kg: As, 8.24; Cr, 77.77; Cu, 87.40; Cd, 4.89; Ni, 42.33; Pb, 109.66; Zn, 75.98; Fe: 26,248.18), all of which, with the exception of Ni, were higher than local background values. In addition, the concentrations of Pb and Cd considerably exceeded the limit values set by the World Health Organization and the Food and Agriculture Organization. To comprehensively assess the degree of pollution, we calculated a wide range of pollution indices, such as the geoaccumulation index (Igeo), enrichment factor (EF), contamination factors (CF), pollution load index (PLI), and potential ecological risk assessment (RI), using geochemical background values. The results showed that the levels of contamination in the soils under investigation ranged from moderate to high levels, suggesting an anthropogenic source. Analysis of the spatial distribution of the heavy metals showed high concentrations in the center and upstream part of Day River, with concentrations gradually decreasing downstream with the flow of wastewater. Statistical analyses were carried out to identify the relationship between the presence of heavy metals in the soils and the possible sources using principal component analysis and hierarchical clustering analysis methods and the Pearson correlation coefficient. The results of these analyses showed a significant and positive correlation between the presence of all heavy metals, indicating an identical or similar source, and also revealed that the contamination is related to the type of irrigation system, intensive agriculture practices, and soil properties (pH, OM, CaCO3). These results provide valuable geochemical data to support land-use managers, policy-makers, and environmental organizations in their efforts to monitor and decrease the accumulation of heavy metals in soils.

An electronic nose system for the monitoring of water cane shoots quality with swarm clustering algorithm

AbstractA fast, portable, nondestructive, and simple‐to‐operate method for determination of water cane shoot quality via an electronic nose (E‐nose) system is presented. The responses of E‐nose sensors to cane shoot samples during storage time are measured. Considering that one of the most common problems with fresh aquatic vegetables is the nearly negligible change in volatile organic compounds, a novel swarm clustering based on particle swarm optimization (SWC) algorithm is proposed to extract the effective features of the sensors responses. In SWC, the number of clusters is automatically estimated based on the neighborhood radius of the particles, which reduces the difficulty in determining the number of cluster. Traditional clustering algorithms, such as K‐means clustering (KMC) and hierarchical clustering analysis (HCA), are used for the purposes of comparison, and the changes in quality of cane shoot samples are quantitatively determined according to the hardness and pH. The results suggest that the E‐nose‐based SWC methodology is capable of accurately classifying the cane shoots freshness during the 20‐day storage time, which is much better than that of the comparative KMC and HCA methods. Additionally，quantitative analysis of the sensory evaluation, hardness, and pH is shown to have good agreement with the E‐nose system analysis results. Validation experiments show that unknown samples are successfully grouped into several different categories with a high correlation (R2 = .982) between unknown and known samples, demonstrating the potential use of E‐nose system to predict the freshness of the cane shoots.

Natural clusters of tuberous sclerosis complex (TSC)-associated neuropsychiatric disorders (TAND): new findings from the TOSCA TAND research project

BackgroundTuberous sclerosis complex (TSC)-associated neuropsychiatric disorders (TAND) have unique, individual patterns that pose significant challenges for diagnosis, psycho-education, and intervention planning. A recent study suggested that it may be feasible to use TAND Checklist data and data-driven methods to generate natural TAND clusters. However, the study had a small sample size and data from only two countries. Here, we investigated the replicability of identifying natural TAND clusters from a larger and more diverse sample from the TOSCA study.MethodsAs part of the TOSCA international TSC registry study, this embedded research project collected TAND Checklist data from individuals with TSC. Correlation coefficients were calculated for TAND variables to generate a correlation matrix. Hierarchical cluster and factor analysis methods were used for data reduction and identification of natural TAND clusters.ResultsA total of 85 individuals with TSC (female:male, 40:45) from 7 countries were enrolled. Cluster analysis grouped the TAND variables into 6 clusters: a scholastic cluster (reading, writing, spelling, mathematics, visuo-spatial difficulties, disorientation), a hyperactive/impulsive cluster (hyperactivity, impulsivity, self-injurious behavior), a mood/anxiety cluster (anxiety, depressed mood, sleep difficulties, shyness), a neuropsychological cluster (attention/concentration difficulties, memory, attention, dual/multi-tasking, executive skills deficits), a dysregulated behavior cluster (mood swings, aggressive outbursts, temper tantrums), and an autism spectrum disorder (ASD)-like cluster (delayed language, poor eye contact, repetitive behaviors, unusual use of language, inflexibility, difficulties associated with eating). The natural clusters mapped reasonably well onto the six-factor solution generated. Comparison between cluster and factor solutions from this study and the earlier feasibility study showed significant similarity, particularly in cluster solutions.ConclusionsResults from this TOSCA research project in an independent international data set showed that the combination of cluster analysis and factor analysis may be able to identify clinically meaningful natural TAND clusters. Findings were remarkably similar to those identified in the earlier feasibility study, supporting the potential robustness of these natural TAND clusters. Further steps should include examination of larger samples, investigation of internal consistency, and evaluation of the robustness of the proposed natural clusters.

Environmental Disposition Types of Homeless Supportive Housing Residents : Focused on H House Cases in Seoul

Homelessness has increased due to radical industrialization and economic crisis in Korea. Recently, supportive housing has begun to be distributed along with social welfare policies. Supportive housing for the homeless, often use existing rental houses, and it is necessary to be planned customer-oriented. Supportive housing should be more inclusive than general housing, which needs to accumulate knowledge about residents. The purpose of this study is to type in the environmental disposition of homeless supportive housing residents. More specifically, this study identifies the composite types of the environmental disposition analyzed in the previous study: territoriality (occupation/order) privacy (interaction/permission). For this study, field experts were transferred to the research team for unobtrusive observation. 47 data were collected in January 2019, equivalent to the total residents of “H house.” The data include quantitative part on the degree of the environmental disposition and qualitative part on expert’s observations. SPSS gave hierarchical cluster analysis method, resulting in a total of 12 environmental disposition types. The proportion of the types are Empty/Tidy/Alone/Closed, Filled/Messy/Alone/Closed, Filled/Tidy/Interact/Open, Empty/Tidy/Interact/ Open, Filled/Messy/Interact/Closed, Empty/Tidy/Alone/Open, Filled/Messy/Interact/Open, Filled/Messy/Interact/Closed, Empty/Tidy/Interact/Medium, Empty/Messy/Interact/Closed, Empty/Messy/Alone/Closed, Empty/Messy/Interact/Open. Environmental disposition types of residents varied with heterogeneity. Supportive housing should be planned to promote the healing and adaptation according to residents’ characteristics and to enable efficient maintenance. This study will contribute to the development of future housing planning by understanding of the customers.

An optimized industry processing technology of peanut tofu and the novel prediction model for suitable peanut varieties

Peanut protein is easily digested and absorbed by the human body, and peanut tofu does not contain flatulence factors and beany flour. However, at present, there is no industrial preparation process of peanut tofu, whereas the quality of tofu prepared by different peanut varieties is quite different. This study established an industrial feasible production process of peanut tofu and optimized the key process that regulates its quality. Compared with the existing method, the production time is reduced by 53.80%, therefore the daily production output is increased by 183.33%. The chemical properties of 26 peanut varieties and the quality characteristics of tofu prepared from these 26 varieties were determined. The peanut varieties were classified based on the quality characteristics of tofu using the hierarchical cluster analysis (HCA) method, out of which 7 varieties were screened out which were suitable for preparing peanut tofu. An evaluation standard was founded based on peanut tofu qualities. Six chemical trait indexes were correlated with peanut tofu qualities (P<0.05). A logistic regressive model was developed to predict suitable peanut varieties and this prediction model was verified. This study may help broaden the peanut protein utilization, and provide guidance for breeding experts to select certain varieties for product specific cultivation of peanut.

Uluslararası Ticaret ve Uluslararası İşletmecilik Bölümleri Lisans Ders Programlarının Karşılaştırılması: Türkiye ve Amerika Birleşik Devletleri Üniversiteleri

The aim of this study is to determine whether universities that offer undergraduate programs under different names such as International Trade, International Business, International Business and Management, International Business Management, Global Business, and Global Business Management have a cross-curricular standard, and to what extent they differ or bear similarities. Therefore, the undergraduate program curricula of thirty-six Turkish and twenty-one United States universities were compared by their course names and contents. Applying the Hierarchical Cluster Analysis method, the obtained results were supported by Euclidean proximity matrix and dendrogram. The dendrogram revealed that a single cluster with two universities formed at a distance of one unit while three clusters with multiple universities formed at a distance of five units. The total number of universities in these three clusters were 23. In the ten-unit distance referenced, there were thirty-eight universities in the first cluster, three universities in the second, and two universities in the third and fourth clusters, respectively. Seventeen of the United States universities gathered in the same cluster. All of the eight universities that were dissimilar and formed their own clusters within the ten-unit distance were the Turkish universities. The results revealed that there was a higher number of differences in the curricula of the Turkish universities. Considering the Euclidean proximity matrix and clusters, the names of the faculties and departments were found not to be a determinant in the similarity of the curricula.

A PRIMARY EXPLORATION ON THE SUPER-NETWORK MODEL OF ECONOMIC DEVELOPMENT UNDER THE RESTRICTION OF RESOURCES AND ENVIRONMENT

With the theory of super-network and the variational inequality method, this paper establishes a super-network model of economic development in three aspects, builds a new indicator system for measuring economic development, and calculates the equilibrium conditions for the balance of economic development. Firstly, the economic development is divided into three parts: economic growth, ecological environment optimization and scientific and technological progress. Respectively, with the set of measuring indicators in three aspects as a point of super-network and the link between these indicators as a super edge, thus a super-network model is established. Secondly, the hierarchical variable clustering analysis method is used to analyze the tightness of the links among the nodes in the super-network; and the objective function is established by using the partial measurement index, which simplifies the model. Finally, the variational inequality and projection iterative algorithm are used to calculate the equilibrium condition of the whole model. The main conclusions in this paper are as follows: (1) The indicators of economic development are interrelated with each other. (2) It can contribute to the balanced development of the economy in satisfied certain conditions.

Chemical composition of the essential oils of four Polyalthia species from Malaysia.

Polyalthia is one of the largest genera in the Annonaceae family, and has been widely used in folk medicine for the treatment of rheumatic fever, gastrointestinal ulcer, and generalized body pain. The present investigation reports on the extraction by hydrodistillation and the composition of the essential oils of four Polyalthia species (P. sumatrana, P. stenopetalla, P. cauliflora, and P. rumphii) growing in Malaysia. The chemical composition of these essential oils was determined by gas chromatography (GC-FID) and gas chromatography-mass spectrometry (GC-MS). The multivariate analysis was determined using principal component analysis (PCA) and hierarchical clustering analysis (HCA) methods. The results revealed that the studied essential oils are made up principally of bicyclogermacrene (18.8%), cis-calamenene (14.6%) and β-elemene (11.9%) for P. sumatrana; α-cadinol (13.0%) and δ-cadinene (10.2%) for P. stenopetalla; δ-elemene (38.1%) and β-cubebene (33.1%) for P. cauliflora; and finally germacrene D (33.3%) and bicyclogermacrene for P. rumphii. PCA score and HCA plots revealed that the essential oils were classified into three separated clusters of P. cauliflora (Cluster I), P. sumatrana (Cluster II), and P. stenopetalla, and P. rumphii (Cluster III) based on their characteristic chemical compositions. Our findings demonstrate that the essential oil could be useful for the characterization, pharmaceutical, and therapeutic applications of Polyalthia essential oil.

A hydrogeochemical analysis of groundwater using hierarchical clustering analysis and fuzzy C-mean clustering methods in Arak plain, Iran

In recent years, groundwater level in Arak plain aquifer, Iran, declines due to overabstraction and sustainable management of the aquifer is changed to a vital issue. Spatiotemporal variation groundwater quality associated with identifying hydrogeochemical characteristics is the goal of this research. In the current study, graphical methods results are compared to two distinct clustering methods, hierarchical cluster analysis (HCA), and fuzzy C-mean (FCM) to analyze the hydrogeochemical dataset. Groundwater quality of Arak aquifer is monitored over an 11-year period in two-year intervals ranging from 2004 to 2014 (i.e., 2004, 2006, 2008, 2010, 2012, and 2014) by sampling from 52 abstraction wells for each year. Graphical methods identified dominant hydrogeochemical processes in the study area. The resulting clusters were categorized into freshwater (HC1, FC1), brackish-saline water (HC2, FC2), and saline water (HC3, FC3). The analysis resulted in three clusters including recharge, transition or mixing, and discharge zones, designated as FC1 and HC1, FC2 and HC2, and FC3 and HC3; respectively. The comparison of groundwater facies in 2004 and 2014 showed that the mixing zone (classes FC2 and FC3 and classes HC2 and HC3) has expanded with time. Observation well monitoring in the area showed that groundwater quality decrease due to groundwater level declines, especially in the residential zone. In addition, the role of geological formations in groundwater quality was evident in the distribution of clusters. Comprehensive regulation and integrated groundwater management is essential to prevent further degradation of groundwater quality in the region.

Clustering the Eco Member Countries based on Their Socio-Economic Indicators by using the Hierarchical Cluster Analysis Method

Sosyo-ekonomik gelişme; yapısal ve insani gelişme ile birlikte ekonomik büyümeyi ele alan süreçleri kapsamaktadır. Ülkelerin, bölgelerin ve illerin gelişmişlik düzeylerini araştırmak ve karşılaştırmak amacıyla yapılan bilimsel çalışmalarında sıklıkla demografik, sağlık, eğitim, kültür, gelir, istihdam, yoksulluk, yaşam kalitesi ve çevre gibi sıklıkla sosyo-ekonomik ve kültürel parametreler kullanılmaktadır. Bu çalışmanın amacı, Ekonomik İşbirliği Örgütü (ECO) üyesi ülkeleri sosyo-ekonomik göstergelerine göre hiyerarşik kümeleme yöntemleri ile kümelere ayırmaktır. Uluslararası bir örgüt olan Ekonomik İşbirliği Teşkilatı (ECO), üye ülkelerin ekonomik süreçlerine katkı sağlamak maksadıyla 1985 yılında kurulmuştur. Sosyo-ekonomik göstergeleri itibariyle ECO’nun en gelişmiş ülkeleri Türkiye ve İran’dır. Afganistan ile Pakistan ise birçok sosyo-ekonomik göstergeler açısından benzerlik göstererek en az gelişmiş ülkeler kümesinde yer almışlardır.

Strangers ante portas: The framing of refugees and migrants in the Czech quality press

This article examines how the two most popular Czech quality dailies framed refugees and migrants during the European refugee crisis in 2015. It explores the extent to which the framing described by previous studies carried out in Western and/or destination countries can also be identified in the newspapers of a country that has had only minimal experience with migration. Instead of identifying frames beforehand and coding them as holistic variables, a routinely used approach to frame analysis, it employs a more reliable and transparent method of hierarchical cluster analysis. The dailies framed the refugees and migrants mainly as a burden on host society, as victims of a humanitarian crisis and, to a lesser degree, as a security threat. The results show that the frames used by the Czech dailies closely correspond to those described in previous research, despite the different methods of analysis and the different geographical and cultural settings.

Study on the Amino Acids in different Varieties of Osmanthus Fragrans Using Grey Pattern Recognition and Grey Hierarchical Clustering Analysis

The research aimed to comprehensively evaluate 17 kinds of amino acids in different varieties of Osmanthus fragrans. The grey pattern recognition and grey hierarchical clustering analysis methods were used to analyze the data of amino acids and find out the relationship among 17 kinds of themselves including aspartic acid, threonine, serine, glutamic acid, proline, glycine, alanine, cystine, valine, methionine, isoleucine, leucine, tyrosine, phenylalanine, histidine, lysine, arginine. Combined with MATLAB2013 and SPSS softwares, the gray metrology methods were made on the amino acids of Osmanthus fragrans and the efficacy of traditional Chinese medicine. The first gray correlation coefficient factor had greater grey correlation coefficient on the indexes of X1, X2, X5, X6, X7, X8, X9, X10, X11, X12, X13, X14, X16, X17, and the second gray correlation coefficient factor had greater grey correlation coefficient on the indexes of X4, X15, and the third gray correlation coefficient factor had greater grey correlation coefficient on the index of X3. 17 kinds of amino acids in Osmanthus fragrans in the order were Gold Osmanthus fragrans > Silver Osmanthus fragrans > the Four Seasons Osmanthus fragrans > Dan Osmanthus fragrans. Based on grey pattern recognition and grey hierarchical clustering analysis, a recognition model for evaluating traditional Chinese medicine quality was constructed. These studies provided the scientific base and theoretical foundation for the future large-scale rational relation development of Osmanthus fragrans resources as well as the relationship between the amino acids and traditional Chinese medicine efficacy for the human.

Multimorbidity and quality of life after blast-related injury among US military personnel: a cluster analysis of retrospective data

BackgroundBlast injury emerged as a primary source of morbidity among US military personnel during the recent conflicts in Iraq and Afghanistan, and led to an array of adverse health outcomes. Multimorbidity, or the presence of two or more medical conditions in an individual, can complicate treatment strategies. To date, there is minimal research on the impact of multimorbidity on long-term patient-reported outcomes. We aimed to define multimorbidity patterns in a population of blast-injured military personnel, and to examine these patterns in relation to long-term quality of life (QOL).MethodsA total of 1972 US military personnel who sustained a blast-related injury during military operations in Iraq and Afghanistan were identified from clinical records. Electronic health databases were used to identify medical diagnoses within the first year postinjury, and QOL was measured with a web-based assessment. Hierarchical cluster analysis methods using Ward’s minimum variance were employed to identify clusters with related medical diagnosis categories. Duncan’s multiple range test was used to group clusters into domains by QOL.ResultsFive distinct clusters were identified and grouped into three QOL domains. The lowest QOL domain contained one cluster with a clinical triad reflecting musculoskeletal pain, concussion, and mental health morbidity. The middle QOL domain had two clusters, one with concussion/anxiety predominating and the other with polytrauma. The highest QOL domain had two clusters with little multimorbidity aside from musculoskeletal pain.ConclusionsThe present study described blast-related injury profiles with varying QOL levels that may indicate the need for integrated health services. Implications exist for current multidisciplinary care of wounded active duty and veteran service members, and future research should determine whether multimorbidity denotes distinct post-blast injury syndromes.

Sexual self-concept among men and women child sexual abuse survivors: Emergence of differentiated profiles

BackgroundChild sexual abuse (CSA) can impact survivor’s sexuality, notably regarding sexual self-concept, a key component of sexual well-being. Yet, sexual self-concept has been understudied among CSA survivors and gender differences have been sparsely investigated. ObjectiveThe current study aimed to identify CSA survivors’ distinct profiles according to their sexual self-concept, and compare these profiles based on factors such as CSA characteristics, gender, current age, sexual functioning and adult sexual assault (ASA). Participants and settingA total of 176 CSA survivors (60 % women, 40 % men), recruited through community organizations for CSA victims and social media publications, completed an online survey. MethodsHierarchical cluster analysis was performed using the Sexuality Scale (Snell & Papini, 1989). Chi-square and ANOVA tests were used to compare the groups on external variables. ResultsCluster analysis revealed the best overall fit for a three-group model. The Confident and non-preoccupied profile (48 %) is characterized by a moderate score on sexual esteem and the lowest scores of sexual preoccupation and depression. The Demeaning and depressive (37 %) profile is characterized by the lowest scores on sexual esteem and the highest scores on sexual depression. The Hyperconfident and preoccupied profile (15 %) shows the highest scores on sexual esteem and sexual preoccupation. ConclusionSexual self-concept is an important component of sexuality that needs to be addressed by practitioners working with CSA survivors. Given heterogeneity and gender differences among survivors, identification of profiles is relevant for adapting interventions and clinical care.

PENGELOMPOKAN PROVINSI-PROVINSI DI INDONESIA MENGGUNAKAN METODE WARD (StudiKasus: Produksi Tanaman Pangan di Indonesia Tahun 2018)

Cluster analysis is a technique for grouping objects or observations into homogeneous groups. Cluster analysis is divided into two methods, namely hierarchy and non-hierarchy. The hierarchy method generally involves a series of n-1 decisions (n is the number of observations) that combine observations into a tree-like structure or dendogram. Hierarchy is divided into two methods, namely agglomerative (concentration) and splitting (distribution). For non-hierarchical methods, the number of clusters can be determined by the researcher. Ward method is a hierarchical cluster analysis method that can maximize homogeneity in the cluster. The Sum-of-Square (SSE) formula is used in this method to minimize variations in the clusters that are formed. In this research, squared euclid distance is used to measure the similarity between object pairs. The data used in this study are secondary data on food crop production, namely rice, corn, soybeans, peanuts, green beans, sweet potatoes, and cassava in Indonesia 2018. To determine the cluster, the elbow method is used to form optimal clusters using WSS formula. Based on the analysis results, it was found that the optimal cluster is four clusters. The first cluster consists of 9 Province, the second cluster consists of 20 Province, the third cluster consists of 1 Province, the fourth cluster consists of 2 Province, and the fifth cluster consists of 2 Province.Keywords: Food Crop, Cluster Analysis, Ward Method, Squared Euclid, Elbow Method

Cluster Analysis of the Routes of the New Management Model for Surface Urban Passenger Mass Transit

The city of Moscow has implemented a new management model for surface urban passenger transportation (NMM). To consistently monitor the quality of transportation services it is suggested to introduce a methodology of its assessment. The methodology for assessing quality of public transport services is supposed to be tested on routes serviced by commercial enterprises under state contracts for provision of public transportation services by public transport on the routes of regular passenger and baggage transportation by road in urban traffic. The objective of the research was to select objects in order to test the methodology for assessing quality of public transportation services provided by surface urban public mass transit (SUPMT) on routes serviced in the framework of NMM. A scientifically substantiated partition of a given sample of routes has been performed. The routes have been classified using the classical method of hierarchical cluster analysis, where the Euclidean distance with a single rule of cluster joining was used as a measure of proximity. The clustering procedure was performed using the Ward method. Based on the results of the cluster analysis, ten routes were selected that are subject to further analysis.

Searching for similarities in EU corporate income taxes for their harmonization

The purpose of this paper is to answer the question whether, despite the differentiation of the corporate income tax in the European Union, there are similarity patterns allowing for the harmonization of the bases of this tax. The analysed CIT static data both quantitative and qualitative concerns the years 2018 and 2020. The method of hierarchical cluster analysis allowed a grouping of EU countries according to their similarities. It also indicated the greatest tax differences between EU member states. In turn the affinity analysis made it possible to distinguish groups of countries which are similar in terms of CIT with the simultaneous identification of a pattern. Results show that despite significant differences in tax rates some EU states show convergence in tax bases. The geographical criterion still plays an important role in determining CIT structure and tax incentives are one of the tools that may be used in the approximation of the bases.

ЭКОНОМИЧЕСКИЙ ПОТЕНЦИАЛ И ТИПЫ ЭКОНОМИЧЕСКОГО РАЗВИТИЯ КРУПНЫХ МУНИЦИПАЛЬНЫХ ОБРАЗОВАНИЙ. ЧАСТЬ 1

In the present context, a significant potential for economic development of the Russian Federation is concentrated in large municipal structures. Research by foreign and domestic scientists, as well as the results of research by the authors is used as the initial data. The subject of the research is the driving forces for the development of economic potential of 34 large cities of the Russian Federation. The hypothesis is the assumption that there is a correlation between the types of economic growth and the economic potential of the territories of large municipal structures. The research methodology includes three stages: 1) classification of municipal structures according to the type of economic development by the method of hierarchical cluster analysis using the statistical package IBM SPSS Statistics; 2) assessment of the economic potential of large cities using the system of indicators (basic and used resource potential; infrastructure and innovative potentials) and an integrated indicator; and 3) comparative studies of municipal clusters by components of economic potential. The results obtained help to divide all large municipal structures into 4 clusters according to the type of economic development: 1) Cluster A, characterized by ultra-high decelerating growth rates in the volume of shipped products; 2) Cluster B, which has high rates in the volume of shipped products growing from year to year; 3) Cluster C with the growth rates of shipped products above average, with slow acceleration; 4) Cluster D, which has the lowest average annual growth rate without acceleration. The assessment of the integral indicator of the economic potential of large cities shows the dependence of the type of their economic development on the economic potential. The comparative analysis identifies priority areas for improving the economic potential. The presented results can be used to assess the real type of economic development of large cities in the Russian Federation and their economic potential, what will contribute to the development of measures for priority fields of expansion of the latter. A further field of research is the adaptation of the methodology for municipal structures with the population of less than 500 thousand people; however, it is difficult to obtain statistical information for these municipal structures.

MODELLING THE QUALITY AND PRODUCTION OF PETROLEUM PRODUCTS WITH MATLAB

The problem of control of indicators of quality of oil products on technological installations is considered. The review of methods of quality control of oil products is carried out, models for forecasting of temperatures of the beginning and the end of boiling of light oil products on oil refineries are considered. Mathematical models of indicators of quality of light oil products (temperatures of the beginning and the end of boiling) in the form of autoregressive models with external factors are received. The factors are the measured technological parameters of the refinery: temperature, flow, pressure. Models of such a structure can be used to predict the quality of light petroleum products in oil refineries. The application of clustering methods for the analysis of the range and production of petroleum products in the Federal districts is proposed. Hierarchical and iterative methods of cluster analysis are briefly described. An example of the application of cluster analysis methods for the evaluation of production and assortment is given. The article studies the task of monitoring the quality of petroleum products at process units. Review of controlling methods quality of mineral oil has been performed, models for forecast temperatures of starting and ending boiling of light oil products at refineries have been considered. Mathematical models of indicators quality of light oil products (temperatures of starting and ending boiling) in the form of autoregressive models with external factors have been obtained. Factors are measured technological parameters of oil refinery including: temperature, consumption, pressure. Models of a similar structure can be applied to forecasting quality indicators of light oil product at refineries. The application of clustering methods have been proposed for analyzing the range and production of petroleum products by federal districts. Iterative and hierarchical cluster analysis methods are briefly described. An example of application of cluster analysis methods to assess the production and range of petroleum products in federal districts over 2 years is given. Research results have been conducted using the MATLAB software package are presented. The obtained results can be used in the analysis of the range of petroleum products of primary and destructive refining within large territorial units. The possibility of forecasting the production of petroleum products using multiplicative models has been considered. Models with different seasonal components and trend types have been proposed. The stages of building a mathematical model have been given. According to statistical data on the development of motor gasoline over 5 years, mathematical models of a time series have been built using the example of the Russian Federation. The quality of modeling has been assessed using the obtained models according to the average relative error. According to the obtained model of production of automobile gasoline, a post-forecast has been built with a forecasting horizon of 1 year. The average relative error of post-prognosis of the Russian Federation does not exceed 5 %, in federal districts – 12 %. Models can be used to predict the production of light petroleum products in the federal districts of the Russian Federation.