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The effort of this article is to solve one of the main problems in the field of banking, which is closely related to the field of information technology. The combination of the management discussion of this topic with the field of information technology will be one of the important topics in the field of information technology management. The main goal of this article is the clustering of bank customers.At first, all customer characteristics were extracted from the bank's database, which was randomly extracted for 900,000 customers, which will be provided as input to the proposed method of this article. All the characteristics of these customers were extracted and 10 characteristics (except four characteristics of the LRFM method) were listed using the opinions of experts. The proposed method should be able to choose among these 10 features for clustering customers, which results in more resolution in clustering. Due to the high number of cases of this problem, it is not possible to do it manually, and the proposed method tries to provide a separate model for clustering for the customers of each bank by examining different cases. Also, the problem of choosing the right value for the number of clusters in the K-means method is solved by the method proposed in this article. The results show that it is better than the basic RFM and LRFM methods.Keywords: relationship management with bank customers, clustering, RFM model, LRFM model, particle swarm algorithm, K-means method.

In the article there are presented results of the study of the state of user competencies for different specialties of the university digital educational environment (UDEE) on issues related to information security (IS). The methods of cluster analysis and analysis of digital (electronic) traces (DT) of users are used. On the basis of analyzing the DTs of different groups of registered users in the UDEE, 6 types of users are identified. These types of users were a result of applying hierarchical classification and k-means method. Users were divided into appropriate clusters according to the criteria affecting IS risks. For each cluster, the UDEE IS expert can determine the probability of occurrence of high IS risk incidents and, accordingly, measures can be taken to address the causes of such incidents. The algorithms proposed in this study enable research during log file analysis aimed at identifying breaches of information security within the university's DEE.

This paper proposes an appointed-time prescribed performance adaptive attitude controller with an event-triggered (ET) mechanism and optimized quantized communication for quadrotor unmanned aerial vehicles (QUAVs) in an uncertain environment. An appointed-time prescribed performance function is first designed to improve the convergence performance of attitude tracking errors considering different convergence time requirements and the convergence rate. In addition, to reduce the communication burden, a switching threshold ET mechanism and an optimized hybrid quantizer are adopted by using the K-means method. The ET mechanism not only involves the fixed threshold but also refers to the change rate of calculated control law flexibly and predictively. The optimized hybrid quantizer creatively classifies signals by their value and maximizes the advantage of each quantization method to reduce excessive quantization errors. It is shown that the proposed control scheme ensures that the closed-loop signals are uniformly ultimately bounded according to the Lyapunov method. Finally, the simulation experiments are implemented on a QUAV to demonstrate the effectiveness of the presented control scheme. [ABSTRACT FROM AUTHOR]

User clustering is crucial for tapping the flexibility of the load side and realizing dynamic management of power loads in new power system. K-means method is widely used in clustering analysis due to its simplicity, high efficiency, and scalability, but it needs to specify the number of clusters in advance, and is sensitive to the initial clustering centers. The current initialization method does not take into account the neighborhood distribution of the data points, and the direct use of data that has undergone dimensionality reduction processing leads to inaccurate selection of the initial clustering centers. To address the above problems, a new K-means improvement method that takes into account the initialization problem and the adaptive determination of the number of clusters: K-means clustering method based on nearest-neighbor density matrix is proposed in this paper. The method improves the efficiency of nearest neighbor search by building a K-D tree, and enhances the performance of unsupervised classification by utilizing the adaptive selection strategy of the number of clusters and the initial clustering centers selection algorithm. The proposed method is applied to real datasets, and its effectiveness is assessed by calculating three clustering evaluation metrics of the clustering results in comparison with several existing initialization and clustering methods. The experimental results show that the method proposed in this paper has higher stability and better clustering performance than existing clustering methods.

This research explores the utilization of the K-Means method for evaluating the performance of employees within the Information Technology Department at PT Nusa Halmahera Minerals. The research leverages data from Manage Engine to analyze diverse parameters of employee performance. By employing the K-Means method, employees are categorized based on specific characteristics, facilitating a deeper understanding of each individual's contribution and potential in attaining company objectives. The implementation of the K-Means method aims to offer a more objective perspective on employee performance, empowering companies to make informed decisions in the enhancement and development of their human resources.

In the article there are presented results of the study of the state of user competencies for different specialties of the university digital educational environment (UDEE) on issues related to information security (IS). The methods of cluster analysis and analysis of digital (electronic) traces (DT) of users are used. On the basis of analyzing the DTs of different groups of registered users in the UDEE, 6 types of users are identified. These types of users were a result of applying hierarchical classification and k-means method. Users were divided into appropriate clusters according to the criteria affecting IS risks. For each cluster, the UDEE IS expert can determine the probability of occurrence of high IS risk incidents and, accordingly, measures can be taken to address the causes of such incidents. The algorithms proposed in this study enable research during log file analysis aimed at identifying breaches of information security within the university's DEE. [ABSTRACT FROM AUTHOR]

Wire icing is a prevalent challenge in both industrial and scientific domains, and it is widely acknowledged that terrain and water vapor are significant contributing factors in the formation of wire icing. Consequently, the identification of terrains that are prone to inducing water vapor uplift serves as the scientific foundation for predicting ice accumulation on power lines. Yunnan Province, a mountainous province in China, features a large elevation difference. In winter, this region is prone to wire, pole and tower icing, which can affect power transmission and cause economic and property losses. Therefore, it is necessary to conduct research on the identification of typical terrain patterns in this region. In previous terrain studies, more attention has been focused on slope and aspect, watershed analysis and terrain profile analysis. When the purpose of the terrain identification is to analyze which terrains are more prone to collecting water vapor, we hope to obtain slightly larger terrain blocks and analyze the water vapor sources for different terrains in order to identify typical terrains that are conducive to icing formation. A new technology for identifying terrain patterns based on the K-means clustering method is proposed in this study to explore the typical terrain in Yunnan province. Additionally, the influences of different terrain patterns on water vapor movement are also analyzed. The results indicate that the typical terrains in Yunnan are "Valley-Air Channel", "Topographic Uplifting", "Ravine", "Mountain Pass" and "Alpine Divide" patterns. The results show that the identified typical terrain is consistent with observations from satellite images, which verifies the effectiveness of this identification method. Among these five typical terrains, the "Valley-Air Channel", the "Topographic Uplifting" and the "Mountain Pass" terrains are prone to collecting water vapor and forming ice cover. The "Alpine Divide" terrain is also prone to accumulating water vapor on both sides to form ice cover. The identified typical terrain demonstrates that typical terrain patterns near water bodies are more prone to the occurrence of wire, pole and tower icing because these areas are abundant in water vapor, and the extensive water vapor is easily condensed under the effects of terrain uplifting and cooling. In these key areas, existing wires and towers, as well as those to be constructed in the future, deserve our special attention. [ABSTRACT FROM AUTHOR]

The exponential rise in accidents and the introduction of new, supposedly trendy ways of living have contributed to the dire need for the needy to have an organ or blood transfusion. Circumstances refer to a circumstance where proper care should be taken when collecting the necessary blood or original parts for transfusion, typically in dire circumstances. To determine the distance at which the interested and qualified donors are located, a thorough investigation must be conducted. People are often first categorized according to their blood type, eligibility, and region. Following that, people group together according to locality. A healthy person can safely donate blood twice within 56 days, as this is the minimal time between successful donations that has been established as a norm. The decision to donate an organ is often made after careful consideration of the severity of the situation, the donor's health, and the health of the recipient. Knowledge data finding tasks can be made easier with the help of KEEL, an open-source programme. The graphs that are produced show clearly how the proposed algorithm varies from the standard K-means method. Therefore, it will be quite useful in the present day and could end up saving lots of lives. The necessity to decide ahead of time on the total number of groups is just one of the issues with the K-means clustering method. In practise, it is difficult to anticipate the precise number of clusters. When the number of clusters is small, incongruous clustering is more common, but when the number of clusters is large, like clustering is more common. Thanks to a method called Active Cluster with Modified k-means clustering, which finds the right number of clusters on the fly, the issue is now resolved. [ABSTRACT FROM AUTHOR]

This work involves calculations of climatic trends of anomalies in daily minimum, maximum, and average air temperatures based on the quantile regression method (QRM), which allows one to estimate trends in detail for any quantile in the range of quantile values from 0 to 1. Based on the QRM climate trend calculations detailed for different quantiles of trends in daily air temperature anomalies, clustering of more than 1400 meteorological stations of Russia is performed. Clustering is carried out in the multidimensional space, the formation of which takes into account seasonal peculiarities of the QRM trends of anomalies for three types of daily temperatures (daily minimum, maximum, and average temperatures) and features of the QRM trends in different parts of the quantile range. Twelve clusters of weather stations have been distinguished in the created multidimensional space using the k-means method. The stations that are included in each of the distinguished clusters are similar in terms of manifestation of the QRM trends of temperature. Despite the absence of characteristics of the geographical location of the observation stations among the variables of the multidimensional space, the stations within each of the twelve distinguished clusters are situated geographically quite compactly. The geographical distribution of stations assigned to different clusters is demonstrated and discussed. Based on the results of clustering, some features of quantile trends of temperature anomalies of specific seasons within the groups of stations assigned to individual clusters are described. Differences in manifestation of quantile trends between 12 clusters of Russian stations distinguished based on QRM quantile trends are obvious. At the same time, however, significant similarities can be observed between some individual pairs of clusters. The approaches and results of this work can be used to improve the climatic zoning of the Russian territory, which seems to be very relevant for the preparation and implementation of regional plans of adaptation to climate changes. The results can also be used for solving various applied climatology problems based on calculations of quantiles of different meteorological parameters. [ABSTRACT FROM AUTHOR]

North Aceh is one of the districts that has great potential in the maritime and fisheries sector, because part of the North Aceh region is a supplier of captured fisheries products. The problem with this research is that there is no model of a clustering system for captured fisheries products in North Aceh District, so it will be difficult to determine which areas produce superior and non-superior types of fish. This research aims to classify captured fishery results for the 2021- 2022 period using the K-Means algorithm with a Data Mining approach to determine superior and non-superior fish types. Therefore, this research can help a little in providing information about the types of fish that are superior and not superior or the types of fish that are most numerous and the types of fish that appear the least at fishing ports in North Aceh District. The stages carried out in this research started from compiling research instruments and literature review, data collection and analysis, designing a clustering system model and implementing the system. This research produces 2 clusters, namely cluster 1 is superior fish and cluster 2 is non-superior fish. The K-Means algorithm with a Data Mining approach can be used to group types of capture fishery products at fishing ports in North Aceh District. [ABSTRACT FROM AUTHOR]

The driving cycle is a significant input for the micro-scale analysis of vehicle emission. Several cities around the world have developed driving cycles based on their driving behaviours, while Pristina still has a lack of studies on this issue. Thus, the first objective of this study was to represent the proper driver behaviour of passenger cars in Pristina by obtaining a driving cycle from real-time measurements on the road. The driving cycle was developed by extracting the micro-trips from 53 491 s of data using a “3-sequential points” algorithm, where 780 micro-trips were detected. Using the K-means method from 780 micro-trips, we selected only 25 to develop the final driving cycle. The second objective was to analyse the effects of different intersections on the final driving cycle parameters. We found that the final driving cycle had significant differences from driving cycle of other cities. Driving habits in Pristina are more aggressive compared to other European cities. Also, we found that the average speed of roundabouts and un-signalized intersections was statistically different from signalized intersections.

Two paintings by the Neapolitan Renaissance painter Colantonio were studied with two non-invasive techniques to enrich the technical–scientific documentation. Infrared reflectography (IR) and x-ray fluorescence (XRF) analyses were performed on Saint Jerome in the studio and Saint Francis delivering the Rule, paintings preserved in the Museo e Real Bosco di Capodimonte. The IR scanning was performed to look beyond the visible layers of the paint for the preparatory drawings and pentimenti, or changes made during the painting process. The XRF technique was applied in many points to determine the elemental composition and enable the identification of pigments and materials used in paint and in the preparatory layers. Elemental XRF mapping was also carried out on a region of particular interest. Results provide an initial overview and hypothesis of color palette and techniques used by the artist.

One of the stages of the statistical study of the competitiveness of a region is the selection of competing regions.Purpose of the study. The purpose of the article is to form a statistical set of regions-competitors based on the sectoral structure of the economy.Materials and methods. As research methods in this article, the method of the main array, factorial, cluster methods, statistical methods are chosen. The statistical data of Rosstat were used for the study. To perform the calculations, the GVA was considered in the structure of Russian National Classifier of Types of Economic Activity2 for 2019. Results. With the help of factor analysis, 19 types of economic activity of the regions were grouped according to similarities and differences. As a result, six factors were formed, each of which collected dependent types of economic activity. The use of cluster analysis made it possible to form groups of regions with a similar sectoral structure of the economy. The study involved 85 regions of the Russian Federation. Cluster analysis made it possible to solve the methodological problem of determining the boundaries of GVA intervals for certain types of economic activity in the selection of competing regions.The paper shows that for the Amur Region, nine regions of the Russian Federation should be considered as competing regions. The regions of this cluster are united by a high share of gross value added by the types of activity “Transportation and storage”, “Public administration”, “Trade”. At the same time, competitors are regions from different federal districts: 70% of the regions of the Far Eastern Federal District, 20% of the Southern Federal District, 10% of the Siberian Federal District. The main results of the study are the following: 1) a high variation of the regions of the Russian Federation in 2019 was revealed by the type of economic activity “Mining” and “Manufacturing”; 2) a grouping of 19 types of economic activity of the regions was carried out using the factor analysis method; 3) a cluster analysis of the regions of the Russian Federation was carried out according to the sectoral structure of gross value added for 2019; five clusters were received. Conclusion. This paper shows that the selection of competing regions must be carried out using the sectoral structure of the region’s economy. Consideration of the region’ specialization is an important requirement of the selection methodology. The advantage of the author’s methodology is its universality, objectivity and reflection of the specialization of the region. As a direction for further research, one should consider determining the specialization of regions using localization coefficients and, on its basis, the formation of a statistical set of competing regions. The presented sample of regions is necessary for assessing their competitiveness.

In the event of a major disaster, base stations in the disaster area will cease to function, making it impossible to obtain life-saving information. Therefore, it is necessary to provide a wireless communication infrastructure as soon as possible. To cope with this situation, we focus on NFV/SDN (Network Function Virtualization/Software-Defined Networking)-enabled UAVs equipped with a wireless communication infrastructure to provide services. The access link between the UAV and the user is assumed to be equipped with a millimeter-wave interface to achieve high throughput. However, the use of millimeter-waves increases the effect of attenuation, making the deployment of UAVs problematic. In addition, if multiple UAVs are deployed in a limited frequency band, co-channel interference will occur between the UAVs, resulting in a decrease in the data rate. Therefore, in this paper, we propose a method that combines UAV placement and frequency division for a non-uniform user distribution in an environment with multiple UAVs. As a result, it is found that the offered data rate is improved by using our specific placement method, in terms of not only the average but also the outage user rate.

Blast-induced ground vibration is highly possible to result in serious losses such as destroyed buildings. The crucial parameter of the mentioned vibration is peak particle velocity (PPV). Many equations have been developed to predict PPV, however, worse performance has been reported by multiple literatures. This paper developed a method for predicting PPV based on Mamdani Fuzzy Inference System. Firstly, Minimum Redundancy Maximum Relevance was employed to identify the blasting design parameters which significantly contribute to the PPV induced by blasting. Secondly, K-means method was applied to determine the value ranges of the selected parameters. The selected parameters and corresponding value ranges were combined to input into Mamdani Fuzzy Inference System for obtaining predicted PPV. Totally, 280 samples were collected from a blasting site. 260 out of them were used to train the proposed method and 20 were assigned for test. The proposed method was tested in the comparison with empirical equation USBM, multiple linear regression analysis, pure Mamdani Fuzzy Inference System in terms of the difference between predicted PPV and measured PPV, coefficient of correlation, root-mean-square error, and mean absolute error. The results from that showed that the proposed method has the better performance in PPV prediction. [ABSTRACT FROM AUTHOR]

Copyright of Dokuz Eylul University Muhendislik Faculty of Engineering Journal of Science & Engineering / Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi is the property of Dokuz Eylul Universitesi Muhendislik Fakultesi Fen ve Muhendislik Dergisi and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Hydrological information is essential for adequate water resources management as well as for water supply, energy supply, water allocation, among other services. However, this information does not always exist in quantity and quality to be used in hydrological or water management studies, and alternative methods are required to estimate minimum flows. Estimation based on homogeneous regions enables to transfer observation data from a known location to a location without data, but in the same region. Since the fluviometric stations in the state of Goiás (Brazil) are not uniformly distributed, the present work aimed at delimiting homogeneous regions of minimum flows, using the cluster grouping method with the K-means algorithm. Thus, 71 fluviometric stations with at least 5 years of continuous data were selected, obtained from the HIDROWEB system. In addition to the observed data, other variables were considered, such as drainage area, perimeter, specific minimum flows Q7,10, Q90, Q95 and average slope. The use of all these variables together with the observed data made it possible to determine, with great accuracy, 5 homogeneous regions of minimum flows based on the cluster analysis, enabling to obtain the minimum flows of reference for each region. In the selected homogeneous regions, it was possible to observe that the regions with the highest values of average slope presented smaller minimum flows, and the same could be observed under inverse conditions, i.e., lower values of average slope had higher minimum flows. It is also noteworthy that river monitoring is deficient in the center-south and center-north parts of the state of Goiás, making water resources management difficult. This fact indicates, therefore, the need to expand the river monitoring system throughout the state, especially in its southern and northern regions. [ABSTRACT FROM AUTHOR]

Research background: The idea of sustainable development, in the face of the challenges encountered by contemporary society, is gaining increasing popularity. Currently, it recognizes the substantial role that companies play in its successful implementation. Initiatives in the field of sustainable development may be undertaken by companies independently as part of their own activities, or together with entities forming the supply chain as an element of sustainable supply chain management. Purpose of the article: Identification of groups of companies that are characterised by a different approach to cooperation in the field of sustainable development in the supply chain. Methods: The quantitative research was conducted in September 2020 with the use of the CATI (Computer-Assisted Telephone Interview) technique and a standardised survey questionnaire. A total of 500 randomly selected companies located in Poland participated in this study. The respondents were representatives of top management of the companies. In order to identify various groups of companies, a cluster analysis was performed using the k-means method in SPSS. Findings & value added: Based on the literature analysis, 3 areas of sustainable development have been identified, in which companies can become involved -- green design, sustainable operations, and reverse logistics & waste management. For each of the 3 areas, 3 clusters of companies were identified: companies that are not involved in sustainable development at all (1), companies that carry out most of the sustainable development initiatives independently (2), companies that carry out most of the sustainable development initiatives jointly with supply chain partners (3). The article also shows that the companies in different cluster differ in terms of perceived economic benefits achieved thanks to the implementation of sustainable development initiatives. This may suggest the need to develop separate sustainability solutions for such groups of companies in the future. [ABSTRACT FROM AUTHOR]

Copyright of Polish Statistician / Wiadomości Statystyczne is the property of State Treasury - Statistics Poland and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The key task of computer vision is the recognition of visual objects in the analysed image. This paper proposes a method of searching for objects in an image, based on the identification of a cluster representation of the query descriptions and the current image of the window with the calculation of the relevance measure. The implementation of a cluster representation significantly increases the speed of identification or classification of visual objects while maintaining a sufficient level of accuracy. Based on the development of models for the analysis and processing of a set of descriptors of keypoints, we have obtained an effective method for the identification of visual objects. A comparative experiment with the traditional method has been conducted, where a linear search for the nearest descriptor was implemented for identification without using a cluster representation of the description. In the experiment, a speed gain for the developed method has been obtained in comparison with the traditional one by approximately 5.2 times with the same level of accuracy. The method can be used in applied tasks where the time of object identification is critical. The developed method can be applied to search for several objects of different classes. The effectiveness of the method can be increased by varying the values of its parameters and adapting to the characteristics of the data. [ABSTRACT FROM AUTHOR]

Wire icing is a prevalent challenge in both industrial and scientific domains, and it is widely acknowledged that terrain and water vapor are significant contributing factors in the formation of wire icing. Consequently, the identification of terrains that are prone to inducing water vapor uplift serves as the scientific foundation for predicting ice accumulation on power lines. Yunnan Province, a mountainous province in China, features a large elevation difference. In winter, this region is prone to wire, pole and tower icing, which can affect power transmission and cause economic and property losses. Therefore, it is necessary to conduct research on the identification of typical terrain patterns in this region. In previous terrain studies, more attention has been focused on slope and aspect, watershed analysis and terrain profile analysis. When the purpose of the terrain identification is to analyze which terrains are more prone to collecting water vapor, we hope to obtain slightly larger terrain blocks and analyze the water vapor sources for different terrains in order to identify typical terrains that are conducive to icing formation. A new technology for identifying terrain patterns based on the K-means clustering method is proposed in this study to explore the typical terrain in Yunnan province. Additionally, the influences of different terrain patterns on water vapor movement are also analyzed. The results indicate that the typical terrains in Yunnan are “Valley-Air Channel”, “Topographic Uplifting”, “Ravine”, “Mountain Pass” and “Alpine Divide” patterns. The results show that the identified typical terrain is consistent with observations from satellite images, which verifies the effectiveness of this identification method. Among these five typical terrains, the “Valley-Air Channel”, the “Topographic Uplifting” and the “Mountain Pass” terrains are prone to collecting water vapor and forming ice cover. The “Alpine Divide” terrain is also prone to accumulating water vapor on both sides to form ice cover. The identified typical terrain demonstrates that typical terrain patterns near water bodies are more prone to the occurrence of wire, pole and tower icing because these areas are abundant in water vapor, and the extensive water vapor is easily condensed under the effects of terrain uplifting and cooling. In these key areas, existing wires and towers, as well as those to be constructed in the future, deserve our special attention.

Two paintings by the Neapolitan Renaissance painter Colantonio were studied with two non-invasive techniques to enrich the technical–scientific documentation. Infrared reflectography (IR) and x-ray fluorescence (XRF) analyses were performed on Saint Jerome in the studio and Saint Francis delivering the Rule, paintings preserved in the Museo e Real Bosco di Capodimonte. The IR scanning was performed to look beyond the visible layers of the paint for the preparatory drawings and pentimenti, or changes made during the painting process. The XRF technique was applied in many points to determine the elemental composition and enable the identification of pigments and materials used in paint and in the preparatory layers. Elemental XRF mapping was also carried out on a region of particular interest. Results provide an initial overview and hypothesis of color palette and techniques used by the artist. [ABSTRACT FROM AUTHOR]

To optimize graded lattice structures with spatially graded geometrical characteristics and promote the rational distribution of finite materials in macrostructure, a two-scale concurrent topology optimization method combining dynamic clustering is proposed. Dynamic clustering strategy is developed to group microstructures, which means that the clustering pattern will be updated according to the current strain energy during the whole iterations and obtains a more reasonable microstructures’ distribution than the static partition. Further, rotational degree of freedom is introduced to make sure that the microstructures are oriented along principal stress directions, so as to obtain a more reasonable load transfer path. Numerical examples show that, compared with the traditional static partition method, this method can more effectively distribute materials, make full use of the anisotropic of lattice structures, and improve structural performance. [ABSTRACT FROM AUTHOR]

Low flow is a parameter that is used for many purposes in water management. It is particularly important in terms of the needs of small water supply systems, which usually are located in ungauged catchment areas. The aim of this study is to determine the values of seasonality indices for the regionalization of low flow characteristics in the Upper Vistula river basin. This study analyzed three seasonality indicators (seasonality index—SI; seasonality histograms—SHs; and seasonality ratio—SR) in 32 selected catchments differentiated in terms of selected physiographic and meteorological characteristics. Daily flows, from the years 1951–2016, were used in the study. On the basis of the analysis, regions characterized by similar values of seasonality indicators were delineated. The non-hierarchical method of cluster analysis, i.e., the K-means method, was used for this purpose. For each region, obtained by this method, regression models were determined using stepwise regression analysis. Three groups of low flow occurrences were determined: winter, summer and mixed. The delineation of an additional mixed group for the Upper Vistula river basin resulted in a much better fit of calculated flows to observed values than in the case when only two groups of low flow seasonality were determined (winter and summer). [ABSTRACT FROM AUTHOR]

The driving cycle is a significant input for the micro-scale analysis of vehicle emission. Several cities around the world have developed driving cycles based on their driving behaviours, while Pristina still has a lack of studies on this issue. Thus, the first objective of this study was to represent the proper driver behaviour of passenger cars in Pristina by obtaining a driving cycle from real-time measurements on the road. The driving cycle was developed by extracting the micro-trips from 53 491 s of data using a "3-sequential points" algorithm, where 780 micro-trips were detected. Using the K-means method from 780 micro-trips, we selected only 25 to develop the final driving cycle. The second objective was to analyse the effects of different intersections on the final driving cycle parameters. We found that the final driving cycle had significant differences from driving cycle of other cities. Driving habits in Pristina are more aggressive compared to other European cities. Also, we found that the average speed of roundabouts and un-signalized intersections was statistically different from signalized intersections. [ABSTRACT FROM AUTHOR]

The object of research is the internal structure of management in universities. The research problem is to confirm the causal relationship between management and rating. Higher education is one of the most important indicators of the level of development of the state. That is why many countries of the world attach great importance to the issue of the quality of higher education. Different international and national university ranking systems of universities were created to reflect the quality of education in the corresponding higher educational institutions. Currently, university ranking includes such criteria as quality of education, indicators of employment of university graduates, the demand for the graduates in the labor market, the symbiosis of science, education and business, and mobility of students. These indicators are a direct result of effective management in universities. Based on this hypothesis, the paper makes an assumption about the possibility of clustering universities in the Republic of Kazakhstan in order to determine the effectiveness of management. The authors consider three clustering models: clear and fuzzy clustering based on k-means and agglomerative cluster analysis. It should be noted that the clustering of universities makes it possible to determine some consistency in relation to the organization of university management. The division of universities into clusters according to the degree of deterioration in management makes it possible to create a kind of hierarchical ranking of the organization of management of university activities. This creates prerequisites for analyzing the internal structure of management in leading universities with the purpose of studying and adopting these practices by universities in lower clusters. [ABSTRACT FROM AUTHOR]

In the event of a major disaster, base stations in the disaster area will cease to function, making it impossible to obtain life-saving information. Therefore, it is necessary to provide a wireless communication infrastructure as soon as possible. To cope with this situation, we focus on NFV/SDN (Network Function Virtualization/Software-Defined Networking)-enabled UAVs equipped with a wireless communication infrastructure to provide services. The access link between the UAV and the user is assumed to be equipped with a millimeter-wave interface to achieve high throughput. However, the use of millimeter-waves increases the effect of attenuation, making the deployment of UAVs problematic. In addition, if multiple UAVs are deployed in a limited frequency band, co-channel interference will occur between the UAVs, resulting in a decrease in the data rate. Therefore, in this paper, we propose a method that combines UAV placement and frequency division for a non-uniform user distribution in an environment with multiple UAVs. As a result, it is found that the offered data rate is improved by using our specific placement method, in terms of not only the average but also the outage user rate. [ABSTRACT FROM AUTHOR]

The scientific article is devoted to the development of a scientific and methodological approach to assessing the level of openness of the investment financial services market. Regarding the basic concept of the scientific article – “openness” - it is proposed to interpret it according to the flow approach, according to which three types of markets should be distinguished - markets with a net outflow of investment resources, markets with a net inflow of investment resources and markets with a balanced movement of investment resources. It is proposed to evaluate the openness of investment financial services markets by the k-means method, that is, by the cluster analysis approach, the purpose of which is to divide m observations into k clusters, while each observation refers to the cluster closest to its center (centroid). The advantages of the k-means cluster analysis method are as follows: firstly, the possibility of using this method with relatively small amounts of data, since it is not necessary to meet the requirements for the normal distribution of random variables, which are mandatory for classical methods of statistical analysis; secondly, to divide a set containing n objects into k clusters, the number of clusters must be specified in advance. Approbation of the methodological approach was carried out on the example of various national models of investment financial services markets, namely: on the example of the countries of the European Union and Ukraine. In general, the markets of investment financial services of post-socialist EU countries (Bulgaria, Estonia, Latvia, Lithuania, Slovakia, Slovenia, Poland, Romania, Hungary, Czech Republic) and Ukraine belong to the group of countries with a low level of market openness, compared to other markets of EU countries. The article develops a scientific-methodical approach to evaluating the openness of investment financial services markets, which, unlike other approaches, involves: distinguishing between markets with a net inflow of investment resources, markets with a net outflow of investment resources, and markets with a balanced movement of investment resources; distribution of markets using the iterative method of "k-means" cluster analysis into three clusters: 1) markets with a high level of openness; 2) markets with an average level of openness; 3) markets with a low level of openness; determination of the average volumes of portfolio investments in securities issued by non-residents, as well as liabilities for securities in which portfolio investments were made by nonresidents, for each cluster. [ABSTRACT FROM AUTHOR]

• Introducing a novel density measurement utilizing shared nearest neighbors, enhancing the assessment of data point density. • Developing an advanced K-means enhancement technique to dynamically determine cluster quantity and initial cluster center. • Integration of density-based and partition-based clustering methodologies, refining the K-means method's performance. • Achievement of peak performance in clustering outcomes via the proposed K-means modification. • Synergizing with various feature selection strategies for optimal clustering results proves its superior adaptability. User clustering is crucial for tapping the flexibility of the load side and realizing dynamic management of power loads in new power system. K-means method is widely used in clustering analysis due to its simplicity, high efficiency, and scalability, but it needs to specify the number of clusters in advance, and is sensitive to the initial clustering centers. The current initialization method does not take into account the neighborhood distribution of the data points, and the direct use of data that has undergone dimensionality reduction processing leads to inaccurate selection of the initial clustering centers. To address the above problems, a new K-means improvement method that takes into account the initialization problem and the adaptive determination of the number of clusters: K-means clustering method based on nearest-neighbor density matrix is proposed in this paper. The method improves the efficiency of nearest neighbor search by building a K-D tree, and enhances the performance of unsupervised classification by utilizing the adaptive selection strategy of the number of clusters and the initial clustering centers selection algorithm. The proposed method is applied to real datasets, and its effectiveness is assessed by calculating three clustering evaluation metrics of the clustering results in comparison with several existing initialization and clustering methods. The experimental results show that the method proposed in this paper has higher stability and better clustering performance than existing clustering methods. [ABSTRACT FROM AUTHOR]

The scientific article is devoted to the development of a scientific and methodological approach to assessing the level of openness of the investment financial services market. Regarding the basic concept of the scientific article – “openness” - it is proposed to interpret it according to the flow approach, according to which three types of markets should be distinguished - markets with a net outflow of investment resources, markets with a net inflow of investment resources and markets with a balanced movement of investment resources. It is proposed to evaluate the openness of investment financial services markets by the k-means method, that is, by the cluster analysis approach, the purpose of which is to divide m observations into k clusters, while each observation refers to the cluster closest to its center (centroid). The advantages of the k-means cluster analysis method are as follows: firstly, the possibility of using this method with relatively small amounts of data, since it is not necessary to meet the requirements for the normal distribution of random variables, which are mandatory for classical methods of statistical analysis; secondly, to divide a set containing n objects into k clusters, the number of clusters must be specified in advance. Approbation of the methodological approach was carried out on the example of various national models of investment financial services markets, namely: on the example of the countries of the European Union and Ukraine. In general, the markets of investment financial services of post-socialist EU countries (Bulgaria, Estonia, Latvia, Lithuania, Slovakia, Slovenia, Poland, Romania, Hungary, Czech Republic) and Ukraine belong to the group of countries with a low level of market openness, compared to other markets of EU countries. The article develops a scientific-methodical approach to evaluating the openness of investment financial services markets, which, unlike other approaches, involves: distinguishing between markets with a net inflow of investment resources, markets with a net outflow of investment resources, and markets with a balanced movement of investment resources; distribution of markets using the iterative method of "k-means" cluster analysis into three clusters: 1) markets with a high level of openness; 2) markets with an average level of openness; 3) markets with a low level of openness; determination of the average volumes of portfolio investments in securities issued by non-residents, as well as liabilities for securities in which portfolio investments were made by non-residents, for each cluster.

To overcome the sensitivity of the initial value generated by the K-means algorithm, the selection method of an initial clustering center is improved in this paper. The clustering algorithm of a random walking centroid is used to improve the efficiency, stability, randomness, and diversity of the iterative process and prevents convergence to the loacl optimal solution. The identification model of busy water in fishing areas is developed and applied to the Minnan fishery. The proposed model can accurately identify the to overcome the sensitivity of the initial value generated by the spatial distribution and scale of busy fishing areas. It can also adjust the algorithm parameters according to the merchant ship scale, and generate the targeted recommended route. The results can help maritime security departments. Furthermore, the identification method helps novel safety supervision of the maritime department. [ABSTRACT FROM AUTHOR]

In the paper, for adequate computer detection, the concept of an object is formalized as a dynamically structured pixel set. A pixel set is considered dynamically structured if (a) it is represented by a sequence of approximations in 1, 2, ... etc., colors; (b) it is described by a convex dependence Eg of total squared errors of piecewise constant image approximations on the number g of their colors; (c) reversible merging of such sets is supported. Attempts to adequately determine the object in the image are made within the framework of the formulation and study of the optimization-approximation problem of simulating successive optimal image approximations corresponding to the sequence of the smallest approximation errors (total squared errors), which are approached by the majorant sequence of errors of the binary suboptimal approximation hierarchy. Both sequences of approximation errors are convex, which limits the difference between them from above. Since the sequence of optimal approximations is, generally speaking, non-hierarchical, it does not coincide with the binary hierarchy of suboptimal approximations. Therefore, the target binary hierarchy is constructed ambiguously and parameterized by a suitable control parameter. Dynamically structured objects, consisting of nested ones, are represented by binary hierarchy of image approximations. The non-hierarchical sequence of optimal approximations, described by convex Eg sequence, represents the image. By intersecting partitions of optimal image approximations in an expanding range of colors, an irregular hierarchy of superpixels (indivisible image elements) is generated, so that the limited series of optimal approximations can be accurately reproduced by merging of given number of superpixels. The image partitions into superpixels is chosen from a number of image approximations constituting an irregular hierarchy, described by nonconvex sequence Eg of approximation errors E. The software implementation of the concept of a dynamically structured object is supported by three classical methods of cluster analysis, namely, Ward pixel clustering, splitting/merging techniques, K-means method, by modernizing and jointly applying them. As a result, it is proposed to use strengthened versions as the listed methods, namely, generalized Ward's pixel clustering by image parts, split/(reversible merge) method (Clustering Improvement method), and K-meanless method. How does it works is illustrated by detecting objects identified by known instances in an additional image merged with the image in question into a single whole. [ABSTRACT FROM AUTHOR]

Reasonable evaluation of building energy performance can provide great benefit for government (policy makers) to put forward effective energy conservation policies, and building regulators to make suitable energy-saving strategies. Traditional approaches employ the annual total energy consumption (or the annual total energy use intensity) to evaluate and rank the energy performance of buildings. However, these approaches ignore the energy consumption fluctuation of buildings in different seasons. As a consequence, this paper proposes an improved data-driven-based building energy performance evaluating and ranking approach for office building in city scale using the Simple-normalization, Entropy-based TOPSIS and K-means method. The proposed approach chooses the monthly energy use intensity in single year of office buildings as evaluation indicators, and consists of three steps: (1) The building energy use intensity are calculated by Simple-normalization method. (2) The Entropy-based TOPSIS method is applied to score and evaluate the building energy performance. (3) Finally, the K-means method is employed to rank the evaluated buildings. To validate the proposed approach, 24 office buildings in Tongling city of China are served as case study to present the evaluating and ranking procedure. Simultaneously, the comparison analysis between the proposed approach and two traditional approaches are conducted considering consistency and rationality synchronously. The comparison results demonstrate the proposed approach can more effectively score the energy performance of office buildings, and achieve the reasonable order and grade. In addition, to demonstrate the application, the proposed approach is also applied on Urban-area Building Energy Consumption Monitoring System of Tongling city in China. As a result, this study provides convenient and effective tool to present the energy efficiency gap and energy-saving potential between different buildings in city scale.

The publication contains the results of research in the field of cluster analysis carried out using data quoted on the Day-Ahead Market of TGE S.A. Two methods were used in the analysis, one hierarchical known as the Ward's method, and the other non-hierarchical - the k-means method. Many interesting research results have been obtained, which are illustrated, among others, in in the form of dendrograms, silhouette graphs and graphs in the form of clusters. Data on the volume and the volume-weighted average price of electricity were examined for various types of quotations: fixing 1, fixing 2 and continuous quotations. The research was carried out in the MATLAB and Simulink environments using a library called Machine and Statistics Learning Toolbox. Selected test results were interpreted. [ABSTRACT FROM AUTHOR]

The presented article is devoted to the study of methods of protecting personal data from the influence of social engineers (SIN). In the course of the work, the types of personal data and ways of their entry into the network, methods of data protection in the network, methods of social engineering (SI) and methods of protection against SI were investigated, information resources containing information about social engineering and data protection were analyzed. Data collection was carried out by the survey method, which characterizes the behavior of people with personal data, in particular when using the Internet. The received data is processed with the help of intelligent data analysis tools using Google and Orange tools. A model of safe behavior has been developed for saving data on the network, the use of which will significantly complicate the access of social engineers to personal data of a person. The results of the study were implemented in the analytical platform KitStat as an update, which allows to provide informational support and ensure the continuation of data collection for further research. [ABSTRACT FROM AUTHOR]

Quantifying the relationship between reservoir flood capacity and peak flow reduction can identify reservoirs with good flood control effect and be important for reasonably allocating flood control capacity for larger peak flow reduction. In this study, an optimization-clustering-fitting framework consisting of the Dynamic Programming-Progressive Optimality Algorithm, K-means method, and multiple linear regression, is proposed and applied to a multireservoir system in the middle and lower reaches of the Ganjiang River, China. The results reveal the law of diminishing marginal benefit in reservoir flood control capacity, which means that as reservoir capacity increases, the peak flow reduction increase is larger in low water levels than that in high ones. In this multireservoir flood control system, the Wan'an reservoir has greater flood control potential and can reduce more peak flow of the downstream flood control points than the Xiajiang reservoir when the same capacity of both reservoirs is used for flood control. [ABSTRACT FROM AUTHOR]

2019 yılının Aralık ayında Çin’de ortaya çıkan Covid-19 salgını, kısa bir süre içerisinde dünyanın tamamına yakınını etkilemiştir. Geçen zamanda ekonomide birçok sektörde daralmalar ortaya çıkmış, neticesinde çoğu ülkede yüksek işsizlik ve enflasyon oranları görülmeye başlanmıştır. Salgınla mücadelede sağlık sektörünün artan giderleri, kamu harcamalarında yüksek oranda artışlar meydana getirmiştir. Kamu harcamalarının finansmanı için borçlanma ve para arzını genişletme yolunu tercih eden çoğu ülkede, enflasyon oranında artışlar görülmüştür. Bu çalışmada, dünyanın en büyük yirmi ekonomisi, makroekonomik veriler ve kamu maliyesi göstergelerinden yola çıkılarak karşılaştırılmıştır. Bu bağlamda Covid-19 salgını öncesi, salgının başlangıç dönemi ve salgının başlangıcından sonraki dönem olmak üzere üç farklı zaman dilimi Hiyerarşik Kümeleme Analizi ve Hiyerarşik Olmayan Kümeleme Analizi yöntemlerinden K-Ortalamalar metodu kullanılarak ayrı ayrı incelenmiştir. Bulgularda, makroekonomik göstergelerle elde edilen kümelerde, kamu maliyesi göstergeleri ile elde edilen kümelere göre ilgili dönemler arası ülke geçişlerinin daha fazla olduğu görülmüştür. Ayrıca Türkiye’nin makroekonomik, Japonya’nın ise kamu maliyesi göstergeleri açısından her dönemde tek başına bir küme oluşturduğu ve diğer ülkelerden ayrıştığı saptanmıştır.

This study concerns the identification and monitoring by acoustic emission (AE) of the different damage mechanisms leading to the final rupture of glass/epoxy laminates. The series of specimens are subjected to static and fatigue loading in order to follow the progression of the various damage mechanisms over time and to identify the most critical ones leading to material failure. The specimens are made with two stacking sequences ([0/90]s and [90/0]s) using the vacuum infusion process. Several parameters such as the energy, the amplitude, the rise-time, the counts and the duration are used for the AE technique in order to identify the damage mechanisms evolutions to classify the collected events (AE). The damage mechanisms recorded in each stacking sequence are identified and characterized using a multi-parametric analysis. The damage accumulated in the test specimen is first of all transverse cracking, followed by interlaminar decohesion and finally fiber breakage leading to total composite failure. [ABSTRACT FROM AUTHOR]

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Copyright of Dicle University Journal of Engineering / Dicle Üniversitesi Mühendislik Dergisi is the property of Dicle Universitesi and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)