Set Of Statistical Indicators Research Articles

A systematic data characteristic understanding framework towards physical-sensor big data challenges

Big data present new opportunities for modern society while posing challenges for data scientists. Recent advancements in sensor networks and the widespread adoption of the Internet of Things (IoT) have led to the collection of physical-sensor data on an enormous scale. However, significant challenges arise in conducting high-quality data analytics within the realm of big data. To uncover big data challenges and enhance data quality, it is essential to quantitatively unveil data characteristics. Furthermore, the existing studies lack analysis of the specific time-related characteristics of physical-sensor data. Enhancing the efficiency and precision of data analytics through the big data lifecycle requires a comprehensive understanding of data characteristics to address the hidden big data challenges. To fill in the research gap, this paper proposes a systematic data characteristic framework based on a 6Vs model. The framework aims to unveil the data characteristics in terms of data volume, variety, velocity, veracity, value, and variability through a set of statistical indicators. This model improves the objectivity of data characteristic understanding by relying solely on data-driven indicators. The indicators related to time-related characteristics in physical-sensor data are also included for the analysis of temporal aspects in the physical-sensor data. Furthermore, the big data challenges are linked to each dimension of the 6Vs model to gain a quantitative understanding of the data challenges. Finally, a pipeline is developed to implement the proposed framework, and two case studies are conducted to illustrate the process of understanding the physical-sensor data characteristics and making recommendations for data preprocessing to address the big data challenges. The proposed framework is able to analyze the characteristics of all physical-sensor data, therefore, identifying potential challenges in subsequent analytics, and providing recommendations for data preprocessing. Furthermore, the characteristic indicators can be used to analyze other types of big data.

Islands’ Tourism Seasonality: A Data Analysis of Mediterranean Islands’ Tourism Comparing Seasonality Indicators (2008–2018)

Tourism seasonality as a tourist demand concentration in specific seasons is still a big challenge for Mediterranean islands’ tourism policies. Overtourism occurs during high tourist seasons, and undertourism occurs during low tourist seasons, creating undesirable economic, social, and environmental effects on sustainability. However, the imbalance of tourism demand, specifically in fragile territories such as islands, needs specific policies based on scientific evidence. This article examines tourism seasonality dynamics in the Mediterranean islands, thoroughly analysing tourist overnights on island destinations. The adopted methodology uses a set of statistical indicators, such as peak factor intensity, calculated in a time series analysis from 2008 to 2018. The results of this article allow us to understand the importance for the Mediterranean islands to plan a resilient, sustainable, and harmonious tourism policy, using strategies to contain seasonal peaks and creating new tourist demand in the low season months.

A novel deep learning framework for landslide susceptibility assessment using improved deep belief networks with the intelligent optimization algorithm

This research proposed a novel deep learning framework that combines the Laplace function sparse regularized continuous deep belief network (LSCDBN) and the Gray Wolf Optimization Algorithm (GWO) and the Whale Optimization Algorithm (WOA), to perform landslide susceptibility assessment. This framework mitigates the challenges of feature homogenization for continuous input variables for landslide condition factors, limited landslide samples, and local optima in the training process. To facilitate this investigation, a meticulous compilation of existing landslide occurrences was used to create a database comprising 18 landslide conditioning factors. To compare the performance of the model, a set of statistical indicators was employed. The results demonstrate the superior performance of both the LSCDBN-GWO model (AUC = 0.952, RMSE = 0.182) and LSCDBN-WOA model (AUC = 0.964, RMSE = 0.174) when compared to the alone LSCDBN model (AUC = 0.913, RMSE = 0.291). It is noteworthy that the performance of the LSCDBN model outperformed that of lone machine learning models (SVM, BP, RF, and LR), lone deep learning models (RNN and CNN), and hybrid deep learning models (CNN-GWO and CNN-WOA). Therefore, it is evident that the proposed LSCDBN-WOA framework can generate models that are optimally suited for landslide susceptibility assessment.

Сімейна політика в повоєнних умовах: висновки для України

The article is devoted to the study of the experience of implementing family policy in countries where, at the same time, military actions took place on their own territory and the demographic behaviour of the population is close to that of Ukrainians. The relevance is due to the existence of significant challenges for the demographic and socio-economic development of Ukraine, which have intensified as a result of the Russian-Ukrainian war. The war intensifies depopulation against the backdrop of decline in the quality of life and increases the migratory outflow of the population. Family policy has the potential to mitigate demographic challenges. Studying the experience of its implementation in countries that have gone through heavy military operations on their own territory and have similar demographic behaviour of the population can contribute to the formation of vision for the development of family policy in Ukraine. The cases of wars and post-war reconstruction of Croatia and Bosnia and Herzegovina were determined to be the most comparable. The purpose of the paper is to study the experience of family policy in countries that had the wars on their own territory and to determine the possibilities of implementing their experience in Ukraine. The work uses the following methods: comparison, generalization and induction, historical analogy and systemic approach, critical assessment of scientific achievements in the relevant field, elements of graphic analysis and comparative legal analysis, abstract-logical method. A set of statistical indicators used in the article is based on the materials of Eurostat, OECD Family Database, World Bank Open Data, the Agency for Statistics of Bosnia and Herzegovina, scientific publications on the subject. The novelty of the work consists in supplementing ideas about the possibilities and consequences of the implementation of family policy in post-war conditions, as well as the possibilities of using the experience of other countries in the conditions of post-war recovery. It is substantiated that the development of the family policy of the European countries that experienced hostilities on their own territory took place in the conditions of a low level of funding, most of the planned instruments were not implemented, the documented goals regarding fertility (quite ambitious) were not achieved. The post-war fertility in the more economically successful Croatia, after the war-induced fluctuations and subsequent decline, stabilized at a higher level than in the poorer Bosnia and Herzegovina, although the pre-war situation was the opposite. The low level of family policy funding, low preschool enrollment rate, lack of significant childbirth payments, difficulties with the opportunities of mothers in the labour market, spread of gender stereotypes are characteristic of the considered countries. The development of family policy in the context of postwar recovery ultimately leads to attempts to develop a comprehensive family policy with an emphasis on childcare services, the spread of gender equality and support for working parents.

RETRACTED: Multi-attribute optimization of sustainable aviation fuel production-process from microalgae source

The aviation sector has been one of the most significant contributors to greenhouse gas (GHG) emissions; there is an urgent need to transition from traditional fossil-based jet fuel to sustainable aviation fuel to meet net-zero targets by 2030. The use of renewable aviation fuel may be regarded as the most effective option for reducing GHG emissions in the aviation sector while allowing for long-term growth. In this study, the microalgae oil was transformed to develop hydrocarbons with the boiling point range of jet fuel. Following the transesterification of algal oil fatty acids to algal oil methyl ester, fractional distillation was carried out. The Box-Behnken technique was used to design experiments for efficient resource utilization by minimizing the number of tests during transesterification. The quantitative relationship function between input (molar ratio of methanol to oil, catalyst concentration, and temperature) and % yield of methyl ester as an output of the process was developed using analysis of variance (ANOVA). The ANFIS (adaptive neuro-fuzzy inference system) was utilized to develop a prognostic model with good prediction effectiveness. MORSM (multi-objective response surface methodology) was also used to build a prediction model using correlations. A set of statistical indicators and Theil's U2 were used to examine the prediction efficacy and model uncertainty of ANFIS and MORSM. On both statistical indices and Theil's U2, the ANFIS-based model outperformed. To get the best results, the desirability approach was used to improve operating parameters. According to the desirability approach, the best operating parameters were catalyst concentration at 2.09%, methanol to oil ratio at 9.17%, and temperature at 60.49 °C, which resulted in the greatest yield of 91%.

Management of the Sports Industry for Ensuring the Public Health: Prospect of Implementing the Innovations

The article is devoted to studying the relationship between sports management and ensuring the population’s health. The study of current trends in the development of sports industry management was carried out with the help of bibliometric analysis. The results proved that the modern sports management system should consider the quality of the provision of sports services and the opportunities for their commercialization, the impact of the dispute on people’s lives and health, the role of sports in ensuring sustainable development, and the financial support of the sports industry. In order to assess the impact of the sports industry on public health and the quality of life, a set of statistical indicators was formed. These indicators characterize the functioning of the sports industry (the number of sports facilities, the involvement of the population in sports and recreational activities) and the state of the population’s health (parameters of life expectancy, morbidity, and mortality) of Ukraine for 2000-2021. Calculations were made by regression modeling using the Prais-Winsten method, including a time lag from 0 to 4 years. The obtained results proved that, at the current stage, the development of the sports industry has a limited impact on the health indicators of the population. In particular, it was found that engaging in the population’s physical culture and health activities contributes to reducing its mortality rate. Besides, engaging the population in such activity at enterprises, institutions, and organizations contributes to reducing the overweight population. On the other hand, a direct connection was also recorded between the growth of the population’s sports activity and the deterioration of certain public health parameters (in particular, the incidence of tuberculosis, hypertension, and mortality from certain diseases). At the same time, it was confirmed that the population’s activity in sports largely depends on the number of sports facilities. It made it possible to assess the transmission effects of the presence of sports facilities on the population’s health. Calculations have shown that the growth of the number of sports facilities for professional sports mostly has a negative impact on the health and life indicators of the population. In contrast, the impact of playgrounds with exercise equipment is mostly positive. That makes it possible to substantiate the areas on which the management of the sports industry should be focused to ensure the maximization of its positive impact on public health.

Prototype of Simulation model of Socio-epidemiological and economic consequences on the example of Russian Federation subjects

Processes associated with extraordinary events that are difficult to control and evaluate, as a rule, are built on patterns of past events or historical analogues, but the spread of coronavirus infection is a kind of unique phenomenon that has affected all spheres of life of states and the world as a whole. This article is a continuation of a study related to the assessment of the socio-epidemiological and economic consequences that led to the spread of COVID-19. Previously, the architecture of the model was created taking into account world practices for building such blocks for simulation models. The prototype combines three fully functional blocks. At this stage of the study, a prototype model was formed, which takes into account demographic, epidemiological and economic processes that are closely related to each other. The simulation has been running continuously since 2018 and covers three chronological periods - before the pandemic, during the pandemic and the forecast period. The experiment ends in 2025, after which a set of statistical indicators is produced for the three aligned blocks. The prototype model has prospects for expansion to all subjects of the Russian Federation. Also, if you add a loading module, it can be launched using supercomputer technologies on a scale of 1: 1 in terms of the number of agents that form an artificial society on the basis of which scenario experiments were carried out.

ОЦЕНКА УРОВНЯ ФИНАНСОВОЙ ГРАМОТНОСТИ ФИНАНСОВОГО ПОВЕДЕНИЯ НАСЕЛЕНИЯ РЕГИОНА (НА ПРИМЕРЕ КИРОВСКОЙ ОБЛАСТИ)

This scientific article discusses the unique methodology proposed by the authors to assess the level of financial literacy of the population of the Kirov region. In the course of the study, the main results of assessing the level of financial literacy and financial behavior of the population living in the Kirov region for 2021 were processed and presented. Research objectives: to analyze the theoretical aspects of financial literacy of the population presented in foreign and Russian science; to propose a descriptive model of financial literacy; to systematize methodological approaches to measuring financial literacy and propose a methodology for measuring the level of financial literacy of the population in the region; to develop a typology of socio-economic groups that differ in the level of financial literacy; to analyze the level of financial literacy of socio-economic groups of the population of the Kirov region on the basis of a questionnaire and a set of statistical indicators. The theoretical and methodological basis is the conceptual approaches and the theories and concepts presented within them, as well as the collective author’s model of financial literacy research based on the questionnaire survey method and the calculation of statistical indicators. The theoretical significance of the work lies in the need for a sociological analysis of such a complex phenomenon as financial literacy of the population, the development of theoretical and methodological foundations for its study in order to deeply analyze the results of applied sociological research. The practical significance of the work is to optimize the directions and mechanisms for implementing the activities of the Regional Program «Improving financial literacy of the population of the Kirov region» for 2020– 2023. based on the results of assessing the level of financial literacy and financial behavior of the population.

Precise Inter-Device Audio Playback Synchronization for Linux

Wave Field Synthesis is a method producing sound that uses arrays of closely placed speakers. This creates an unique challenge for distributed playback systems. Because of clock frequency drift, the playback must constantly be corrected via interpolation and shifting in time of the played stream. In this paper a new approach to network based audio playback synchronization is presented, that makes heavy use of the PTP network time synchronization protocol and ALSA Linux audio subsystem. The software does not need any specialized hardware and can approximate precisely how the playback stream should be interpolated via a set of statistical indicators. The evaluation shows that the difference between two devices playing audio using the presented system is under 10 μs for 99 % of the time, which fully satisfies the requirements of Wave Field Synthesis. The system was compared to other network audio synchronization systems available currently: NetJack2, RAVENNA and Snapcast, all of which had from 10 to 50 times higher differences between two devices than the presented system.

Validation of dispersion model designated for the coke production industry

In the practical application of air protection, diverse dispersion models are used to calculate the concentration of contaminants in the air. They usually involve a universal character, which typically makes them sufficient for use in almost all conditions, with the exception of those clearly deviating from the average. This is especially relevant to industrial objects of large areas, introducing a great amount of heat and mechanical energy into the air. For such cases, the standard models can be extended in order to adapt them to the unusual local diffusion conditions. Next, to be applied in practice, they must have undergone validation to document the correctness of its operation. The article describes the process of validation of the air quality assessment model containing extended procedures to incorporate special factors affecting atmospheric dispersion in a coke industry. The set of statistical indicators, obtained on the basis of SF6 field experiment, evaluate its performance. The short comparison with some popular models of general-purpose character and an assessment of the suitability of individual indicators for validation purposes are also presented.

Performance evaluation of NCEP/NCAR reanalysis blended with observation-based datasets for estimating reference evapotranspiration across Iran

Computing crop reference evapotranspiration (ETo) with the FAO Penman-Monteith method (PM-ETo) requires maximum and minimum air temperature, shortwave radiation, relative air humidity, and wind speed that are often unavailable in many places. The use of reanalysis data, which is common in climate studies, represents an alternative to observation data for the mentioned weather variables when are not available. This study focuses on the use of the National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR)reanalysis blended with gridded data sets for computing monthly PM-ETo at 43 synoptic stations distributed across Iran. First, with a set of statistical indicators, the reanalysis weather variables required for computing ETo were compared with observation data, where a good match was obtained for solar radiation, maximum temperature, and minimum temperature. In contrast, at most of the studied stations, the reanalysis wind speed and relative humidity showed large errors when compared with observations. The PM-ETo computed with the blended reanalysis data were also compared with those obtained using observations. The results show that the PM-ETo computed from blended reanalysis compares relatively well with the ETo computedfrom observation at most of the studied stations. However, the spatial pattern of performance indicators reveal extremely poor results for the coastal locations of the Caspian Sea in the north and the Persian Gulf and Oman Sea in the south of Iran. Results suggest that, except for wind speed and relative humidity, the blended reanalysis products are suitable forestimatingETo in inland areas of Iran despitesome degree of overestimationat most of the studied stations.

APPLICATION OF RECENT DEVELOPMENTS IN INTEGRAL DATA ASSIMILATION TO IN-DEPTH ANALYSIS OF UH1.2 EXPERIMENT AND TRANSPOSITION TO WHOLE PWR CORE

Recent developments in the Integral Data Assimilation (IDA) methods within Bayesian framework have been achieved at CEA to tackle the problem of correlated experiments (through technological uncertainties) and neutron transport model numerical effects. Hence, reference Monte-Carlo and deterministic calculations (TRIPOLI4® and APOLLO3®) are used together to solve neutron transport equations and get the sensitivity profiles. Furthermore, the analysis of the mock-up experiments technological parameters is performed to get accurate uncertainties and correlations between the experiments (finally the covariance experimental matrix required for IDA). We apply here the IDA approach with a new, extend set of statistical indicators (Cook’s distance, Bayesian and Aikike Information criteria (BIC, AIC)) implemented in the nuclear physics CEA CONRAD code, to the integral experiments UH1.2 in reference and voided configurations (standard PWR fuel assembly in the EOLE mock-up reactor). The adjusted multigroup cross-sections and posterior covariances are compared by choosing different ingredients in the assimilation process. Finally, the investigated key neutron parameters; reactivity, reactivity worth (void effects) and fissions rates are transposed (with the same CONRAD code) to a standard PWR core. This in-depth analysis enables us to predict the residual uncertainties and biases due to the multigroup cross-section adjustments assessing at the same time the similarity of these integral experiments for the main PWR neutronic safety parameters. In addition, technological parameters uncertainties and their impact on Bayesian adjustment process are taken into account through a global experimental covariance matrix. We point out that the UH1.2 experiments bring relevant additional information to PWR keff calculations reducing significantly the posterior results but are less relevant for fission rate distribution in reference and voided configurations.

ALTERNATIVE SOURCES OF INFORMATION TO SUPPORT A SET OF STATISTICAL INDICATORS OF THE BOOK PUBLISHING

Issues of supporting the book publishing as an economic activity by a set of statistical indicators are investigated. It is found out that the existing set of statistical indicators does not meet the needs of researchers and practitioners, which is the case of not only Ukraine, but the global book publishing area. The case of the Ukrainian book publishing is taken for analysis to identify core problems faced by this industry. It is emphasized that a comprehensive study of the book publishing industry and presentation of the statistical information with high level of quality and aggregation requires the involvement of new alternative sources of data, of which big data should be highlighted. The component of scientific novelty is that an updated system of statistical indicators is proposed for the first time, with eight modules of sources of statistical information as alternative ones: questionnaires, electronic books, digital libraries, websites of publishers and bookstores, electronic diaries of reading (Goodreads as an example), social networks (Instagram, Facebook, Telegram (open channels)), video hosts (YouTube being the most popular one), and blogs. It is stressed that all the modules of alternative data must be involved for obtaining reliable data, where output data will be processed anew and have direct and reverse links, which will require the use of neural networks with efferent type of links. This statistical support to the book publishing industry is an innovation designed to meet urgent needs of the public and official statistics.

Evaluation of Grid-Based Rainfall Products and Water Balances over the Mekong River Basin

Gridded precipitation products (GPPs) with wide spatial coverage and easy accessibility are well recognized as a supplement to ground-based observations for various hydrological applications. The error properties of satellite rainfall products vary as a function of rainfall intensity, climate region, altitude, and land surface conditions—all factors that must be addressed prior to any application. Therefore, this study aims to evaluate four commonly used GPPs: the Climate Prediction Center (CPC) Unified Gauge-Based Analysis of Global Daily Precipitation, the Climate Prediction Center Morphing (CMORPH) technique, the Tropical Rainfall Measuring Mission (TRMM) 3B42, and the Global Satellite Mapping of Precipitation (GSMaP), using data collected in the period 1998–2006 at different spatial and temporal scales. Furthermore, this study investigates the hydrological performance of these products against the 175 rain gauges placed across the whole Mekong River Basin (MRB) using a set of statistical indicators, along with the Soil and Water Assessment Tool (SWAT) model. The results from the analysis indicate that TRMM has the best performance at the annual, seasonal, and monthly scales, but at the daily scale, CPC and GSMaP are revealed to be the more accurate option for the Upper MRB. The hydrological evaluation results at the daily scale further suggest that the TRMM is the more accurate option for hydrological performance in the Lower MRB, and CPC shows the best performance in the Upper MRB. Our study is the first attempt to use distinct suggested GPPs for each individual sub-region to evaluate the water balance components in order to provide better references for the assessment and management of basin water resources in data-scarce regions, suggesting strong capabilities for utilizing publicly available GPPs in hydrological applications.

ЭВОЛЮЦИОННЫЙ АЛГОРИТМ РАЗБИЕНИЯ МЕТОДОМ КРИСТАЛЛИЗАЦИИ РОССЫПИ АЛЬТЕРНАТИВ

The operation of the partitioning algorithm is based on the use of collective evolutionarymemory, which means information that reflects the history of the search for a solution and isstored independently of individuals. The algorithm associated with evolutionary memory seeks tomemorize and reuse ways to achieve better results. The collective evolutionary memory of thepartitioning algorithm is a set of statistical indicators that reflect, for each implemented alternative,the number θ of its occurrences in the best solutions at previous iterations of the algorithmand the number δ indicating the usefulness of the implemented alternative when constructing solutionsat previous iterations of the algorithm. The team does not have centralized management, andits features are the presence of indirect exchange of information. Indirect exchange consists inperforming certain actions, at different times, during which some parts of evolutionary memorychange by one agent. In the future, this changed information is used by other agents in these parts.First, at each iteration, a constructive algorithm generates nk solutions Qk. Each solution Qk is amapping Fk=V→X, is represented as a bipartite subgraph Dk and is formed by sequentially assigningelements to nodes. The formation of each solution Qk is performed by the set of agents A,by means of the probabilistic choice by each agent ai of the node vj. The process of assigning anelement to a node involves two stages. In the first stage, agent ai is selected, and in the secondstage, the node. In this case, the restriction must be fulfilled: each agent of the set A correspondsto one unique node of the set V. The estimate ξk of the solution Qk and the utility estimate δk of theset of alternatives implemented by the agents in the solution Qk are calculated. At the second stage,the agents increase the integral utility of the set of alternatives in the integral placer of alternativesR* by the value δk. At the third stage, the utility estimates δk of the integral placer of alternatives arereduced by μ. The paper uses the cyclic method of forming decisions. In this case, the building up ofestimates of the integral utility δk of the set of positions P is performed after the complete formationof the set of solutions Q at iteration l. Experimental studies were carried out on the basis of formedtest cases with the optimal solution obtained earlier. The results obtained were compared with theresults obtained by other well-known algorithms for dividing circuits into parts. For comparison, aset of standard benchmarks was formed. After analyzing the results, we can conclude that the proposedmethod allows you to get 4–5 % better solutions than its analogues.

Industry Modernization Based on the Principles of Smart Specialization. Part I. Using the Key Enabling Technologies as a Priority of the EU Industrial Policy

The concept of smart specialization is investigated with emphasis on key components of its successful implementation. Its relation with the concepts of technology, innovation and industrialization is shown through their common goal: to increase the productivity and ensure the sustainable economic growth. An analytical review of the official European documents elaborating the mechanisms for industry modernization and the principles for the new strategy of building the smart, innovative and sustainable industry, to promote competitiveness, creation of hybrid jobs and economic growth, is made. The critical role of key enabling technologies, especially advanced industrial technologies, in modernizing industry and implementing the smart specialization strategy is highlighted. The European Commission initiatives are analyzed, aimed at implementing the programs on competitiveness enhancement in the small and medium business sector, their implementation measures, including ones on constructing statistical tools for monitoring and analysis of generation and use of advanced technologies in the context of technological innovations in the private sector. The methodological approaches to building a new set of statistical indicators are supposed to ensure approvals of political decisions on enhancing competitiveness in the EU industry through launching three specific and related actions: (i) support to the development of cross-regional investment projects in the framework of S3P-Industrial Modernization; (ii) support to the development of European strategic cluster partnerships for cooperating in thematic fields and enhancing the industry’s role; (iii)) support to companies in the manufacturing industry in using advanced industrial technologies, in order to enhance their innovation capacities. It is noted that the program “Government Support to Technological Innovations for Industry Development” has not been launched in Ukraine, in spite of 300,000 UAH appropriated in the Budget – 2018 and the procedure for use of budget funds, created and coordinated with the central executive power bodies, by which the funds had to be allocated in implementing industrial innovation technologies in the private sector, for two purposes: (i) co-funding of project implementation; (ii) partial compensation for interest rates on loans received by manufactures for project implementation. The need to form the Strategy for Smart, Innovative and Sustainable Industry in Ukraine with consideration to directives and best practices of EU and the Ukrainian realities is emphasized.

Possibilities of statistical methodology in the study of economic security of the region

Economic security, following the physical protection and security rapidly broke into the life of modern domestic society. This is a huge, very theoretically capacious section of the management system and at the same time a variety of practical activities. As the state of any economic system, characterized by the ability to withstand any negative encroachment from any subject, institution, other systems, social environment, etc., both economic and financial security is estimated, in fact, a polygamous system of indicators on all aspects of financial and economic activity.Purpose:therefore, the purpose of this scientific work, defined as the improvement of information support structures, decision-making in relation to economic security, fully meets the needs of management science and practice.Materials and methods:At the same time, all the methods, necessary for the statistical study of economic and financial security are used in the work: at each stage, an extensive block of statistical tools is involved, represented by the methods of the general theory of statistics and mathematical statistics, including multidimensional groups – cluster analysis, correlation and regression methods. The official materials of the state statistics and Internal Affairs Bodies of the Russian Federation, as well as the official statistics of the Ministry of Economy and Finance of Moscow Region Government were used to perform practical calculations.Results:A key aspect of this research work is related to the selection and formation of a set of statistical indicators, which in the interconnection and addition gave a complete comprehensive picture of the existing relationships and established patterns. These are demographic and resource indicators, and the results and efficiency of the economic complex. The use of the designated statistical tools allowed obtaining such results as justification of analytical capabilities and approbation of statistical methods in the study of economic security of the Moscow region; formation of clusters, consisting of homogeneous municipal districts of the Moscow region, designated as clustering units; calculation of relative analytical indicators; construction and interpretation of regression models.Conclusion:the complex of the obtained results and conclusions, as well as the scientifically based algorithm of statistical tools application, tested in the work, are undoubtedly an important information component in the management of economic and financial security not only in the Moscow region, but also in any region, both in the Central Federal district and in the whole central Russia.

علاقة الوظائف الداعمة لإدارة علاقة الزبون بتحقیق المیزة التنافسیة في قطاع الاتصالات دراسة حالة: موبیلیس للفترة الممتدة )جویلیة 2018إلى غایة سبتمبر (201

Relationship Marketing is a new marketing paradigm, that based on customer focus,operations and procedures to meet the needs efficiently and the least cost and effort, therebymaximizing the profits of the organization and customer. We aim through this study mainly tomeasure the relationship between customer relationship management, in developing thecompetitive advantage through their supported functions. By building a model that shows therelationship between the variables studied (Customer relationship management and competitiveadvantage) with its dimensions. This study has been conducted on a sample of 22 agency of thetelecom operator of ‘Mobilis’, relying on survey method to collect and analyze information by setof statistical indicators, with using of statistical program SPSS V20 .through using T test tovalidate hypotheses. The main results of this study show the existence of indicators that proofsthe relationship between customer relationship management adoptions by ‘Mobilis’and itscontribution on achieve the competitive advantage

On Evaluating Floating Car Data Quality for Knowledge Discovery

Floating car data (FCD) denotes the type of data (location, speed, and destination) produced and broadcasted periodically by running vehicles. Increasingly, intelligent transportation systems take advantage of such data for prediction purposes as input to road and transit control and to discover useful mobility patterns with applications to transport service design and planning, to name just a few applications. However, there are considerable quality issues that affect the usefulness and efficacy of FCD in these many applications. In this paper, we propose a methodology to compute such quality indicators automatically for large FCD sets. It leverages on a set of statistical indicators (named Yuki-san) covering multiple dimensions of FCD such as spatio-temporal coverage, accuracy, and reliability. As such, the Yuki-san indicators provide a quick and intuitive means to assess the potential “value” and “veracity” characteristics of the data. Experimental results with two mobility-related data mining and supervised learning tasks on the basis of two real-world FCD sources show that the Yuki-san indicators are indeed consistent with how well the applications perform using the data. With a wider variety of FCD (e.g., from navigation systems and CAN buses) becoming available, further research and validation into the dimensions covered and the efficacy of the Yuki-San indicators is needed.

Land concentration and land grabbing: Implications for the socio-economic development of rural communities in south-eastern Romania

In the periods of pre- and post-accession to the European Union, Romania experienced great and rapid land concentration and land grabbing. The current article is seeking to identify the relationship between these processes, and the socio-economic development of rural communities in two counties (Călăraşi and Ialomiţa) located in the south-east of Romania. By using a set of statistical indicators, the authors computed two secondary indexes: Agricultural Development Index and Social Disadvantage Index, whose correlation revealed that areas with massive land concentration (almost 80% of all Local Administrative Units) have a low level of socio-economic development.