Methods Of Multidimensional Analysis Research Articles

From the concept of threat to the common sense of societal threat: a qualitative inquiry of societal threats’ features / Del concepto de amenaza al significado lego de la amenaza social: un estudio cualitativo de las características de la amenaza social

The word ‘threat’ currently proliferates in the public sphere, which contributes to the large-scale information spread of societal threats of our time. However, the difference of common sense that laypeople attribute to the concepts of ‘threat’, ‘risk’ and ‘danger’ remains unclear. Therefore, this research aims to fill the social psychological knowledge gap and investigate laypeople’s common sense of ‘societal threat’. Participants ( N = 186) were asked to complete the word association task, and their narratives (N = 1,146) were analysed using two complementary research methods: the automatized methods of multidimensional analysis and the classic method of qualitative content analysis. The concept of ‘societal threat’ has been associated most frequently with specific large-scale societal issues that are tainted by uncertainty, and a range of primarily negative feelings and emotions. Additionally, the conceptually and semantically close concepts of ‘risk’ and ‘danger’ have not been associated, on the lexical basis, with the concept of ‘societal threat’. The implications for an improved systemic understanding of actual societal threats’ features and related social psychological applications are discussed.

Comparable analysis of flavor compounds and quality assessment of fermented bean curd using HS-SPME-GC/MS and colorimetric sensor array

This study aims to develop an effective tool by integrating traditional analysis methods with advanced sensor array technology and multiple algorithms, to achieve rapid characterization, qualitative identification, and flavor quality evaluation of fermented bean products. Firstly, the flavor compounds and flavor profiles of fermented bean curd from different manufacturers were identified using headspace solid-phase microextraction gas chromatography-mass spectrometry (HS-SPME-GC/MS) and quantitative descriptive analysis (QDA). A total of 63 volatile compounds were identified, of which 13 volatile compounds were identified as the key differential compounds in selected samples. Eight descriptors were selected to represent the aroma characteristics of fermented bean curd. Based on the key differential compounds, a low-cost colorimetric sensor array (CSA) was designed and constructed. Furthermore, the feasibility of CSA combined with multiple algorithms was investigated for brand discrimination and flavor quality assessment. Linear discriminant analysis (LDA) exhibited a better performance in distinguishing different flavor quality samples, and the recognition accuracy of its prediction set was 97.22%. The study suggests that CSA combined with multiple algorithms can be an effective tool for flavor characterization and qualitative discrimination of fermented bean curd, and can provide a new multi-dimensional analysis method for food analysis.

A novel impact indentation technique with dynamic calibration method for measurement of dynamic mechanical properties

High strain rates and continuous real-time data acquisition are difficult to balance in existing impact indentation techniques. Meanwhile, the inaccuracy of the testing P-h curves limits the development of quantitative applications for dynamic testing of mechanical properties at micro region. This article develops a momentum exchange impact indentation testing method, which extends the testing range of strain rate to above 105 s−1, and at the same time avoids secondary impact. Impact indentation tests are conducted on single-crystal aluminum specimens with impact velocity from 76.9 to 176 mm/s. Based on calibrated data, the comparison of calculation methods for dynamic hardness is carried out. The dynamic elastic modulus is obtained by means of the Oliver-Pharr method, which increases from 71.9 to 89.4 GPa with increasing impact velocity. A multi-dimensional data analysis method is proposed based on the obtained experimental data to quantitatively study the damping and stiffness characteristics of materials. This testing method is particularly useful for quantitatively obtaining the micro region dynamic mechanical properties of solid materials at high strain rates.

A Comparative Study on the Operational Efficiency of Joystick and Touch Screen Control Methods for Sugarcane Harvesters

Reducing cumbersome mechanical control components is one of the trends of modern agricultural machinery towards a high degree of automation. Whether the control components of sugarcane harvesters can gradually be replaced by central control screens, similar to car cabins, is an unresolved question. At the level of human machine interaction, this involves comparing the efficiency between joystick and touch screen control. This paper conducts a simulated experiment to compare and study the efficiency and user experience of joystick and touch screen control in adjusting the topper and base cutter heights of sugarcane harvesters, aiming to provide a reasonable basis for the design of control interfaces in sugarcane harvester cabins. The electromyographic signals, experiment duration, and subjective cognitive evaluations of participants in both the topper and base cutter groups were analyzed. The results showed that the efficiency, learnability, and ease of use of different control methods varied under different operational tasks. For the topper that corresponds to the operating behavior and height transformation in real time, joystick control demonstrated superior ease of use and operational efficiency compared to touch screen control, with weaker learnability. There was no discernible difference in muscle activation levels between the two control methods. Consequently, joystick control is deemed more suitable for the height adjustment of the topper. Regarding the base cutter with non-real-time mapping of operating behavior and height changes, no significant disparity in ease of use and learnability was observed between the two control methods. Touch screen control yielded lower muscle activation levels and garners higher overall subjective cognitive scale ratings. Thus, touch screen control is considered more suitable for base cutter height adjustment. Lastly, the paper proposes the optimal combination of software and hardware for control components in sugarcane harvester cabins, and provides an objective and multidimensional experimental analysis method for future research on similar human machine interaction interfaces.

Sustainability Analysis of Domestic Raw Water Supply in Bandung City of Indonesia

The availability and sustainability of good quantities and qualities of water supplies for human needs and support development should be warranted; therefore, existing water resources should be managed sustainably. A multidisciplinary rapid appraisal method called multidimensional scaling (MDS) is an approach for a comprehensive analysis of the sustainability statuses of domestic water supplies. This study aims to analyze the index and sustainability status of raw water management from three dimensions of sustainability. The results that were obtained from a specific multidimensional scaling analysis method called Rapid Appraisal for Air Baku (Rapaku) are expressed in the form of indices and sustainability statuses. Based on different dimensions of the sustainability status review, the analysis results showed that Bandung’s domestic raw water was “less sustainable” (42.34%). Of the 35 attributes that were analyzed, there were 13 sensitive attributes that affected the index and sustainability status with a very small error at a 95% confidence level.

Sustainable Fishery Value Chain (SFVC): Study Case in Tilapia Industry

This research is built on the concept of sustainable value chains, where a framework for sustainable value chain analysis emerges that covers not only economic aspects but also includes environmental and social impacts in a collaborative model of value chain management. It explores the dimensions on which value chain sustainability should focus. It illustrates how and why it is necessary to take a broader perspective in ensuring the internal economic sustainability of the chain in line with its external socio-environmental consequences. This research aims to analyze the sustainability of the tilapia processing value chain. This research uses the Multi-Dimensional Scaling analysis method supported by Rap-fish software. The Rapfish methodology is a flexible technique that rapidly assesses fisheries status by integrating ecological and human factors according to identified norms. The results of this research show that the sustainability of the tilapia processing value chain is considered entirely sustainable from economic, social, and environmental aspects, with a sufficient sustainability index of 74.40%. The sustainability of the value chain relies on four key actors: tilapia cultivators, collectors, processors, and distributors. The sustainable tilapia processing value chain strategy in North Sumatra can limit floating net cages by considering the environmental carrying capacity and supported by suitable policies and active stakeholder participation in community-based legal institutions. This research found this is a strong sustainability concept if implemented with better governance and policy management in the tilapia industry value chain.

Two-Dimensional Fluctuation Correlation Spectroscopy (2D-FlucCS): A Method to Determine the Origin of Relaxation Rate Dispersion.

Relaxation rate dispersion, i.e., nonexponential or multicomponent kinetics, is observed in complex systems when measuring relaxation kinetics. Often, the origin of rate dispersion is associated with the heterogeneity in the system. However, both homogeneous (where all molecules experience the same rate but inherently nonexponential) and heterogeneous (where all molecules experience different rates) systems can exhibit rate dispersion. A multidimensional correlation analysis method has been demonstrated to detect and quantify rate dispersion observed in molecular rotation, diffusion, solvation, and reaction kinetics. One-dimensional (1D) autocorrelation function detects rate dispersion and measures its extent. Two-dimensional (2D) autocorrelation function measures the origin of rate dispersion and distinguishes homogeneous from heterogeneous. In a heterogeneous system, implicitly there exist subensembles of molecules experiencing different rates. A three-dimensional (3D) autocorrelation function measures subensemble exchange if present and reveals if the system possesses static or dynamic heterogeneity. This perspective discusses the principles, applications, and potential and also presents a future outlook of two-dimensional fluctuation correlation spectroscopy (2D-FlucCS). The method is applicable to any experiment or simulation where a time series of fluctuation in an observable (emission, scattering, current, etc.) around a mean value can be obtained in steady state (equilibrium or nonequilibrium), provided the system is ergodic.

Linguistic variation in supreme court oral arguments by legal professionals: A novel multi-dimensional analysis

This study uses the method of novel Multi-Dimensional Analysis to compare the discourses of justices, appellant’s attorneys, and respondent’s attorneys to provide a corpus-based description of linguistic co-occurrence patterns in their registers during oral arguments based on the extracted seven functional dimensions: (1) Instructive argumentation versus Informational production; (2) Elaborative exposition; (3) Concern with degree; (4) Concern with projection; (5) Narrative versus Non-narrative expression; (6) Impersonal expression; and (7) Stance-focused expression. Three profession-based legal corpora, totaling 32,107,839 words, were built using case transcripts from oral arguments between 1979 and 2014. The results show that justices are more argumentative, concerned with degrees, projection-, and stance-focused than attorneys. Attorneys are more informative, elaborative, narrative, and impersonal than justices. Among attorneys, appellant’s attorneys are relatively more informative, elaborative and impersonal, and less projection-concerned than respondent’s attorneys. This study has implications for MD analysis, courtroom discourse analysis, language pedagogy, and accounting research.

Multidimensional sentiment analysis method on social media data: comparison of emotions during and after the COVID-19 pandemic

PurposeThis study aims to elucidate the dynamics of social media discourse during global health events, specifically investigating how users across different platforms perceive, react to and engage with information concerning such crises.Design/methodology/approachA mixed-method approach was employed, combining both quantitative and qualitative data collection. Initially, thematic analysis was applied to a data set of social media posts across four major platforms over a 12-month period. This was followed by sentiment analysis to discern the predominant emotions embedded within these communications. Statistical tools were used to validate findings, ensuring robustness in the results.FindingsThe results showcased discernible thematic and emotional disparities across platforms. While some platforms leaned toward factual information dissemination, others were rife with user sentiments, anecdotes and personal experiences. Overall, a global sense of concern was evident, but the ways in which this concern manifested varied significantly between platforms.Research limitations/implicationsThe primary limitation is the potential non-representativeness of the sample, as only four major social media platforms were considered. Future studies might expand the scope to include emerging platforms or non-English language platforms. Additionally, the rapidly evolving nature of social media discourse implies that findings might be time-bound, necessitating periodic follow-up studies.Practical implicationsUnderstanding the nature of discourse on various platforms can guide health organizations, policymakers and communicators in tailoring their messages. Recognizing where factual information is required, versus where sentiment and personal stories resonate, can enhance the efficacy of public health communication strategies.Social implicationsThe study underscores the societal reliance on social media for information during crises. Recognizing the different ways in which communities engage with, and are influenced by, platform-specific discourse can help in fostering a more informed and empathetic society, better equipped to handle global challenges.Originality/valueThis research is among the first to offer a comprehensive, cross-platform analysis of social media discourse during a global health event. By comparing user engagement across platforms, it provides unique insights into the multifaceted nature of public sentiment and information dissemination during crises.

Unbiased Multivariate Multiscale Sample Entropy

The development of multi-channel data acquisition techniques has provided richer prior information for studying the nonlinear dynamic characteristics of complex systems. However, conventional nonlinear feature extraction algorithms prove unsuitable in the context of multi-channel data. Previously, the Multivariate Multiscale Sample Entropy (MMSE) algorithm was introduced for multi-channel data analysis. Although the MMSE algorithm generalized the multiscale sample entropy algorithm, presenting a novel method for multidimensional data analysis, it remains deficient in theoretical underpinning and suffers from shortcomings, such as missing cross-channel correlation information and having biased estimation results. In this paper, unbiased multivariate multiscale sample entropy algorithm (UMMSE) is proposed. UMMSE increases the embedding dimension from <i>M</i> to <i>M</i> + <i>p</i>. This increasing strategy facilitates the reconstruction of a deterministic phase space. By virtue of theoretical scrutiny grounded in probability theory and subsequent experimental validation, this paper illustrates the algorithm's effectiveness in extracting inter-channel correlation information through the integration of cross-channel conditional probabilities. The computation of similarities between sample points across different channels is recognized as a potential source of bias and instability in algorithms.Through simulation experiments, this study delineates the parameter selection range for the UMMSE algorithm. Subsequently, diverse simulation signals are employed to showcase the UMMSE algorithm's efficacy in extracting both within-channel and cross-channel correlation information. Ultimately, this paper demonstrates that the new algorithm has the lowest computational cost compared with traditional MMSE algorithms.

TAXONOMIC ANALYSIS OF UNEMPLOYMENT IN EU COUNTRIES IN THE CONTEXT OF SUSTAINABLE DEVELOPMENT

Unemployment is one of the most important problems affecting people in all countries. It affects the functioning of entire economies, industries, and enterprises; it also reduces the level and quality of people’s lives. The dynamic changes that are taking place in the labor market are forcing entrepreneurs to adopt a flexible approach to labor market resources – that is, employees – and also to choose appropriate forms of human capital management. This may lead to a reduction in unemployment in the market. Consequently, tackling unemployment is one of the important challenges facing the European Union, especially on its way to achieving sustainable development goals. High levels of unemployment could significantly hamper the achievement of the Agenda 2030 employment targets (SDG8), leading to poverty, social and economic inequality, and social instability. Therefore, the subject of this study is unemployment in EU countries; the main objective is to examine unemployment levels across EU member states. The deliberations and analysis are carried out in the context of the Agenda 2030 Goal 8 (SDG 8) for sustainable development. Taking into account the different degrees of implementation of SDG8 by individual Member States, and thus the different results in this respect, the study finds differences and similarities among the surveyed Member States in terms of types and levels of unemployment. The research is based on Eurostat data for 2022, and uses multidimensional comparative analysis methods such as cluster analysis and linear ordering. A review of selected sources on international and European law focuses on employment policy standards, including the problem of unemployment; this serves as a background for the analyses. The considerations are embedded in the concept of sustainable development, which influences the functioning of entire economies, but also in the processes of enterprise management. Based on the literature and the results of the analyses, it is concluded that levels of unemployment vary greatly in the analyzed countries. The best conditions for availability and accessibility of work are found in the Czech Republic and Malta; the worst are in Spain and Greece, where unemployment is very high.

Poverty reduction as a factor of ensuring food security in Kazakhstan

Object: One of the key factors of economic security is the economic availability of food. This directly affects the problem of poverty reduction. Today, in Kazakhstan, the decline in real incomes of the population threatens the possibility of meeting the need for high-quality nutrition of citizens. The purpose of the study is to assess the level of poverty in Kazakhstan and develop measures to reduce it.Methods: The research methods were: systematic, the method of economic and statistical data processing, expert evaluation, the method of multidimensional comparative analysis of the level of poverty in the context of the regions of Kazakhstan and the republic as a whole.Findings: It is revealed that Kazakhstan ranks 32nd in the ranking of 113 countries in the world on food security. This is largely determined by the decrease in the availability of food products as a result of the lagging income of the population from the increase in food prices. The decrease in the economic availability of food occurs as a result of the increase in poverty indicators. The methodology for assessing poverty based on the consumer basket does not reflect the real picture and needs to be adjusted. The underestimation of the subsistence minimum directly determines the amount of social payments from the state budget for vulnerable segments of the population, which further aggravates the growth of the number of poor.Conclusions: Based on the analysis, a number of recommendations aimed at combating poverty, including organizational, legislative and financial instruments, are presented. In particular, it is proposed to revise the methodology for assessing the consumer basket; set the poverty line by the subsistence minimum, as is customary in most countries and recommended by international experts; develop state programs to promote youth employment; increase the responsibility of local executive bodies for the implementation of employment programs and the development of regional employment centers.

The level of renewable energy used in EU member states - A multidimensional comparative analysis

This paper assesses EU countries in terms of their level of renewable energy use. The ranking of 27 EU countries in terms of the complex phenomenon under study was constructed on the basis of 7 variables. The research period was 2011 and 2020. The research method was one of multidimensional comparative analysis methods, i.e. Hellwig’s pattern development model. The research results confirm that there wasa positive change in the use of renewable energy in all EU countries in 2020 compared to 2011, with Sweden, Austria, Finland, and Denmark taking the lead. However, most EU countries (16 in 2011 and 17 in 2020) have a medium-low level of use of “green energy”. A low level was identified in Cyprus and Malta in 2011 and Poland in 2020.

A Study on a New Way of Thinking Based on Linear Programming to Analyze Civic Education in Colleges and Universities

Abstract This paper intends to construct a new multi-dimensional preference analysis model for learners by using the improved linear programming method of multi-dimensional preference analysis (LINMAP) to get the weight vector of attributes under the situation that the weights of attributes are completely unknown and for the uncertain multi-attribute decision-making problem that the elements of decision-making matrix are triangular fuzzy numbers, and the preference information is ordered pairs. The data of subjective or objective Civics education learning preferences are aggregated, and the statistical analysis of the data is carried out using SPSS.21 to generate the Civics education learning preference input degree weighting system. The results show that the offline behavioral input weight in the college Civics course is 0.5769, which is greater compared to the online input. And the attribute weights of course Civics preference are calculated to get Z max = 2.29, Z min = 0.73. Multiple feasible solutions can be obtained by taking any value between [0,1]. Based on the development law of the times, this paper makes a new thinking on ideological and political education and actively explores a new road suitable for the development of ideological and political education in colleges and universities.

Analisis Pemetaan Perseptual Dan Preferensi Kunjungan Wisata Religi Kota Banjarmasin Di Masa Pandemi Covid-19

Analysis of Perceptual Mapping and References to Religious Tourism Visits in Banjarmasin City during the Covid19 Pandemic (Study on Tourists in Banjarmasin City). Supervised by Taharuddin. This study aims to identify and explain perceptual mapping, visit preferences, predictions of destination religious destinations in Banjarmasin City with perceptual mapping analysis and tourist visit preferences. This research is quantitative research using SPSS for Windows version 26 with Multidimensional scaling analysis method. The sample in this study used the Matchin & Campbell formula with a sample of 150 respondents. The analytical tools used are descriptive analysis, perceptual mapping analysis and thrustone case analysis. Based on the results of the analysis carried out, the results show that the destinations of the Sabilal Muhtadin Grand Mosque, Jami Mosque, and Habib Basirih Tomb which will be crowded Keywords: Perceptual Mapping, Visit Reference, Tourism and Religion

A novel visual representation method for multi-dimensional sound scene analysis in source localization problem

A visual representation method, DoAgram, for multi-dimensional sound scene analysis is suggested. The visual representation of the sound source localization result gives intuitive information about the estimated one to the end user. Also, image-based deep learning is popularly used in the acoustic field nowadays, so such a visualized one can be used for data augmentation. To analyze the spatial sound scene for the moving source, the method displays the estimated azimuth angle and elevation angle of the source, and its corresponding time stamp and frequencies as RGB color channels and metadata by mapping the spatial coordinate to color space. Even though the suggested method is human-interpretable, decoding is needed for the quantitative analysis. Therefore, the time and frequency scanning method, and a histogram to estimate the DoA of the source are proposed. An experiment is conducted in an anechoic chamber to localize two quadcopter drones that have a mean angular velocity of 8°/s ± 9°/s (95 % CI) and 25°/s ± 31°/s (95 % CI), respectively, and the spatial sound scene analysis is implemented using the proposed methods. The test result shows that the trajectories with respect to the time of each source are well separated. Also, an additional test is conducted using an open-access audio dataset for machine learning. The cumulative source mapping method is adopted for the spatial sound scene analysis, and the decoded result shows that the DoAgram is feasible to adopt for machine learning applications.

Incorporating Multidimensional Data Analysis Methods for Probability Theory and Mathematical Statistics Teaching Reform and Practical Exploration

Abstract Based on the new curriculum reform and big data technology, this paper uses the radial function and RBF neural network algorithm in the multidimensional data analysis method to obtain the center, variance and output layer power of the neurons of Probability Theory and Mathematical Statistics. Construct the teaching evaluation model of Probability Theory and Mathematical Statistics according to the RBF neural network algorithm and screen 25 secondary indicators from the three aspects of teachers, students, and course content, thus forming the teaching evaluation index system of Probability Theory and Mathematical Statistics. Determine the evaluators and evaluation methods, according to the specific implementation of the evaluation. The evaluation model of “Teaching Probability Theory and Mathematical Statistics” based on the RBF neural network is tested for reliability and validity. The results show that the evaluation values of 25 indicators in the indicator system of the RBF-based evaluation model for the teaching of Probability Theory and Mathematical Statistics are all out of the range of 8.010-9.0, and |u 1| = 16.392≥2.241, |u 2| = 10.052≥2.241 in the examination scores of Probability Theory and Mathematical Statistics from the second semester of the academic year 2018/2019 to the second semester of the academic year 2020/2021, i.e., the examination scores of the course for the five semesters as a whole do not obey a normal distribution. This study enables students to better master the theoretical knowledge of Probability Theory and Mathematical Statistics, which is of great significance to the educational reform and practical exploration of Probability Theory and Mathematical Statistics in colleges and universities.

MORPHOLOGY OF FEMALE SKULLS OF THE 17th – 18th CENT. FROM BATURYN

This article deals with the publication and the comparative analysis of the 17th – 18th cent. female cranial series from town of Baturyn found during excavations by V. Kovalenko, O. Kovalenko, Yu. Sytyi, V. Mezentsev, V. Skorokhod during 2005–2015 on the territory of the Fortress (the Cathedral of the Life-Giving Trinity) and in resedimentation. Computer software designed by B. Kozintsev and O. Kozintsev in 1991 has been applied in the research. 14 craniometric traits defined by R. Martin have been involved into the analysis.
 The objective of this paper is to introduce a new material into scientific domain and define the place of the cranial series among famous anthropological types. To achieve this goal, it is necessary to provide the general morphological description of female group from Baturyn of the 17th – 18th cent. on the background of neighbouring ethnic groups from Ukraine and Eastern Europe (synchronistic and diachronic methods), determine the distance between separate cranial series of the 14th – 19th cent. with the help of multidimensional canonical and cluster analysis, and to find out the place of female population from Baturyn in the system of craniological types of Eastern Europe.
 For the first time, the general female series from Baturyn of the 17th – 18th cent. including 33 skulls has been created. After comparison of square divergence of 69 basic traits and indexes of Baturyn female skulls with the standard ones, one can conclude that the researched selection is heterogeneous by its structure. The multidimensional canonical and cluster comparative analyses of the studied female group of the 17th – 18th cent. with the synchronic and earlier series from the 14th – 19th cent. from Eastern Europe (except the Caucasus) have demonstrated its closest similarity to the urban skulls from Podillia.
 The comparison of 37 studied groups of the 14th – 19th cent. with the method of multidimensional canonical analysis in three-dimensional space has demonstrated the connection of the female skull sample from the town of Baturyn with two urban groups from Podillia.

The cultivation path of vocational talents in rural revitalization industry based on multidimensional data analysis method in the context of industry-education integration

Abstract This paper firstly analyzes the demand for industrial technical talents and industrial vocational talents training specifications for rural revitalization and proposes a vocational talents training model for the rural revitalization industry based on the integration of industry and education. Secondly, a multidimensional talent training evaluation index system is constructed, and then a network hierarchy analysis of the evaluation index system is conducted using algorithms such as evaluation matrix, entropy value method and game theory combination assignment. Finally, quantitative data analysis was carried out on the indicator weights and the evaluation of indicators using County A of City B as an example. The results show that the overall score of the talent cultivation index evaluation system in County A is 79.96, among which the evaluation scores of first-level indicators are 16.73, 10.56, 21.42, 7.89, 17.06 and 6.30.

Exploring the Significance of Heritage Preservation in Enhancing the Settlement System Resilience

The sustainable development of urban and rural settlements is considered one of the primary objectives of economic, social, and urban planning. Recent studies focused significant attention on the issue of the resilience of rural settlements due to the challenges and threats they face. One of the essential drivers affecting the resilience of rural settlements is cultural heritage preservation. The research methods were based on the complex systems approach. The study employed a multidimensional analysis method to evaluate the resilience of rural settlements based on five sub-systems: social, economic, environmental, physical, and managerial. The proposed method for evaluation of the settlement sustainability involves the application of a settlement development index and considers the preservation of cultural heritage objects. The data sources used contain open-access information and statistical data provided by municipal organizations, including GIS maps, statistical, and archival records. The study aim was to establish methodological frameworks for evaluating the influence of cultural heritage on the resilience of rural settlements as well as to obtain a quantitative assessment of the resilience of rural settlements within the Zavałočycy local council, located at Hlusk district, Mahiloŭ region, Belarus.