Methods Of Multidimensional Analysis Research Articles

CLUSTERING OF RUSSIAN COMPANIES IN THE TECHNOLOGY SECTOR BASED ON MARKET AND FINANCIAL INDICATORS

The study is devoted to the clustering of Russian companies in the technology sector based on their financial performance and average share price for 2021-2023. The relevance of the work is due to the need to analyze the investment attractiveness and sustainability of companies in conditions of high volatility and the importance of technological progress for the economy and the achievement of technological sovereignty. The purpose of this study is to identify similarities and differences between companies in the technology sector by grouping them into clusters and analyzing changes in their distribution over time. To do this, the work collects and systematizes data on financial indicators and the average share price of Russian companies in the technology sector, uses clustering methods to identify groups of companies with similar characteristics, analyzes dynamic changes in the distribution of companies by clusters. The object of the study is companies in the technology sector operating in the securities market of the Russian Federation. The subject of the study is the process of clustering companies based on financial indicators and the average value of shares to identify the structural and dynamic characteristics of their economic activities. The methods of multidimensional statistical analysis, including hierarchical clustering, the k-means method, DBSCAN and spectral clustering, are used as a methodological basis. The results of the study showed that the use of various clustering methods is an effective tool for grouping companies in the technology sector. Clusters have been identified that demonstrate the similarities of companies in key financial parameters. The analysis showed significant changes in the distribution of companies by cluster in 2021-2023. The data obtained indicate a high level of differentiation of companies in terms of financial performance and average share price. The identified clusters make it possible to assess competitive advantages and potential risks in the technology sector, providing an opportunity for the formation of investment strategies. The use of multidimensional statistical analysis helps to identify factors affecting the activities of companies, which allows investors to make informed decisions.

A Comprehensive Exploration of Hellwig’s Taxonomic Measure of Development and Its Modifications—A Systematic Review of Algorithms and Applications

This paper presents an original and comprehensive investigation into the Taxonomic Measure of Development (TMD), introduced by Hellwig in 1968, enriching both its theoretical foundations and practical applications. It provides an overview of various variants of the Hellwig method, including their extensions and applications, while also exploring recent trends across multiple research domains. Primarily developed as a method for multidimensional analysis, TMD has evolved into a pivotal tool in multi-criteria decision-making. It is widely used for evaluating and ranking alternatives, particularly in the analysis of complex socio-economic phenomena and decision-making scenarios involving multiple criteria. This study systematically reviews the original algorithm and its subsequent extensions and modifications, including adaptations for fuzzy sets, intuitionistic fuzzy sets, and interval-valued fuzzy sets. Furthermore, it explores an integrated multi-criteria approach based on Hellwig’s method and its practical applications across various domains. This paper introduces an original approach by conducting a detailed, step-by-step analysis of the TMD framework. This process-oriented analysis is a novel contribution that sets this study apart from typical reviews based on statistical or bibliometric data. By examining key steps in the TMD framework—such as data collection, criterion weighting, data normalization, ideal value determination, distance calculation, and normalization factor—this paper highlights the method’s versatility in addressing complex, real-world decision-making problems. Although similar to the widely used Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method in its reliance on distance to evaluate alternatives, Hellwig’s approach is unique in focusing exclusively on proximity to an ideal solution, without considering distance from a negative ideal. This distinctive emphasis has led to numerous adaptations and extensions that address specific issues such as criterion dependencies, uncertainty, and rank reversal. The findings underscore the continued relevance of the Hellwig method, its recent extensions, and its growing international recognition.

Multidimensional multiscale complexity analysis of sediment dynamics in the Yanhe Watershed of the loess Plateau, China

The Yanhe Watershed, emblematic of the loess hilly-gully landscape and ecological fragility in China's Loess Plateau, has experienced significant soil erosion and extensive soil-water conservation measures. To elucidate sediment dynamics and identify influencing factors in this region from 1960 to 2020, a novel multidimensional multiscale complexity analysis (MMCA) method was developed, based on entropy and complexity perspectives. This method integrated the refined composite multiscale fuzzy entropy (RCMFE) with multidimensional complexity analysis, offering a nuanced evaluation of sediment complexity and its implications for water resource management and ecological restoration. The findings revealed two distinct stages of sediment complexity variations: 1971-1988 and 2000-present. During the first period, the operation of the Wangyao Reservoir predominantly influenced sediment dynamics, initially reducing sediment complexity through sediment interception but later increasing it during discharge phases, particularly at larger scales. After 2002, extensive vegetation restoration efforts significantly reduced sediment complexity but raised concerns about long-term ecosystem resilience. Over the past decade, urbanization and climate change have exacerbated sediment instability, especially over semi-annual scales. This study advocates for management strategies that prioritize ecosystem sustainability and address the challenges posed by climate change and urbanization, facilitating improved soil and water conservation efforts in the Yanhe Watershed and similar regions in the Loess Plateau.

Экономико-математическое моделирование устойчивого развития горных территорий

Introduction. Mountains territories play a key role in the sustainable development of the Russian economy, since the economy of flat territories largely depends on mountain resources. The successful solution of this problem largely depends on the methods used to form and implement the state financial policy. The purpose of the article is to develop a simulation management model that helps improve the efficiency of public financial decisions for sustainable development of mountainous areas of Russia using economic and mathematical methods of multidimensional data analysis. Statement of the problem and solution method. The task is to decompose regions of Russia, including mountainous territories, into clusters according to a vector assessment indicator that determines their ability to self-development, based on the application of the c-means method. Assessment of the development of mountainous territories. Currently, mountainous regions are lagging far behind in economic development. The authors have presented a list of subjects with mountainous terrain that are subject to subsidies. It is concluded that interbudgetary regulatory measures have a significant impact on changing the economic level of development of these territories. Research areas on the concepts of hard and soft budget constraints. It is shown that the works of Russian and foreign authors focus on issues of fiscal policy in solving spatial development problems. At the same time, the phenomenon of decentralization of public goods is considered as the most important factor in economic development. The author's position is formulated, which consists in assessing the feasibility of using an individual approach to the application of the concepts of «soft» and «hard» budget constraints based on the use of mathematical methods of multidimensional data analysis. Results and discussion. As a result of decomposition of Russian federal districts using the c-means method, clusters were built, which include territories with a high, medium and low degree of self-development. Recommendations are given for each cluster regarding the application of budget constraints concepts. Conclusion. The article proposes and tests an approach based on the use of economic and mathematical methods of multidimensional data analysis, which made it possible to develop a simulation model for making predictive decisions on interbudgetary regulation that contribute to increasing the effectiveness of financial policy for the sustainable development of mountainous areas in Russia. Suggestions for practical applications and directions for future research. The proposed approach, based on the application of economic and mathematical methods, allows for scientific and quantitative justification of decisions taken in the sphere of interbudgetary regulation and can be used in the process of financial policy forming aimed at sustainable development of mountainous regions of Russia.

The Relationship Between Foreign Language Anxiety and Self-Efficacy: A Study in Chinese College Students' English Learning

In the process of foreign language learning, learners will encounter various psychological phenomena, among which anxiety and self-efficacy are two psychological variables that have received much attention. Foreign language anxiety refers to the emotional states of tension, unease, and worry that learners experience during the process of learning a foreign language. This emotional state can have a negative impact on learners' learning outcomes. Self-efficacy refers to an individual's judgment of their ability to perform a specific task, directly affecting their behavioral motivation and level of effort. This study will analyze the impact of foreign language anxiety and self-efficacy on the effectiveness of foreign language learning among Chinese college students and emphasize the use of multidimensional analysis methods, including listening, speaking, reading, and writing, to analyze and summarize previous literature. Foreign language anxiety and self-efficacy show a negative correlation and together influence the effectiveness of college students' foreign language learning. In China, the anxiety subjects of college students are ranked from high to low as follows: speaking, listening, writing, and reading. This article will also summarize and analyze ways to improve foreign language anxiety and enhance self-efficacy from the perspectives of student self-regulation and teacher assistance.

Assessing the thermoeconomic performance of a solar-powered trigeneration system with an upgraded transcritical carbon dioxide unit

Assessing the thermoeconomic performance of a solar-powered trigeneration system with an upgraded transcritical carbon dioxide unit

The effect of rehabilitation measures on the severity of endotoxicosis and its relationship with changes in hemorheology in acute poisoning

Introduction. The use of multidimensional statistical analysis methods (factorial and cluster methods) to study the pathogenesis and sanogenesis of acute poisoning (AP) with psychopharmacological agents (pPPhA), neurotoxicants (pNT) and corrosive substances (pCS) did not allow us to judge the presence of a direct connection between the hemorhealogy system (HR). We received a positive answer to this question earlier with the help of pair correlation analysis (PCA). New data on the sanogenetic effect of the applied methods of rehabilitation treatment in terms of changes in the indicators of HR and endotoxicosis (ET) and the relationship between them are also interesting. The purpose of the work is to assess the initial state of the indicators of HR and ET and the effect on them of a combination of medicinal and non–medicinal products used as part of rehabilitation measures for AP. Material and methods. Paired correlation analysis of the block structure of the obtained correlation matrices was applied to 52 patients with severe pPPhA, pNT and pCS. This analysis established the relationship between two indicators – both before the start of rehabilitation measures (the use of ethylmethylhydroxypyridine succinate, laser hemotherapy, hyperbaric oxygenation and mesodiencephalic modulation in accordance with the type of AP), and after their completion, before the discharge of patients from the hospital. Results. Statistically significant correlations were established between the indicators of HR and ET both before the start of treatment (0.44–0.94, without sign) and after its completion (0.49–0.99, without sign), which allowed us to confirm at a higher level a direct link between the development of HR and ET disorders, as well as to draw a conclusion about the sanogenetic effect of rehabilitation treatment. Limitations. The study has no limitations for further use of this methodological approach. Conclusion. The obtained data can serve as a justification for the introduction into practice of the proposed methods of mathematical analysis of the results of treatment measures.

Application of advanced multidimensional analysis methods to study modulated structures of the Hyp-1/ANS protein complex

Application of advanced multidimensional analysis methods to study modulated structures of the Hyp-1/ANS protein complex

Assessing the Sustainable Development Level of the Tourism Eco-Security System in the Chengdu-Chongqing Urban Agglomeration: A Comprehensive Analysis of Dynamic Evolution Characteristics and Driving Factors

Based on the DPSIR framework, this study constructed an evaluation index system to assess the sustainable development levels of the tourism eco-security system (TESS-SDL) present in the Chengdu-–Chongqing urban agglomeration and synthesized multi-dimensional analysis methods to explore its spatiotemporal evolution characteristics and driving factors to provide an important theoretical and practical basis for promoting the sustainable development of the regional tourism eco-security system. The results showed the following: (1) From 2011 to 2021, the regional TESS-SDL was generally at a medium level and showed a trend of steady growth. Although the gap between cities was widening year by year, the speed of the TESS-SDL exceeded the speed of the widening gap, promoting the coordinated development of the regional TESS-SDL. (2) The spatial spillover effect of the regional TESS-SDL was obvious; however, the siphoning effects of Chongqing and Chengdu were strong, and the demonstration effect was insufficient. (3) The dynamic evolution process of the TESS-SDL shows a strong self-locking effect. The risk of downward development (lower sustainability) is greater than the potential for upward development (higher sustainability) and is significantly influenced by neighboring cities. (4) In terms for assessing the driving factors, open-door and green-development policies show positive facilitating effects, while the positive influencing capacities of information technology, economic development, and tourism are moving toward having negative effects; the influencing effect of technological innovation has transitioned from positive to negative.

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

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

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.

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

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

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.

Структурне факторне дослідження загроз економічній безпеці галузі в контексті стратегічних інноваційних рішень

The proposed article examines the application of economic and mathematical instruments for multidimensional data, namely factor analysis in the context of assessing the factor space of economic security of an industry. The research is aimed at identifying the key factors influencing the socioeconomic sustainability of the industry and disclosing their interaction. Methods of multidimensional factor analysis are used to analyze the structure and identify important variables, fostering the development of strategies for improving the economic security of the industry both as a whole and with individual components. The carried out research focuses on the use of multidimensional factor analysis models for structural assessment of threats and risks to the economic security of the industry. By identifying and analyzing key factors, the research aims to provide conclusions about the dynamics of interaction and mutual influence of the variables that determine the socioeconomic sustainability of the industry. Using factor analysis as a methodological approach, the research discloses the structural components and key variables that determine the economic security of the development of the studied sector of the food industry. The results obtained during the research are intended to become the basis for the development of strategic measures aimed at increasing and ensuring the economic stability of the industry, preventing threats and precluding losses from the impact of various unpredictable challenges and uncertainties of the external environment. The results of the research are important for clarifying the factor relationships in the industry, providing valuable conclusions for decision-making to managers, specialists and scholars who are engaged in assessing, analyzing, forecasting the level of economic risks, threats in the period of rapid transformations of the external and internal environment.