Construction Of Matrix Research Articles

ГЕНДЕРНЫЕ ОСОБЕННОСТИ ВОСПРИЯТИЯ ВИЗУАЛЬНОГО ОБЛИКА МЕДИЦИНСКИХ ОРГАНИЗАЦИЙ

The article presents the results of an analytical sociological research on the population’s opinion study in the gender aspect regarding the perception and approval of various attributes of the visual image of medical organizations in the Russian Federation. The research found that women are more likely than men to pay attention to design solutions for elements of a visual image. The construction of the Kano matrix made it possible to determine the most attractive attributes of medical imaging systems – a single design of pointers and the presence of graphic elements that received great approval from women. Accounting for gender balance in the formation and implementation of national health policy can become the basis for the development of multi-purpose strategies aimed at creating maximum compliance for both men and women in terms of the conditions for providing medical care.

Heterogeneous Traffic Flow Detection Using CAV-Based Sensor With I-GAIN

This study proposes using connected automatic vehicles (CAVs) as traffic flow detectors to collect and exchange traffic flow data for heterogeneous traffic management and control. The proposed method includes the construction of a mathematical matrix to represent the status of the road section, the use of unsupervised machine learning to evaluate traffic data, and an improved generative adversarial imputation net (GAIN) to evaluate and impute missing traffic data. Next-generation simulation (NGSIM) data are used to verify the accuracy and robustness of the proposed method. One of the primary innovations of this study is the use of GAIN, a deep learning framework based on generative adversarial networks (GANs), to impute missing traffic data. GAIN has been shown to be more robust and stable when handling incomplete heterogeneous data than existing imputation methods. Additionally, this study contributes to the field by proposing the use of CAVs as sensors to detect mixed traffic flow, which could lead to more efficient and accurate traffic management and control. Experimental results demonstrate that the proposed method outperforms existing imputation methods, with a normalized root mean squared error and symmetric mean absolute percentage error of less than 0.2/0.3 and 0.08/0.13 in I-80 and Lankershim Boulevard, respectively. The findings of this study have important implications for the development and implementation of connected and automated vehicle technologies in the field of transportation.

Lensing to employee attrition rate and internal movement in an organization: Application and intervention through Markov chain

The objective of the paper is to apply Absorbing Markov chain model in the study of employees’ attrition rate of an organisation over a discrete time interval. This requires transition probability matrix to be stochastic and Markov network to have at least one ‘absorbing state’. The paper addresses this problem by associating the employees leaving the organisation with a state, hereafter called ‘ exit state’, which is ascribed to ‘the absorbing state’. The physical interpretation of the probabilistic characteristic of an absorbing state logically relates it to a ‘void’ . The state of ‘void’ has no resource / element in the initial state of the system, and, in the final state, any resource/ element transitioned to this state becomes irretrievable, hence non-contributory to the organisation for all practical purpose. This concept supports the construction of stochastic matrix of Markov analysis. The dynamics of free markets and prevalent meaning of freedom- freedom from fear, freedom from scarcity and freedom from harsh subordination, as perceived by the present-day digital generation, are the reasons behind their good intention to exercise freedom of choosing and changing organisations. It is, therefore, a matter of concern for corporate leadership to ponder over how to retain talented personnel and keep watch over changing trends of employees quitting the organisations. The present paper demonstrates the successful application of our conceptual model by a case study of employees’ attrition rate of an MNC operating in India, discusses the causes of high attrition rate and provides suggestions to leadership for win-win interaction with the employees.

ДОСЛІДЖЕННЯ ТА ПРОГНОЗУВАННЯ ПОЗИЦІОНУВАННЯ ПІДПРИЄМСТВ НА РИНКУ ПИВА В УКРАЇНІ

The article is devoted to the research and forecasting of the positioning of enterprises on the beer market in Ukraine. The essence of the competitive position of the enterprise as its position in the market segment due to the more effective implementation of the competitive strategy chosen by it in relation to analogues is determined. The role of the domestic beer market for creating additional jobs in the field of retail trade, restaurant and hotel businesses has been determined. Trends in the development of the domestic beer market of Ukraine were studied. The dynamics of export and import of beer for 2011–2021 were analyzed, and trends in trade balance changes were determined. The impact of the prolongation of the drought in Europe on the change in the situation on the domestic beer market in Ukraine has been determined. The structure of the beer market by main producers is analyzed. A BKG matrix of beer market producers in 2021 was built and the features of their strategic development were determined. The essence and significance of forecasting the positioning of enterprises on domestic consumer markets is determined. The essence and features of forecasting the volume of product sales have been determined. These include the scientific justification of the forecasting process, the probabilistic nature of the research, and the need to use data from past years. The toolkit of trend analysis and data from the financial reports of independent auditors AbInBev Ephesus Ukraine PJSC, Carlsberg Ukraine PJSC, Obolon PJSC and Oasis CIS for 2015–2021 were used to forecast the volumes of their implementation in the near future. Based on the use of linear trend equations, forecasts of sales volumes of the main beer producers in Ukraine were formed. The structure of sales volumes of beer producers in Ukraine is predicted. The probability of the influence of some external factors on this trend is determined. They will be the result of changes in the global world. Significant changes in the market positions of the main beer producers are predicted thanks to the construction of the BKG matrix of the beer market producers in 2024. Measures are proposed to strengthen the market positions of enterprises in the brewing industry.

Estimating the Efficiency of Building Stones in Complex Fuzzy Environment

Rocks are importantly used as building materials. Here chemical, biochemical, clastic and organic sedimentary rocks are chosen for building stones. These rocks are tested for their efficiency in structure strength, toughness, weathering resistances and quality. Using complex picture fuzzy soft VIKOR method it is concluded that clastic sedimentary rocks is the best building stone. First a score function is defined and is used for the construction of complex picture fuzzy soft score matrix the proficiency of CPFS score function over the existing score function is established. Next entropy measure is determined from which weight values are calculated. Complex picture fuzzy soft positive and negative ideal solutions are found and hence the group utility and individual regret values are obtained. By making use of these values the compromising ranking solution is found.

Multiagent LQR-based Control Design and Gain tuning for Quadcopters Fleet

An LQR-based Control design and gain tuning strategies proposals for a multi-agent system are presented in this article, the agents are connected in an undirected graph. Controller gains tuning are adjusted by selecting the Q and R weighting matrices of the Linear Quadratic Regulator. Agreement (consensus) is one of the fundamental problems in multi-agent control, where a set of agents must agree on a joint state value. In the proposed design, first considering that the behavior of the agreement protocol is undirected and static, the main objective is to highlight the complexity of the relationship between the convergence properties of this protocol and the structure of adjacent interconnections. The effects on the formation due to static geometry are analyzed from the resulting data according to the proximity between the agents, where behavior and stability are analyzed based on the desired formation geometry through the construction of the Laplacian matrix.

РАЗРАБОТКА МЕТОДИКИ ОЦЕНКИ ФИНАНСОВОЙ УСТОЙЧИВОСТИ ПРЕДПРИЯТИЙ НЕФТЕГАЗОВОЙ ОТРАСЛИ СЕВЕРНОГО РЕГИОНА В УСЛОВИЯХ ЦИФРОВИЗАЦИИ

The subject of the study is the mechanisms and ways in which digitalization impacts the financial sustainability of oil and gas enterprises in the Northern region. The study aims to develop methods for assessing the financial sustainability of oil and gas enterprises in the Northern region under digitalization. The scientific novelty of the study includes the development of methods for assessing the financial sustainability of oil and gas enterprises in the Northern region under digitalization that are based on the calculation of integral indicators and the construction of an enterprise sustainability matrix, considering the factors that affect its financial stability in the context of digitalization.

Economic, Financial and Legal Assessment of Corporate Integration Association Activity

The content of the integration process of joint stock companies (JSC) is considered. The prerequisites for rational and reasonable selection of potential participants in the corporate integration association (CIA) based on forecasting and evaluation of the results of such association’s activities are summarized. Established peculiarities of organization and management of corporate integration associations (difficulties in obtaining, analyzing and using the information necessary for management; individual management functions duplication; ambiguity of the certain activities effectiveness assessment; centrifugal trends within the CIA, risks associated with association management decentralization). The need for a two-level assessment of the corporate integration association activities (at the local level within individual JSCs, and on a corporate scale) to solve the tasks of the CIA strategic management has been proved. Complex analysis directions of corporate integration association activities efficiency are defined, which should consist of two blocks: general analysis of CIA activities efficiency; analysis of the synergistic effect that is formed within the CIA. The indicators system composition of corporate integration association assessment based on construction of the basic studies matrix is substantiated. It is proved expediency of sources differentiation of CIA synergy occurrence into four main categories: increase of income, reduction of expenses, tax deductions reduction and additional investments reduction, and on the basis of which, if necessary, factor analysis is carried out to identify the achievement degree of a certain strategy, integration goals and, if possible, reserves detection of synergetic effect increase. The analysis directions of the CIA activities are determined based on determining the integration degree of various activities within the CIA, such as: the production potentials integration of CIA participants; financial and production potentials integration of various joint stock companies – participants of the corporate integration association; scientific potentials integration of CIA participants. The indicators composition for assessing the depth of integration of the corporate integration association participants is substantiated. An approach to determining the synergy effect in the corporate integration association formation to constant changes in the economic environment is proposed.

МЕТОДИЧНІ ТА ПРИКЛАДНІ АСПЕКТИ ВИКОРИСТАННЯ МАТРИЦІ BCG

The BCG method is a portfolio analysis tool. It is used to effectively manage a business. This method helps to identify which businesses should be invested in and which should be disposed of. Nowadays BCG method is a popular tool for planning corporate product portfolios. However, this method has come under a lot of criticism, with many academics drawing attention to methodological weaknesses of product portfolio analysis based on the BCG matrix. The relevance of further research of the BCG matrix method leads to methodological and practical issues. They arise when the assortment of the entity in a particular market is evaluated. The following research methods are used: monographic (in the study of theoretical and methodological foundations of the classical method Boston Consulting Group Matrix); dialectical, abstract-logical (for theoretical generalizations to identify methodological shortcomings and ways to overcome them, the formulation of conclusions), graphic and tabular (to present research results). We have proved that the use of the traditional BCG matrix methodology can be impossible in practice because of the lack of accurate data on competitors and their sales volumes of certain products. The main problems arising in the construction of the BCG matrix: the lack of clear criteria for classifying businesses to a particular market, the existence of alternative methodologies for calculating the market share of the product, the lack of unambiguous quantitative criteria for classifying products as «high» or «low» market share, the lack of unambiguous quantitative criteria for classifying products as having «high» or «low» growth rates, the failure to compare only two years to evaluate the market growth. The objectives of the study are to adapt the classical matrix for use in an uncertain market environment. The proposed refinements to the use of the BCG matrix deserve to be implemented in the practice of strategic management of companies. The results of the analysis using the modified BCG matrix can be used in financial strategic planning and in the development of the marketing strategy, in particular when planning advertising campaigns.

Sharing Multiple Secrets in XOR-Based Visual Cryptography by Non-Monotonic Threshold Property

Multiple-secret visual cryptography scheme (MVCS) and fully incrementing visual cryptography scheme (FIVCS) have the same functionality that different secrets are gradually revealed by stacking different numbers of shadows. In essence, MVCS and FIVCS are the same. However, both of the two schemes suffer from large pixel expansion and deteriorated reconstructed image quality. In addition, MVCS and FIVCS require intensive computations to create base matrices. In this research, we exploit the capacity of sharing multiple secrets in XOR-based VCS (XVCS). First of all, three efficient base matrix constructions are proposed for realizing the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(k, n)$ </tex-math></inline-formula> non-monotonic XVCS (NXVCS), where the secret image is only revealed by XOR-ing exact <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> shadows. The <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(k, n)$ </tex-math></inline-formula> -NXVCS is adopted to constitute the multiple-secret XVCS (MXVCS). Theoretical analysis on the proposed constructions is provided. Extensive experiments and comparisons are conducted to illustrate that the pixel expansion, the visual quality of recovered image and the efficiency of generating base matrices are significantly improved by the proposed MXVCS, while comparing to MVCS and FIVCS.

Assessment of the financial stability of insurance companies in Ukraine under new challenges

Introduction. Today, the activities of insurance companies, like any other economic entities, are subject to a number of risks caused by new socio-economic conditions in the world, the existence of a touchless economy, recurring pandemics, and military aggression against Ukraine. Consequently, all these external factors have a certain impact on the stability of individual insurers and the insurance market as a whole. Objective. The objective of this article is to assess the financial stability of insurance companies in Ukraine, determine changes in stability characteristics of insurers of different types based on their key performance indicators, and identify the factors that influenced the formation of stability during turbulent times. To achieve this objective, the following tasks were set: to formulate a methodological toolkit for assessing the financial stability of insurance companies and to conduct relevant calculations for several years in Ukraine. Methods. The study employs a methodological toolkit based on a combination of cluster analysis and multifactor analysis. The research also utilized data standardization methods, exploratory data analysis, correlation matrix construction, variance analysis, and others. Given the increased dynamics of the external environment and the constantly emerging new challenges for determining financially stable insurers, a set of indicators characterizing their activities was used, based on which coefficients were calculated to comprehensively reflect the state of insurance companies and their potential impact on their stability. For the study, over 30 insurance companies that operated in Ukraine as of the end of 2016, 2019, and 2022 were selected. The sample included insurance companies with different characteristics to reflect the situation in the insurance market as a whole, rather than just a specific segment. The initial data were formed by analyzing the reports of insurance companies. Results. As a result of the analysis, the examined insurance companies were divided into two clusters for each year. The first cluster consists of insurers with unsatisfactory indicators, while the second cluster consists of insurance companies with satisfactory coefficient values, thus being more financially stable. It was found that in recent years, there has been a decrease in the number of insurance companies, influenced, among other factors, by the COVID-19 pandemic, economic instability in the country, difficulties in complying with and implementing updated NBU requirements, and challenges in obtaining reinsurance protection and insuring Ukrainians who have emigrated. Moreover, the situation worsened after the introduction of martial law and the start of hostilities on Ukrainian territory. It was also revealed that the financial stability of insurance companies significantly depends on the coefficient of financial autonomy, the coefficient of loan capital concentration, the coefficient of financial dependency, and the stability of financing coefficient. The financial capability of insurance companies at present is dependent on the capital reserves and the structure and quality of their assets in the pre-war period.

ТРЕНДЫ ИННОВАЦИОННОЙ СЕРВИСНОЙ СИСТЕМЫ ПРЕДПРИЯТИЙ ИНДУСТРИИ ГОСТЕПРИИМСТВА В КОНТЕКСТЕ ДИВЕРГЕНТНОГО СЕРВИСА

The introduction of the concept of divergent service into the organizational and service process of hospitality industry enterprises, including sanatorium-resort complexes, is based on the basic trends of the hospitality industry, this is the study of consumer behavior and their recreational needs; the formation of a team of specialists with a creative focus for the organization of innovative service programs using recreational technologies and divergent approaches; the creation of conditions in parts of the design of the spatial environment to create points of attraction, atmospherics in symbiosis with a set of additional services, both service and medical. Divergent thinking determines the creative way of development – the matrix construction of organizational culture, convergent thinking determines the constructive way of development – the hierarchical construction of organizational culture. The conceptual approach in the organization of service processes will allow to differentially increase the index of customer satisfaction, as well as to navigate modern trends in the field of health tourism. The importance of assessing the real situation of the quality of service lies in the analysis of basic quality assessment tools and system monitoring of customer satisfaction, finding the most suitable divergent approaches to solving service tasks for a specific hospitality enterprise. Hospitality industry enterprises use traditional management and quality control systems in their organizational culture – a convergent approach. In the case of the divergent approach, it is used quite rarely, due to the fact that the management staff is not yet ready to embark on innovative tracks, this is caused by several factors: - risks associated with unreasonable financial costs; - lack of a creative team capable of solving current problems; - fear of moving away from the traditional management system, control and remuneration system; - weak motivation for professional growth and the difficulty of getting out of the template of the mental matrix of promotion and sales of sanatorium and resort products. The conceptual solution of the implementation of the divergent service program in the conditions of the sanatorium-resort complex, as shown by the practice of testing programs with an individual trajectory of recreation, has a fairly high rating from the point of view of consumers.

Noncoherent Massive MIMO With Embedded One-Way Function Physical Layer Security

We propose a novel physical layer security scheme that exploits an optimization method as a one-way function. The proposed scheme builds on nonsquare differential multiple-input multiple-output (MIMO), which is capable of noncoherent detection even in massive MIMO scenarios and thus resilient against risky pilot insertion and pilot contamination attacks. In contrast to conventional nonsquare differential MIMO schemes, which require space-time projection matrices designed via highly complex, discrete, and combinatorial optimization, the proposed scheme utilizes projection matrices constructed via low-complexity continuous optimization designed to maximize the coding gain of the system. Furthermore, using a secret key generated from the true randomness nature of the wireless channel as an initial value, the proposed continuous optimization-based projection matrix construction method becomes a one-way function, making the proposed scheme a physical layer secure differential MIMO system. An attack algorithm to challenge the proposed scheme is also devised, which demonstrates that the security level achieved improves as the number of transmit antennas increases, even in an environment where the eavesdropper can perfectly estimate channel coefficients and experience asymptotically large signal-to-noise ratios.

The use of the risk matrix method for assessing the risk of implementing rail freight services

The article presents a proposal for the use of a risk matrix for assessing the safety of the implementation of rail freight transport. The starting point for considerations related to risk assessment is the conditions arising from the business need and the obligation of entities operating in the railway market to ensure the safety of the implementation of freight tasks. The authors presented selected literature within the framework of the issues discussed. The layers of risk assessment, which include the analytical layer, the decision layer and the elimination layer, indicate the possibility of considering the problem of risk assessment from different perspectives. The identification of direct causes and consequences of undesirable events during the implementation of rail freight transport was also made. The article describes one of the methods of risk management which is the risk matrix. The authors stressed that the construction of a risk matrix should be preceded by an analysis of the factors that affect the safety of rail transportation. This is possible by determining their probability of occurrence and setting values for the consequences of adverse events. The article divides the assignment of levels of adverse events to a five-level risk matrix (slight, low, medium, high, very high). Also presented is a case study considering the risk assessment of rail freight transport in Poland using the risk matrix method for railway accidents in the area of railway lines. Thanks to the development of the risk matrix, a risk management strategy can be used. The last part of the article is a summary, which highlights the possibility of applying the presented approach to risk assessment among entities operating in the rail transport market.

GAGAM v1.2: An Improvement on Peak Labeling and Genomic Annotated Gene Activity Matrix Construction.

Single-cell Assay for Transposase-Accessible Chromatin using sequencing (scATAC-seq) is rapidly becoming a powerful technology for assessing the epigenetic landscape of thousands of cells. However, the sparsity of the resulting data poses significant challenges to their interpretability and informativeness. Different computational methods are available, proposing ways to generate significant features from accessibility data and process them to obtain meaningful results. Foremost among them is the peak calling, which interprets the raw scATAC-seq data generating the peaks as features. However, scATAC-seq data are not trivially comparable with single-cell RNA sequencing (scRNA-seq) data, an increasingly pressing challenge since the necessity of multimodal experiments integration. For this reason, this study wants to improve the concept of the Gene Activity Matrix (GAM), which links the accessibility data to the genes, by proposing an improved version of the Genomic-Annotated Gene Activity Matrix (GAGAM) concept. Specifically, this paper presents GAGAM v1.2, a new and better version of GAGAM v1.0. GAGAM aims to label the peaks and link them to the genes through functional annotation of the whole genome. Using genes as features in scATAC-seq datasets makes different datasets comparable and allows linking gene accessibility and expression. This link is crucial for gene regulation understanding and fundamental for the increasing impact of multi-omics data. Results confirm that our method performs better than the previous GAMs and shows a preliminary comparison with scRNA-seq data.

Hypergraph based semi-supervised symmetric nonnegative matrix factorization for image clustering

Semi-supervised symmetric nonnegative matrix factorization (SNMF) has been shown to be a significant method for both linear and nonlinear data clustering applications. Nevertheless, existing SNMF-based methods only adopt a simple graph to construct the similarity matrix, and cannot fully use the limited supervised information for the construction of the similarity matrix. To overcome the drawbacks of previous SNMF-based methods, a new semi-supervised SNMF-based method called hypergraph based semi-supervised SNMF (HSSNMF), is proposed in this paper for image clustering. Specifically, HSSNMF adopts a predefined hypergraph to build a similarity matrix for capturing the high-order relationships of samples. By exploiting a new hypergraph based pairwise constraints propagation (HPCP) algorithm, HSSNMF propagates the pairwise constraints of the limited data points to the entire data points, which can make full use of the limited supervised information and construct a more informative similarity matrix. Using the multiplicative updating algorithm, a discriminative assignment matrix can then be obtained by solving the optimization problem of HSSNMF. Moreover, analyses of the convergence, supervisory information, and computational complexity of HSSNMF are presented. Finally, extensive clustering experiments have been conducted on six real-world image datasets, and the experimental results have demonstrated the superiority of HSSNMF while compared with several state-of-the-art methods.

Efficient bone regeneration of BMP9-stimulated human periodontal ligament stem cells (hPDLSCs) in decellularized bone matrix (DBM) constructs to model maxillofacial intrabony defect repair

BackgroundBMP9-stimulated DPSCs, SCAPs and PDLSCs are effective candidates for repairing maxillofacial bone defects in tissue engineering, while the most suitable seed cell source among these three hDMSCs and the optimal combination of most suitable type of hDMSCs and BMP9 have rarely been explored. Moreover, the orthotopic maxillofacial bone defect model should be valuable but laborious and time-consuming to evaluate various candidates for bone regeneration. Thus, inspired from the maxillofacial bone defects and the traditional in vivo ectopic systems, we developed an intrabony defect repair model to recapitulate the healing events of orthotopic maxillofacial bone defect repair and further explore the optimized combinations of most suitable hDMSCs and BMP9 for bone defect repair based on this modified ectopic system.MethodsIntrabony defect repair model was developed by using decellularized bone matrix (DBM) constructs prepared from the cancellous part of porcine lumbar vertebral body. We implanted DBM constructs subcutaneously on the flank of each male NU/NU athymic nude mouse, followed by directly injecting the cell suspension of different combinations of hDMSCs and BMP9 into the central hollow area of the constructs 7 days later. Then, the quality of the bony mass, including bone volume fraction (BV/TV), radiographic density (in Hounsfield units (HU)) and the height of newly formed bone, was measured by micro-CT. Furthermore, the H&E staining and immunohistochemical staining were performed to exam new bone and new blood vessel formation in DBM constructs.ResultsBMP9-stimulated periodontal ligament stem cells (PDLSCs) exhibited the most effective bone regeneration among the three types of hDMSCs in DBM constructs. Furthermore, an optimal dose of PDLSCs with a specific extent of BMP9 stimulation was confirmed for efficacious new bone and new blood vessel formation in DBM constructs.ConclusionsThe reported intrabony defect repair model can be used to identify optimized combinations of suitable seed cells and biological factors for bone defect repair and subsequent development of efficacious bone tissue engineering therapies.

Automatic feature learning model combining functional connectivity network and graph regularization for depression detection

Depression has become a major health and economic burden worldwide. Electroencephalography (EEG) data has been used by a growing number of researchers to study depression. EEG-based functional connectivity (FC) features have emerged since they can account for the relationships between different brain regions. In this paper, the time–frequency analysis technique is introduced into the construction of the FC matrix. Specifically, instead of directly building the FC matrix from the EEG signals, the intrinsic time-scale decomposition (ITD) method is employed to mine the time–frequency information, and then the Pearson correlation is used to measure the FC between channels. The results show the significant differences in the FC networks between different groups. Furthermore, the graph-based adaptive least absolute shrinkage and selection operator model (GA-LASSO) is proposed in this paper to learn the discriminative features from the FC matrix, which is mainly achieved by adding both the adaptive L1 and graph regularized terms to the original least absolute shrinkage and selection operator (LASSO) model. The advantages of GA-LASSO come from the processing of discriminative weights of different features, and the connections between features by graph topology. In addition, the effectiveness of the proposed strategy of depression detection is validated on the open dataset MODMA, as well as the self-collected dataset called EDRA. The experimental results show that the current study sheds new light on the pathological mechanism of subclinical depression and suggests that EEG resting-state FC analysis may identify potentially effective biomarkers for its clinical diagnosis.

Health Risks and Country Sustainability: The Impact of the COVID-19 Pandemic with Determining Cause-and-Effect Relationships and Their Transformations

The authors investigated the coronavirus pandemic as a health and safety risk factor for sustainable state development. The main purpose is to determine the cause-and-effect relationships between the key spheres of society life: economic, financial–budgetary, political-–institutional. The authors hypothesize that these spheres influence each other and that this influence becomes more obvious and important to consider during significant shifts such as health threats and transformations in the public health system. As part of the calculations, the methodology of canonical regression analysis was used, which made it possible to evaluate the influence of a set of indicators using the construction of a correlation matrix. Aggregation of the complex of development indicators for each direction was carried out, and their mutual influence and degree of importance within each group of indicators was determined. The identified interdependencies are valuable for predicting the state of various industries in the future. It was concluded that there were no significant changes between the indicators of the analyzed components of a country’s development in the pre-and post-pandemic period. This makes it possible to state with a high probability that forecasting in the long-term perspective of a country’s development is possible based on the degree of interrelationships between the indicators of individual areas of development. Forecasting can also be based on the trends occurring in a specific related field to correct the upward or downward movement of a particular indicator, and to change the functioning of the complex system under the influence of threats to public health.

Airport flight ground service time prediction with missing data using graph convolutional neural network imputation and bidirectional sliding mechanism

Accurate flight ground service time prediction plays a key role in the efficient operation of airports, which can help managers make better resource scheduling optimization decisions, reduce flight delays and improve airport operation capabilities. However, since flight ground service data is typically acquired by sensor equipment or manually recorded, data missing and abnormalities caused by sensor failure or manual recording errors are an unavoidable occurrence, making the prediction problem challenging. Although missing values can be imputed, the traditional methods usually fill in missing values and then make predictions separately, which leads to a two-step process and increases the prediction error. Besides, the existing one-step methods still suffer some limitations and shortcomings: failure to integrate bidirectional spatial–temporal dependencies leads to unsatisfying model prediction accuracy and poor robustness under high data missing rates, which cannot be directly used in the study of flight ground service time prediction with missing values. To solve this problem, we presented a sliding bidirectional graph convention network model framework that capable of dealing predicting with missing values. First, we proposed a new adjacency matrix construction method to capture more comprehensive spatial–temporal dependencies and developed a graph convention network based missing value imputation unity (GCNM) to inference and filling missing features. Then, multiple GCNM units are stacked to develop a bidirectional module BDGCNM to realize the filling and prediction into one step. Finally, we introduced a variable sliding window mechanism to improve the model’s robustness. Extensive experiments on real airport data sets and PEMS traffic data sets show that our method is more accurate and robust than the current state-of-the-art.