Graphical Modeling Method Research Articles

A Network Analysis of the Relationship Between Automation and Job Characteristics

The purpose of this study is to identify core occupational characteristics that affect the potential for a job to be automated and to reveal the relationship and paths between those characteristics, such as skill(s), work environment and knowledge. In this study, a network was estimated and visualised to identify the relationship between occupational characteristics using the Gaussian graphical model method and to reveal the occupational characteristics linked to the potential for automation variable. As a result of estimating and visualising the network, it was found that the occupational characteristics with low potential for automation were art, decision-making, assisting and caring for others and working in cramped spaces, while those with high potential for automation involved repeating the same tasks. Furthermore, after analysing the shortest paths from each occupational characteristic node to the automation node, it was revealed that even occupational characteristics with high potential for automation can have varying levels of automation possibility depending on the mediators. Based on the results, education and vocational training policies for low- and middle-skill workers, young workers and potential workers are suggested.

А comprehensive approach to managing robot group formation

Subject matter: Research and development of methods for controlling swarms of unmanned aerial vehicles (UAVs) based on the "master – slave" model. This includes examining existing classifications and interactions between unmanned aerial vehicles in various formations such as groups, flocks, associations, and swarms, with the goal of creating an effective management system. Goal To improve the quality of interaction between unmanned aerial vehicles based on the "master – slave" model during flight missions through constant control between objects. Ensuring reliable execution of flight missions by implementing new management methods that account for different modes of interaction between devices. Tasks: Analyze the classification of existing UAVs; analyze the parameters and model of interaction of unmanned aerial vehicles in existing groups, flocks, associations, swarms; create a scenario of interaction between two UAVs based on the "master – slave" model; develop a program for visualizing the flight of unmanned aerial vehicles based on the "master – slave" model; conduct flight testing according to the proposed model on stages with various geospatial objects. Methods: Simulation method for developing a UAV flight visualization subsystem; graphical modeling method for creating an aircraft-type unmanned aerial vehicle model; methods of algorithm theory for developing a scenario of interaction between two UAVs. Utilization of specialized software tools for visualization and simulation of UAV behavior in real-time conditions. Results: Developed a classification of unmanned aerial vehicles; created a graphical model of the Mini-Flight-M aircraft; developed a scheme for the interaction of two UAVs in "teacher" or "mentor" modes; created a program for visualizing the flight of UAVs based on the "master – slave" model; conducted flight testing according to the proposed model on stages with various geospatial objects. The results confirmed the effectiveness of the developed model and demonstrated its applicability in various fields, including environmental monitoring, rescue operations, and other autonomous missions. Conclusions: The proposed approach to controlling a UAV swarm based on the "master – slave" model improves the quality of interaction between the devices and ensures reliable execution of flight missions. Further research should focus on optimizing energy consumption and ensuring reliable communication between swarm agents. It is also important to develop methods for protecting UAV swarms from cyberattacks and other threats to enhance their resilience and reliability during complex missions.

Research on the construction method of virtual mapping digital twin system for substation

Abstract At present, there are problems in the process of fault location and cause analysis in the actual work of substations, such as long time consumption, high investment in manpower and material resources, and low returns. A Digital Twin (DT) system construction method that can be used for virtual mapping in substations is proposed to address this problem. Firstly, the design principles and overall structure of the substation DT system were formulated by comprehensively considering the needs of transient modeling, debugging, and verification calculations in the substation. Then, the construction method and process of the three-dimensional twin model of the primary system (PS) in the substation were analyzed by using SolidWorks and Unity3D, and the graphical modeling method and process of the general protection and secondary circuit logic modules were analyzed by using the Cybersim integrated simulation support system. Finally, a three-dimensional twin model of the PS and secondary system (SS) equipment inside the substation was constructed. The reliability of the proposed method was verified through simulation experiments, and the results showed that the fault identification accuracy, fault location accuracy, and fault cause judgment accuracy were the highest during bus faults, with 96.10%, 94.17%, and 92.93%, respectively. The capacitor had the lowest accuracy, with 94.86%, 92.96%, and 91.73%, respectively. The DT system can accurately achieve functions such as status viewing and alarm of the substation, precise positioning and navigation of equipment, and positioning of faulty and abnormal equipment.

Identification of Criteria for Enabling the Adoption of Sustainable Maintenance Practice: An Umbrella Review

The evolution from traditional industrial maintenance to sustainable maintenance (SM) is pivotal within an existing industrial ecosystem. This study, utilising an umbrella review (UR), critically examines this transition, highlighting its increased importance in maintenance decision-making (MDM). Using a sample (n = 20) of reviews, we synthesised meta-, methodological-, and content-based evidence and performed bibliometric, thematic and statistical analyses. For the bibliometric and thematic/conceptual analyses, we used the R bibliometrix package. The results show that the early research focuses mainly on theoretical aspects, while recent studies examine the practical implications. Also, comprehensive studies evaluating the benefits of implementing environmental and social aspects within MDM are still lacking. For that reason, we switched the attention to content-based data, from which we identified 43 distinct criteria discussed. For the analysis of criteria, the Bayesian Network Analysis with Gaussian Copula Graphical Model (BNA-GCGM) method was used. Although the evidence shows that environmental pollution, energy consumption and health and safety of workers are the most discussed criteria, the BNA-GCGM suggests that labour costs, resource consumption, employee satisfaction and energy consumption, among others, are the most influential criteria in the network analysis. Interestingly, after distinguishing studies into pre- and post-2021 research, the results show that pre-2021 research is primarily focused on economic and technical factors, reflecting a profit-oriented approach. The post-2021 analysis suggests a discernible shift towards more balanced considerations by incorporating social and environmental factors, suggesting a more socially responsible approach. Finally, while SM is gaining momentum, further empirical and practical research are required to demonstrate the advantages that SM offers in the light of the upcoming Industry 5.0.

FORMATION OF AN INNOVATIVE MODEL FOR THE DEVELOPMENT OF E-COMMERCE AS PART OF ENSURING BUSINESS ECONOMIC SECURITY

The purpose of the article is to formulate a methodological approach to building a model of e-commerce development in the context of ensuring the economic security of business. The object of the study is the business activity of socio-economic systems and their economic security. The scientific task is to model the stages and processes of e-commerce development in the framework of ensuring business economic security. The key problem is the lack of a clear methodological approach to modeling the development of e-commerce in the context of ensuring the economic security of business. A review of the literature showed that there is no single approach to solving this problem. The research methodology involves the use of functional modeling technology. This is achieved through a graphical modeling method. Based on the results of the study, the main model for the development of e-commerce in the framework of ensuring the economic security of business was presented. Each block has been modeled separately for better understanding. The scientific innovativeness of the presented research results consists in the presented methodological approach to determining the ways for the development of e-commerce in the framework of ensuring the economic security of a business.

Comorbidity network analysis using graphical models for electronic health records.

The comorbidity network represents multiple diseases and their relationships in a graph. Understanding comorbidity networks among critical care unit (CCU) patients can help doctors diagnose patients faster, minimize missed diagnoses, and potentially decrease morbidity and mortality. The main objective of this study was to identify the comorbidity network among CCU patients using a novel application of a machine learning method (graphical modeling method). The second objective was to compare the machine learning method with a traditional pairwise method in simulation. This cross-sectional study used CCU patients' data from Medical Information Mart for the Intensive Care-3 (MIMIC-3) dataset, an electronic health record (EHR) of patients with CCU hospitalizations within Beth Israel Deaconess Hospital from 2001 to 2012. A machine learning method (graphical modeling method) was applied to identify the comorbidity network of 654 diagnosis categories among 46,511 patients. Out of the 654 diagnosis categories, the graphical modeling method identified a comorbidity network of 2,806 associations in 510 diagnosis categories. Two medical professionals reviewed the comorbidity network and confirmed that the associations were consistent with current medical understanding. Moreover, the strongest association in our network was between "poisoning by psychotropic agents" and "accidental poisoning by tranquilizers" (logOR 8.16), and the most connected diagnosis was "disorders of fluid, electrolyte, and acid-base balance" (63 associated diagnosis categories). Our method outperformed traditional pairwise comorbidity network methods in simulation studies. Some strongest associations between diagnosis categories were also identified, for example, "diagnoses of mitral and aortic valve" and "other rheumatic heart disease" (logOR: 5.15). Furthermore, our method identified diagnosis categories that were connected with most other diagnosis categories, for example, "disorders of fluid, electrolyte, and acid-base balance" was associated with 63 other diagnosis categories. Additionally, using a data-driven approach, our method partitioned the diagnosis categories into 14 modularity classes. Our graphical modeling method inferred a logical comorbidity network whose associations were consistent with current medical understanding and outperformed traditional network methods in simulation. Our comorbidity network method can potentially assist CCU doctors in diagnosing patients faster and minimizing missed diagnoses.

HIV-1 Group M Capsid Amino Acid Variability: Implications for Sequence Quality Control of Genotypic Resistance Testing.

With the approval of the HIV-1 capsid inhibitor, lenacapavir, capsid sequencing will be required for managing lenacapavir-experienced individuals with detectable viremia. Successful sequence interpretation will require examining new capsid sequences in the context of previously published sequence data. We analyzed published HIV-1 group M capsid sequences from 21,012 capsid-inhibitor naïve individuals to characterize amino acid variability at each position and influence of subtype and cytotoxic T lymphocyte (CTL) selection pressure. We determined the distributions of usual mutations, defined as amino acid differences from the group M consensus, with a prevalence ≥ 0.1%. Co-evolving mutations were identified using a phylogenetically-informed Bayesian graphical model method. 162 (70.1%) positions had no usual mutations (45.9%) or only conservative usual mutations with a positive BLOSUM62 score (24.2%). Variability correlated independently with subtype-specific amino acid occurrence (Spearman rho = 0.83; p < 1 × 10-9) and the number of times positions were reported to contain an HLA-associated polymorphism, an indicator of CTL pressure (rho = 0.43; p = 0.0002). Knowing the distribution of usual capsid mutations is essential for sequence quality control. Comparing capsid sequences from lenacapavir-treated and lenacapavir-naïve individuals will enable the identification of additional mutations potentially associated with lenacapavir therapy.

Effective diagnosis of sepsis in critically ill children using probabilistic graphical model.

Probabilistic graphical model, a rich graphical framework in modelling associations between variables in complex domains, can be utilized to aid clinical diagnosis. However, its application in pediatric sepsis remains limited. This study aims to explore the utility of probabilistic graphical models in pediatric sepsis in the pediatric intensive care unit. We conducted a retrospective study on children using the first 24-hour clinical data of the intensive care unit admission from the Pediatric Intensive Care Dataset, 2010-2019. A probabilistic graphical model method, Tree Augmented Naive Bayes, was used to build diagnosis models using combinations of four categories: vital signs, clinical symptoms, laboratory, and microbiological tests. Variables were reviewed and selected by clinicians. Sepsis cases were identified with the discharged diagnosis of sepsis or suspected infection with the systemic inflammatory response syndrome. Performance was measured by the average sensitivity, specificity, accuracy, and area under the curve of ten-fold cross-validations. We extracted 3,014 admissions [median age of 1.13 (interquartile range: 0.15-4.30) years old]. There were 134 (4.4%) and 2,880 (95.6%) sepsis and non-sepsis patients, respectively. All diagnosis models had high accuracy (0.92-0.96), specificity (0.95-0.99), and area under the curve (0.77-0.87). Sensitivity varied with different combinations of variables. The model that combined all four categories yielded the best performance [accuracy: 0.93 (95% confidence interval (CI): 0.916-0.936); sensitivity: 0.46 (95% CI: 0.376-0.550), specificity: 0.95 (95% CI: 0.940-0.956), area under the curve: 0.87 (95% CI: 0.826-0.906)]. Microbiological tests had low sensitivity (<0.10) with high incidence of negative results (67.2%). We demonstrated that the probabilistic graphical model is a feasible diagnostic tool for pediatric sepsis. Future studies using different datasets should be conducted to assess its utility to aid clinicians in the diagnosis of sepsis.

Разработка интегрированной пространственно-временной базы данных, характеризующей последствия изменения климата

Currently, at the global and regional (national) levels, the expert community, as well as statesmen, have prioritized the task of practical implementation of the Sustainable Development Goals (characterizing the dynamics of a development of various parameters of socio-ecological and economic systems). Achieving these goals requires serious analytical work based on a deep and comprehensive analysis of a processes taking place in various spheres, as well as the formation of an appropriate information platform, including a set of databases that adequately describe changes taking place in various spheres of state activity. However, today, in Russian practice, this tool is practically absent, which significantly complicates the qualitative and quantitative analysis, assessment and forecasting of the processes of adaptation of Russian regions (including the population living in them) to consequences of global climate change. The purpose of this study is to form a database of indicators characterizing process of adaptation of socio-ecological and economic systems of a northern regions of Western Siberia (The Khanty-Mansiysk Autonomous Okrug and Yamalo-Nenets Autonomous Okrug) to global climate change. The projected database should become the basis for creating an open information resource for a wide range of users when they solve analytical and predictive tasks related to a socio-economic assessment of an impact of global climate warming on permafrost. To achieve the objectives of the study, a set of methods was used: theoretical generalization and comparison, formalization, algorithmization, structuring and grouping, economic and statistical methods (including correlation and regression analysis), cartographic method, graphical modeling method. As a result, we have formed a database representing a single, periodically updated repository containing spatio-temporal data sets characterizing processes and phenomena, on the one hand, socio-ecological and economic systems, on the other – climate change. Further work with the database assumes, on the basis of available quantitative data and methods of mathematical statistics, the identification of elements of the ecological, socio-economic systems of the KhMAO and YaNAO vulnerable to global climate change, as well as forecasting the dynamics of their (systems) transformation.

Bayesian Modeling of the Mnemonic Similarity Task Using Multinomial Processing Trees.

The Mnemonic Similarity Task (MST: Stark et al., 2019) is a modified recognition memory task designed to place strong demand on pattern separation. The sensitivity and reliability of the MST make it an extremely valuable tool in clinical settings. We develop new cognitive models, based on the multinomial processing tree framework, for two versions of the MST. The models are implemented as generative probabilistic models and applied to behavioral data using Bayesian graphical modeling methods. We demonstrate how the combination of cognitive modeling and Bayesian methods allows for flexible and powerful inferences about performance on the MST. These demonstrations include latent-mixture extensions for identifying individual differences in decision strategies, and hierarchical extensions that measure fine-grained differences in the ability to detect lures. One key finding is that the availability of a "similar" response in the MST reduces individual differences in decision strategies and allows for more direct measurement of recognition memory.

An Integrated Approach of Learning Genetic Networks From Genome-Wide Gene Expression Data Using Gaussian Graphical Model and Monte Carlo Method.

Global genetic networks provide additional information for the analysis of human diseases, beyond the traditional analysis that focuses on single genes or local networks. The Gaussian graphical model (GGM) is widely applied to learn genetic networks because it defines an undirected graph decoding the conditional dependence between genes. Many algorithms based on the GGM have been proposed for learning genetic network structures. Because the number of gene variables is typically far more than the number of samples collected, and a real genetic network is typically sparse, the graphical lasso implementation of GGM becomes a popular tool for inferring the conditional interdependence among genes. However, graphical lasso, although showing good performance in low dimensional data sets, is computationally expensive and inefficient or even unable to work directly on genome-wide gene expression data sets. In this study, the method of Monte Carlo Gaussian graphical model (MCGGM) was proposed to learn global genetic networks of genes. This method uses a Monte Carlo approach to sample subnetworks from genome-wide gene expression data and graphical lasso to learn the structures of the subnetworks. The learned subnetworks are then integrated to approximate a global genetic network. The proposed method was evaluated with a relatively small real data set of RNA-seq expression levels. The results indicate the proposed method shows a strong ability of decoding the interactions with high conditional dependences among genes. The method was then applied to genome-wide data sets of RNA-seq expression levels. The gene interactions with high interdependence from the estimated global networks show that most of the predicted gene-gene interactions have been reported in the literatures playing important roles in different human cancers. Also, the results validate the ability and reliability of the proposed method to identify high conditional dependences among genes in large-scale data sets.

Cglasso: An R Package for Conditional Graphical Lasso Inference with Censored and Missing Values

Sparse graphical models have revolutionized multivariate inference. With the advent of high-dimensional multivariate data in many applied fields, these methods are able to detect a much lower-dimensional structure, often represented via a sparse conditional independence graph. There have been numerous extensions of such methods in the past decade. Many practical applications have additional covariates or suffer from missing or censored data. Despite the development of these extensions of sparse inference methods for graphical models, there have been so far no implementations for, e.g., conditional graphical models. Here we present the general-purpose package cglasso for estimating sparse conditional Gaussian graphical models with potentially missing or censored data. The method employs an efficient expectation-maximization estimation of an ℓ₁ -penalized likelihood via a block-coordinate descent algorithm. The package has a user-friendly data manipulation interface. It estimates a solution path and includes various automatic selection algorithms for the two ℓ₁ tuning parameters, associated with the sparse precision matrix and sparse regression coefficients, respectively. The package pays particular attention to the visualization of the results, both by means of marginal tables and figures, and of the inferred conditional independence graphs. This package provides a unique and computational efficient implementation of a conditional Gaussian graphical model that is able to deal with the additional complications of missing and censored data. As such it constitutes an important contribution for empirical scientists wishing to detect sparse structures in high-dimensional data.

Physics‐data‐fusion based decoupling model for coupled faults of complex electromechanical systems

AbstractCoupled faults are formed by the nonlinear coupling of multiple lower‐level faults in complex electromechanical systems (CES). Although fault decoupling plays a crucial role in locating fault cause and isolating fault components, it still faces challenges due to the harsh reality of common mode failure, networked propagation, and a lack of accurate fault mechanism knowledge in the fault coupling process. A novel physics‐data‐fusion‐based decoupling model for coupled faults of CES was proposed using standard meta components, rigorous formulation, and intuitive representation. First, a hierarchical graph representing the static complex decoupling model was defined by composing proposed meta models. Second, the dynamic model parameters inspired by the time‐varying fault characteristics were determined using real‐time operation data analysis. Then, based on a proposed numerical reasoning formula, the most likely fault cause was determined, which can also identify fault level by level. Finally, the decoupling model was proved to be reasonable and effective with an offshore wind turbine case. As a graphical modelling method, it handles the decoupling process by fusing static physics and dynamic data of coupled faults. While inheriting the benefits of conventional models, it overcomes the limitations of these existing methods for real‐time results. Moreover, the proposed method provided a foundation for tracing the root cause of performance fluctuations, fault detection, and isolation of CES.

Systematization of the Factors Influencing the Activity of Private Investors in the Russian Securities Market, in the Context of Professional Mediation

The article outlines the problem of mobilizing the resource potential of private investors through the mechanisms of professional mediation in the securities market. The significance of the processes of transformation of private savings into investments for the purposes of ensuring economic growth and the stability of the economic system of the Russian Federation is beyond doubt. This aspect becomes especially relevant in the context of the sanctions load. In view of the limited nature of Russian institutional investors and the decrease in the share of non-residents in the turnover on the Russian securities market, the emphasis is shifting to private investors. Professional intermediaries providing investors with the necessary services, investment products and infrastructure solutions are able to contribute to the sustainable presence of private investors in the market. The authors present a detailed analysis of the processes of development of private investment using the mechanisms of professional mediation in the securities market, identifying cause-and-effect relationships of the current dynamics for the period 2017–2021. The study made it possible to identify a number of factors that determine the presence and activity of private investors in the market. The systematization of these factors has been carried out, taking into account the possibility of professional intermediaries influencing the processes of attracting private investors to the market. Using a graphical modeling method in the form of an Ishikawa relationship diagram, the factors were prioritized both by groups of factors and within each group. The results of the study can be used to modify the activities of professional intermediaries in the securities market in order to maintain the growth rate of the number of private investors and maximize their activity.

A mission reliability modelling method for intuitively displaying time elements

In view of the problems that the commonly used reliability block diagram modelling methods can not directly reflect the operation time elements of units and that whether an unit can be repaired or not during mission period, this paper proposes a graphical reliability modelling method that can uniformly represent the two key elements of equipment unit reliability logic relationships and its operation times, so as to improve the comprehensiveness of the display elements of reliability model and the accuracy of the logical relationship of each unit. Finally, a comprehensive example shows the feasibility of this method.

Bayesian Networks based approach to enhance GO methodology for reliability modeling of multi-state consecutive-k-out-of-n: F system

As a success-oriented system reliability and safety analysis technique, GO methodology is widely used in the modeling and analysis scenarios of critical safety engineering systems with signal flow, such as nuclear, chemical industry, electric power transportation, wireless network and microwave communication network. Consecutive-k-out-of-n systems are also widely applied in these fields, but due to their relatively complex logic, there are no corresponding graphical modeling and calculation methods. In this paper, the basic GO methodology is extended to support the modeling and analysis of multi-state linear and circular consecutive-k-out-of-n: F system (MLC(k,n) &MCC(k,n)) models. Two new operators are defined in graphical modeling to represent the two types of complex system logic. The multi-state logic semantics of the new operators and mapping rules to Bayesian networks (BN) are given. And the programmable process is presented to transform the extended GO model into BN, and the reliability of the MLC(k,n)/MCC(k,n) is calculated. Finally, three cases are used to verify i) the correctness of the method supporting multi-state model, ii) the efficiency and ability of the algorithm, and iii) the forward calculation and reverse diagnosis of the MLC(k,n) and MCC(k,n) systems with external shared components.

A Scalable Strategy for the Identification of Latent-Variable Graphical Models

In this article, we propose an identification method for latent-variable graphical models associated with autoregressive (AR) Gaussian stationary processes. The identification procedure exploits the approximation of AR processes through stationary reciprocal processes thus benefiting of the numerical advantages of dealing with block-circulant matrices. These advantages become more and more significant as the order of the process gets large. We show how the identification can be cast in a regularized convex program and we present numerical examples that compares the performances of the proposed method with the existing ones.

F2G: A hybrid fault-function graphical model for reliability analysis of complex equipment with coupled faults

Reliability analysis plays a crucial role in revealing the failure causes and determining the improvement measures for reliability growth. However, reliability analysis of complex equipment with coupled faults still corresponds to a challenging task, due to unclear coupling mechanism and unsuitable analysis model. A down-top, deductive modeling method, named as fault-function graph (F2G), is proposed. First, the meta models are defined to normalize all the atomic faults, coupling relations and coupling forms in modeling. Next, an initial fault model is constructed based on typical fault-relations and coupling forms. Furthermore, the functional hierarchy of fault determined by IDEF0 is appended. Lastly, the rigorous modeling rules and computing processes are explained based on an actual case. As a graphical modeling method, it handles the coupling faults by integrating the system functional and fault information. Exploiting the advantages of conventional models, the coupling relations are quantified, and the false relations are detected based on functional constraints. Therefore, the proposed method can be used flexibly in the reliability modeling of coupled faults. Moreover, it provides a foundation for the comprehensive and dynamic reliability analysis and the failure mechanism mining of complex equipment, and it can be used in other engineering applications as well.

Research on Modeling Technology of Aircraft Hydraulic System Based on AMESim

With the development of electro-hydraulic integration technology, the control accuracy, complexity and dynamic response characteristics of aircraft hydraulic system actions have become a hot topic in the field of hydraulics. The traditional hydraulic system design method mainly uses the designer’s knowledge and experience to construct a dynamic system with real components, and then tests on the system, this method is more difficult to adjust parameters. Therefore, taking the hydraulic system of an aircraft as an example, the simulation model of each component of the system is established by using modeling and simulation technology and graphical modeling method, so as to provide support for aircraft fault diagnosis and prediction.

Conceptual approaches to the interaction of labor market entities and educational institutions in the Russian Federation within the ecosystem based on neural network mechanisms

The purpose of the study: to develop and describe, the process of functioning of a neural network system of expert justification of management decisions in the field of preparation of educational programs for promising activities using graphical modeling methods. Results: conceptual approaches to ensuring the interaction of labor market entities and educational organizations of the Russian Federation within the information and communication ecosystem based on neural network mechanisms described in the BPMN 2.0 notation have been proposed. The main subjects of the system have been characterized through the "pool" and "swimline" tools, their interaction through the "flow", "messages flows" tools, the main operations displayed through private processes data mining, IDSS, communication driven and document driven interaction. The scientific novelty of the study: the concept of strategic interaction between the subjects of the labor market and educational institutions of the Russian Federation based on automation of communication and the use of neural network mechanisms has been proposed.