Tool For Analysis Of Systems Research Articles

Extending Generalized Explicit Terms and Applying Euler–Bernoulli Beam Theory to Enhance Dynamic Response Prediction in Receptance Coupling Method

This paper presents a theoretical framework to enhance the prediction of dynamic responses in complex mechanical systems, such as vehicle structures, by incorporating both translational and rotational degrees of freedom. Traditional receptance coupling methods often neglect rotational effects, leading to significant inaccuracies at higher frequencies. Additionally, approaches that implicitly include full dynamics frequently result in redundancy of generalized coordinates, especially at connection points. To address these limitations, the generalized receptance coupling method using Frequency-Based Substructuring is extended to explicitly account for rotational dynamics resulting in a refined GRCFBS approach. This extension enhances both the understanding and prediction of system responses, which are represented through the receptance matrix or Frequency Response Function. Building on Jetmundsen’s foundational work, the proposed framework introduces a practical, generalized formulation that explicitly incorporates full translational and rotational dynamics at each substructure node. This explicit definition provides deeper insights into system behavior, particularly for complex interactions between substructures under weak and strong coupling scenarios at interface points. The Euler–Bernoulli beam theory is employed to model rotational behavior at critical points, yielding reduced-order and explicit receptance matrices for substructures in the coupling process. The methodology’s accuracy and applicability in capturing resonance and anti-resonance modes are validated through two case studies: the coupling of two flexible subsystems and the integration of flexible and rigid components. Results are benchmarked against numerical finite element analysis, and all limitations and potential improvements are discussed. By directly incorporating rotational dynamics directly, this approach enables more reliable dynamic response predictions under multi-directional loading conditions, particularly for vehicle and machinery system design. The GRCFBS method offers a versatile and reliable tool for dynamic system analysis, with significant potential for vibration analysis over a broad frequency range.

Effect of electromagnetic radiations on the dynamics of a five-chain coupled inertial Hopfield neural network and control of multi-stability with the selection of a desired attractor

Abstract In this paper, we investigate the effect of electromagnetic radiation on the dynamics of a network consisting of five chain-coupled inertial Hopfield neurons. The study revealed that the neural system designed such a way involves several complex phenomena, namely Hopf bifurcation, chaos, hyperchaos and the coexistence of up to thirty-two attractors in phase space. The complexity specific to our system is due to the higher number of equilibrium points, namely two hundred and forty-three. Under the conditions of safe functioning of our neural system (chaos or hyperchaos), we have been able to observe that electromagnetic radiation has a harmful character for the system, because we found for a range of variation in the intensity of the electromagnetic feedback induction current a coexistence of chaotic and periodic states (epileptic state). We then controlled the multi-stability using a desired attractor selection scheme, with the aim of suppressing the pathological state. All the work carried out in this contribution is done with the help of dynamical system analysis tools such as the bifurcation diagram, the spectrum and the maximum exponent of Lyapunov, phase portraits and basins of attraction. The scheme used is the Runge-Kutta-4. In order to validate the numerical results, we use analog calculation and some results were derived from the Pspice software. These results are in good accordance in amplitude and location on the plane to those of numerical simulations.

Electric vehicles in transition: Opportunities, challenges, and research agenda – A systematic literature review

This study conducts a systematic literature review on electric vehicle (EV) adoption, mapping critical themes and presenting an integrated framework to advance understanding of EVs' role in sustainable transportation. Drawing on 917 Scopus-indexed articles and 23 stakeholder interviews, it explores economic, environmental, energy, and social (EEES) dynamics through the Theory-Context-Methodology (TCM) framework and causal loop diagrams. Findings reveal dominant theories, such as the Theory of Planned Behavior and Value-Belief Norm Theory, and underscore the importance of methodological approaches like regression analysis and structural equation modeling. Key research themes include adoption intention, green infrastructure, and sustainable transport policy, all aligning with Sustainable Development Goals (SDGs) 7 (Clean Energy), 11 (Sustainable Cities), and 13 (Climate Action). By constructing a causal loop diagram, this study illustrates complex interrelations among EEES factors, highlighting the reinforcing and balancing influences on EV adoption behavior. The proposed integrated framework provides a roadmap for future research, identifying significant gaps and strategic directions for policymakers, industry stakeholders, and researchers. Practical implications include guidelines to foster EV integration into smart cities, addressing infrastructure and environmental challenges while encouraging policy incentives to enhance public adoption. The study's theoretical implications include the expansion of existing behavioral theories, the development of an integrated theoretical framework that combines TCM and CLDs, the introduction of CLDs as a tool for complex systems analysis, and the identification of thematic clusters within EV adoption research. This review supports informed decision-making for sustainable urban mobility, positioning EVs as transformative in the global shift toward eco-friendly transportation solutions.

Uncovering migration systems through spatio-temporal tensor co-clustering

A central problem in the study of human mobility is that of migration systems. Typically, migration systems are defined as a set of relatively stable movements of people between two or more locations over time. While these emergent systems are expected to vary over time, they ideally contain a stable underlying structure that could be discovered empirically. There have been some notable attempts to formally or informally define migration systems. However, they have been limited by being hard to operationalize and defining migration systems in ways that ignore origin/destination aspects and fail to account for migration dynamics over time. In this work, we propose to employ spatio-temporal tensor co-clustering—that stems from signal processing and machine learning theory—as a novel migration system analysis tool. Tensor co-clustering is designed to cluster entities exhibiting similar patterns across multiple modalities and thus suits our purpose of analyzing spatial migration activities across time. To demonstrate its effectiveness in describing stable migration systems, we first focus on domestic migration between counties in the US from 1990 to 2018. We conduct three case studies on domestic migration, namely, (i) US Metropolitan Areas, (ii) the state of California, and (iii) Louisiana, in which the last focuses on detecting exogenous events such as Hurricane Katrina in 2005. In addition, we also examine a case study at a larger scale, using worldwide international migration data from 200 countries between 1990 and 2015. Finally, we conclude with a discussion of this approach and its limitations.

Highlighting the Urgent Need for Reforms in the Face of Financial Failures at State-Owned Enterprises in Iraq

General Background: The financial analysis of companies' reports is critical for assessing their past performance, forecasting future results, and identifying operational strengths and weaknesses. Specific Background: Financial failure, which occurs when a company cannot generate adequate returns to cover costs and capital expenses, poses significant risks to stakeholders. For state-owned companies, the implications of financial failure are especially profound, given their integral role in national economies, such as Iraq's. Knowledge Gap: There is limited understanding of the extent to which state-owned companies in Iraq utilize financial analysis to predict financial failure, particularly in the context of economic transitions and privatization pressures. Aims: This study aims to evaluate the dependence of Iraqi state-owned companies on financial analysis as a predictive tool for financial distress and to identify the key factors contributing to their financial failures. Results: The research reveals that 42% of the companies examined have experienced financial failure, as measured by the Argenti index. Contributing factors include employment challenges, insufficient financing, and the transition from a socialist to a capitalist economy, which has left companies vulnerable to privatization. Novelty: The study provides novel insights into how economic transition impacts the financial stability of state-owned companies, highlighting the practical application of the Argenti index in diagnosing financial weaknesses. Implications: The findings underscore the urgent need to support state-owned enterprises by revitalizing the industrial sector, improving administrative efficiency, and adopting more systematic financial analysis tools. Additionally, the research recommends creating an accessible information center for investors and creditors, which would enhance transparency and support decision-making. This approach could help mitigate financial distress and attract new investment, ultimately contributing to Iraq’s economic recovery. Highlights: Financial analysis helps predict failures in state-owned companies. 42% of companies fail, based on the Argenti index. Privatization and economic shifts increase financial risk. Keywords: Financial analysis, Argenti index, state-owned companies, financial failure, privatization

Analysis of load flow based on power system analysis software package by using the IEEE 30-bus system

Abstract A power-flow study is a numerical analysis to judge the stability of the power system during operation. By analyzing the results of the load flow calculations, the transmission line, the load distribution, the power factor, the equipment capacity, the distribution network, and the power quality and reliability of the distribution network are improved. This paper uses the IEEE 30-Bus system as an example of building a modified system via the power system analysis software package (PSASP) for the power-flow study. Other simulation tools are validated by using the MATPOWER toolbox and MATLAB/Simulink. The result of the load flow calculation for the three simulation tools is almost consistent, so the practicability of the tool is proved. It is verified that the PSASP is one of the reference simulation tools for power system analysis.

Probing the inner structure and dynamics of pH-sensitive block copolymer nanoparticles with nitroxide radicals using scattering and EPR techniques

This research emphasizes the crucial role of electron paramagnetic resonance (EPR) spectroscopy in nanoparticle analysis, showcasing its distinctive ability to probe molecular-level details. We demonstrate the synthesis and self-assembly of a new class of pH-responsive amphiphilic diblock copolymers, specifically poly[N-(2-hydroxypropyl)-methacrylamide]-block-poly[2-(diisopropylamino)ethyl methacrylate] (PHPMA-b-PDPA), incorporating 2,2,6,6-tetramethylpiperidine-1-oxyl (TEMPO) radicals covalently bound to the hydrophilic PHPMA segment. The copolymer synthesis involved a three-step process, combining reversible addition − fragmentation chain transfer (RAFT) polymerization with carbodiimide (DCC) chemistry. TEMPO radical-containing nanoparticles (RNPs) were created using microfluidic (MF) nanoprecipitation, a technique essential for generating uniform nanoparticles with predictable biodistribution and cellular uptake. Adjusting MF protocol parameters allowed fine-tuning of RNP sizes. EPR spectroscopy confirmed the formation of core–shell RNPs, with features aligning closely with those obtained from scattering techniques like dynamic light scattering (DLS), static light scattering (SLS), small-angle X-ray scattering (SAXS), and cryo-transmission electron microscopy (cryo-TEM). EPR spectroscopy emerges as a potent tool for molecular-level analysis of colloidal polymer systems. This study highlights the EPR-spin label method’s efficacy in probing the internal structural features and dynamics of core–shell nanoparticles, especially their response to pH-triggered disassembly. The findings open new avenues for the use of pH-responsive TEMPO-labeled diblock copolymers in controlled drug delivery applications.

PENGARUH SISTEM INFORMASI AKUNTANSI MANAJEMEN, MOTIVASI KERJA, DAN KEPUASAN KERJA TERHADAP KINERJA MANAJERIAL

This study aims to see the effect of management accounting information systems, work motivation, and job satisfaction on the managerial performance of managers and supervisors who work at hotel service companies in the city of Bengkulu. This study uses a quantitative approach. Respondents in this study are managers and supervisors who work in five-star hotels in Bengkulu. The sample selection in this study using purposive sampling. Sources of data in this study using primary data with a questionnaire. Data analysis using multiple linear regression analysis using the SPSS program.The results of this study indicate that there is a positive effect of management accounting information systems, work motivation, and job satisfaction on managerial performance, which means that the better the application of management accounting information systems, providing work motivation, and job satisfaction can improve managerial performance.Theoretically, the results of this study support the contingency theory which states that in carrying out their duties, managers need conformity to be able to implement a particular strategy. Contingency theory is a theory that is used as an analysis tool for forms and systems of management accounting, work motivation, and job satisfaction that are useful for providing information to companies, one of which is information to be able to face competition.The limitation in this study is the reduction in the sample because most of the hotel service companies in the capital city are temporarily closed, thus narrowing the results of this study to the number of samples that do not include all hotels in Bengkulu City.

Tensor-Train Format Hierarchical Equations of Motion Formalism: Charge Transfer in Organic Semiconductors via Dissipative Holstein Models.

Hierarchical Equations of Motion (HEOM) in the Tensor-Train (TT) representation is applied to study the charge-transfer dynamics in organic semiconductors (OSCs). The theoretical formulation as well as the basic computational aspects of HEOM-TT are discussed in detail. Charge transfer in OSCs is modeled using dissipative polaronic models that incorporate the effects of both high- and low-frequency molecular vibrations, and it is simulated in a fully quantum and nonperturbative manner, which has not been studied intensively. The capability of treating complex electron-vibrational systems is examined by analyzing and comparing the numerical behavior of the time-dependent variational approach and the time-Alternating Minimal Energy methods and by calculating the current autocorrelation function and diffusivity across various models. Our results indicate that the HEOM-TT framework offers a robust tool for the detailed analysis of complex polaronic systems, suggesting its potential for broader applications.

Development of a DSP based collaborative computing platform for power system

Abstract With the advancement of information technology, there has been tremendous progress in data production, processing, and storage technology, especially in data processing technology. With the rapid development of the power grid, its operation has become more intelligent and efficient. Traditional power system simulation and analysis tools have disadvantages, such as slow message transmission and the inability to share data, which cannot meet the requirements of modern power system simulation calculations. DSP was introduced and developed by the Southern Power Grid Research Institute and can perform various simulation calculations such as transient stability calculation, power flow calculation, and short circuit calculation. It is widely used in departments such as power planning and design. However, the data calculated by DSP is input through a data card, which is cumbersome and difficult to manage. In order to meet the new requirements of power system simulation analysis, a power system collaborative computing platform has been developed based on DSP, which makes DSP data maintenance and management more efficient and intuitive. It comprehensively improves the efficiency of power grid planning work. This article provides a detailed analysis of the architecture design and application problems of the power system collaborative computing platform, clarifies the requirements of large-scale interconnected power grids for simulation computing data management, and proposes solutions that can meet the needs of simulation computing data management in China’s power grid from two aspects: key data architecture issues and software architecture technology.

A rule reasoning diagram for visual representation and evaluation of belief rule-based systems

Belief rule-based (BRB) systems are usually regarded as much more interpretable than those systems based on black-box techniques. Unfortunately, understanding the reasoning behavior of BRB systems is a complex task, although the use of linguistic variables and rules makes BRB systems have outstanding semantic expressiveness. In this study, a rule reasoning diagram, named RULING, is proposed as a tool for visual representation and exploratory analysis of the BRB systems. RULING describes the BRB system in the form of a network graph based on the interaction between the rules, which allows users to understand the behavior of the BRB system by examining the information represented graphically, and provides an effective means to improve the interpretability of BRB systems. In the meanwhile, some quantitative indicators based on the network structure and rule characteristics are proposed to evaluate the performance of rules and BRB systems. A classification case and a regression case are studied as examples to illustrate the feasibility and effectiveness of RULING. The results indicate that RULING can offer many possibilities for BRB system analysis and evaluation, such as identifying important rules, interpreting rule interactions, detecting inconsistent rules, and guiding the adjustment of the rule base.

Reconstruction of Eriocheir sinensis Protein-Protein Interaction Network Based on DGO-SVM Method.

Eriocheir sinensis is an economically important aquatic animal. Its regulatory mechanisms underlying many biological processes are still vague due to the lack of systematic analysis tools. The protein-protein interaction network (PIN) is an important tool for the systematic analysis of regulatory mechanisms. In this work, a novel machine learning method, DGO-SVM, was applied to predict the protein-protein interaction (PPI) in E. sinensis, and its PIN was reconstructed. With the domain, biological process, molecular functions and subcellular locations of proteins as the features, DGO-SVM showed excellent performance in Bombyx mori, humans and five aquatic crustaceans, with 92-96% accuracy. With DGO-SVM, the PIN of E. sinensis was reconstructed, containing 14,703 proteins and 7,243,597 interactions, in which 35,604 interactions were associated with 566 novel proteins mainly involved in the response to exogenous stimuli, cellular macromolecular metabolism and regulation. The DGO-SVM demonstrated that the biological process, molecular functions and subcellular locations of proteins are significant factors for the precise prediction of PPIs. We reconstructed the largest PIN for E. sinensis, which provides a systematic tool for the regulatory mechanism analysis. Furthermore, the novel-protein-related PPIs in the PIN may provide important clues for the mechanism analysis of the underlying specific physiological processes in E. sinensis.

Finding a closest saddle–node bifurcation in power systems: An approach by unsupervised deep learning

We propose a neural network using an unsupervised learning strategy for direct computation of closest saddle–node bifurcations, eliminating the need for labeled training data. Our method not only estimates the worst-case load increase scenarios but also significantly reduces the computational complexity traditionally associated with this task during inference time. Simulation results validate the effectiveness and real-time applicability of our approach, demonstrating its potential as a robust tool for modern power system analysis.

Sustainable Production and Consumption from Human Needs

Objective: Understand sustainable production and consumption from human needs. Its objectives are: to describe sustainable production from human needs, to explain the relationship between human needs and sustainable consumption Theoretical Framework: Main concepts and theories that support research from human needs and sustainable production and consumption are presented, finding strong relationships with ecological limits and circular economy, providing a solid basis to understand the context of the research. Method: Qualitative, documentary analysis technique and systematic analysis tool of the content of scientific documents according to prism declaration, grouping publications and compiling the most prominent concepts. Results and Discussion: For sustainable production based on human needs: socioeconomic, political and environmental factors, circular economy, sustainable production models and ecological limits; results for the relationship between human needs and sustainable consumption: motivators, patterns and determinants in purchasing behavior, promotion of responsible lifestyles and education for sustainable consumption. Research Implications: Sustainable production and consumption contribute to satisfying the needs of communities from the context and that the consumer and producer must recognize the limits of the planet and satisfiers which allow having a responsible lifestyle and that promote well-being. Originality/Value: Understand that although the need to be satisfied must be clear, it is essential to recognize its satisfaction from the environmental, social, and economic dimension. Its relevance lies in the relationship of elements found in the collected publications to visualize sustainable production and consumption as a strategy aimed at promoting sustainable lifestyles from the consumer's awareness, considering biophysical limits, social justice, and economic sustainability

The Evaluation of Water Loss in The Western North Tarum Irrigation Channel

The Western North Tarum Irrigation channel plays a crucial role in supplying water to irrigated agricultural areas. However, it encounters challenges in ensuring adequate water delivery to all channel segments. This research assesses water loss during the distribution process in the Western North Tarum Irrigation channel, focusing on the evaluation of channel capacity and the impact of suboptimal water management on water loss. The study employs the HEC-RAS model, a hydraulic system analysis tool, to evaluate the channel’s capacity and simulates its behavior under various discharge conditions. The evaluation reveals that two channel segments, B.TUB 13 and B.TUB 25, have exceeded their capacity limits, resulting in overflow. Sedimentation downstream, particularly in these segments, exacerbates the issue by altering the channel slope and impeding water flow. This research identifies poor water distribution management as a significant factor contributing to water loss in the irrigation channel. Inadequate scheduling and the absence of proper water measurement tools result in instances of overwatering or underwatering in some areas. The lack of monitoring and control in the irrigation system hampers the detection of uncontrolled flow in the channels, leading to substantial water loss and inefficient water use. This research underscores the importance of evaluating and maintaining irrigation channel capacity to prevent overflow and water loss. Furthermore, it emphasizes the significance of effective water management to achieve more efficient water distribution and irrigation. Addressing these challenges is crucial for ensuring the long-term sustainability of irrigated agriculture in the Western North Tarum Irrigation area.Keywords: HEC-RAS, Irrigation Channel, Water Distribution, Water Loss, Western North Tarum

A computerized tool for the systematic visual quality assessment of infant multiple-breath washout measurements.

Multiple-breath washout (MBW) is a sensitive method for assessing lung volumes and ventilation inhomogeneity in infants, but remains prone to artefacts (e.g., sighs). There is a lack of tools for systematic retrospective analysis of existing datasets, and unlike N2-MBW in older children, there are few specific quality control (QC) criteria for artefacts in infant SF6-MBW. We aimed to develop a computer-based tool for systematic evaluation of visual QC criteria of SF6-MBW measurements and to investigate interrater agreement and effects on MBW outcomes among three independent examiners. We developed a software package for visualization of raw Spiroware (Eco Medics AG, Switzerland) and signal processed WBreath (ndd Medizintechnik AG, Switzerland) SF6-MBW signal traces. Interrater agreement among three independent examiners (two experienced, one novice) who systematically reviewed 400 MBW trials for visual artefacts and the decision to accept/reject the washin and washout were assessed. Our tool visualizes MBW signals and provides the user with (i) display options (e.g., zoom), (ii) options for a systematic QC assessment [e.g., decision to accept or reject, identification of artefacts (leak, sigh, irregular breathing pattern, breath hold), and comments], and (iii) additional information (e.g., automatic identification of sighs). Reviewer agreement was good using pre-defined QC criteria (κ 0.637-0.725). Differences in the decision to accept/reject had no substantial effect on MBW outcomes. Our visual quality control tool supports a systematic retrospective analysis of existing data sets. Based on predefined QC criteria, even inexperienced users can achieve comparable MBW results.

Propagated (fragment) Pipek-Mezey Wannier functions in real-time time-dependent density functional theory.

Localization procedures are an important tool for analysis of complex systems in quantum chemistry, since canonical molecular orbitals are delocalized and can, therefore, be difficult to align with chemical intuition and obscure information at the local level of the system. This especially applies to calculations obeying periodic boundary conditions. The most commonly used approach to localization is Foster-Boys Wannier functions, which use a unitary transformation to jointly minimize the second moment of the orbitals. This procedure has proven to be robust and fast but has a side effect of often mixing σ- and π-type orbitals. σ/π-separation is achieved by the Pipek-Mezey Wannier function (PMWF) approach [Lehtola and Jónsson, J. Chem. Theory Comput. 10, 642 (2014) and Jónsson et al., J. Chem. Theory Comput. 13, 460 (2017)], which defines the spread functional in terms of partial charges instead. We have implemented a PMWF algorithm in the CP2K software package using the Cardoso-Souloumiac algorithm to enable their application to real-time time-dependent density functional theory. The method is demonstrated on stacked CO2 molecules, linear acetylenic carbon, boron and nitrogen co-doped graphene, and nitrogen-vacancy doped diamond. Finally, we discuss its computational scaling and recent efforts to improve it with fragment approaches.

Future of District Heating Systems – Investigation of Various Technologies in Danish Context

In response to the urgent need for decarbonization, district heating (DH) systems must explore emission-reducing investments that simultaneously lower operational costs of the plant. The recent years with wildly fluctuating gas and electricity prices put even more emphasis on finding the optimal combination of generation units for DH systems. This article aims at investigating which of the available technologies are the future of district heating. Solar collectors, photovoltaics (PV) and wind turbines and their combinations with other technologies are considered. The analysis is done based on Assens District Heating located in Funen, Denmark, where the annual heat production is approximately 96 000 MWh. The plant has already invested in an East-West oriented PV plant and a heat pump. However, for the purpose of this paper, an assumption is made that this investment can be redone based on the present investment costs in Denmark. The analysis is made in the energy system analysis tool energyPRO, where different combinations of technologies are analyzed. The study investigates the operational and investments costs, looking into the influence on Net Heat Production Cost (NHPC) and quantifying the investment yearly benefit. The results demonstrate profitability and feasibility of integrating renewable energy resources into district heating systems within the Danish context. The ambition is to showcase the possibilities and provide decision makers with insight into robust investments in renewable energy systems. Furthermore, by highlighting the success and potential of renewable energy integration in district heating systems in Denmark, this study aims to inspire further research and innovation in the field in other European countries, where the DH systems are primarily based on fossil fuels.

Analysis and Control of Partially Observed Discrete-Event Systems via Positively Constructed Formulas

This paper establishes a connection between control theory for partially observed discrete-event systems (DESs) and automated theorem proving (ATP) in the calculus of positively constructed formulas (PCFs). The language of PCFs is a complete first-order language providing a powerful tool for qualitative analysis of dynamical systems. Based on ATP in the PCF calculus, a new technique is suggested for checking observability as a property of formal languages, which is necessary for the existence of supervisory control of DESs. In the case of violation of observability, words causing a conflict can also be extracted with the help of a specially designed PCF. With an example of the problem of path planning by a robot in an unknown environment, we show the application of our approach at one of the levels of a robot control system. The prover Bootfrost developed to facilitate PCF refutation is also presented. The tests show positive results and perspectives for the presented approach.

An approach for assessing the functional vulnerabilities criticality of CPS components

Timely identification of critical security flaws in a cyber-physical system makes identifying risks and potential threats possible. To address this issue, threat models are created to better understand potential vulnerabilities that must be considered to ensure system reliability. Selecting the optimal solution for assessing the functional vulnerabilities criticality of cyber-physical system components is a complex process since all vulnerabilities must be identified, classified, and quantified according to a unified approach as part of the cybersecurity process. An effective tool for cyber-physical systems analysis is the Bayesian attack graph. Each path in the graph represents a sequence of attacks that an attacker can use to achieve a specific goal, such as gaining access to sensitive data or controlling a system. This paper proposes a quantitative method for assessing the vulnerability criticality of cyber-physical system components based on the Promethee II multi-criteria decision-making method. It allows ranking and identification of the system's most vulnerable components. The proposed approach is evaluated using a threat model and three scenarios of cyberattacks on a cyber-physical system. Comparison with TOPSIS, VIKOR, and ELECTRE methods proves the effectiveness of the proposed approach. The proposed approach can help technical specialists make more reasoned decisions when ranking critical vulnerabilities of cyber-physical system components to provide security measures and prevent cyberattacks.