Informatica

The Gene Ontology (GO) knowledge base provides a standardized vocabulary of GO terms for describing gene functions and attributes. It consists of three directed acyclic graphs which represent the hierarchical structure of relationships between GO terms. GO terms enable the organization of genes based on their functional attributes by annotating genes to specific GO terms. We propose an information-retrieval derived distance between genes by using their annotations. Four gene sets with causal associations were examined by employing our proposed methodology. As a result, the discovered homogeneous subsets of these gene sets are semantically related, in contrast to comparable works. The relevance of the found clusters can be described with the help of ChatGPT by asking for their biological meaning. The R package BIDistances, readily available on CRAN, empowers researchers to effortlessly calculate the distance for any given gene set.

The performance evaluation of public charging service quality is frequently viewed as the multiple attribute group decision-making (MAGDM) issue. In this paper, an extended TOPSIS model is established to provide new means to solve the performance evaluation of public charging service quality. The TOPSIS method integrated with FUCOM method in probabilistic hesitant fuzzy circumstance is applied to rank the optional alternatives and a numerical example for performance evaluation of public charging service quality is used to test the newly proposed method’s practicability in comparison with other methods. The results display that the approach is uncomplicated, valid and simple to compute. The main results of this paper: (1) a novel PHF-TOPSIS method is proposed; (2) the extended TOPSIS method is developed in the probabilistic hesitant fuzzy environment; (3) the FUCOM method is used to obtain the attribute weight; (4) the normalization process of the original data has adapted the latest method to verify the precision; (5) The built models and methods are useful for other selection issues and evaluation issues.

The purpose of this manuscript is to develop a novel MAIRCA (Multi-Attribute Ideal-Real Comparative Analysis) method to solve the MCDM (Multiple Criteria Decision-Making) problems with completely unknown weights in the q-rung orthopair fuzzy (q-ROF) setting. Firstly, the new concepts of q-ROF Lance distance are defined and some related properties are discussed in this paper, from which we establish the maximizing deviation method (MDM) model for q-ROF numbers to determine the optimal criteria weight. Then, the Lance distance-based MAIRCA (MAIRCA-L) method is designed. In it, the preference, theoretical and real evaluation matrices are calculated considering the interaction relationship in q-ROF numbers, and the q-ROF Lance distance is applied to obtain the gap matrix. Finally, we manifest the effectiveness and advantage of the q-ROF MAIRCA-L method by two numerical examples.

Modelling the reliability information in decision making process is an important issue to inclusively reflect the thoughts of decision makers. The Evaluation Based on Distance from Average Solution (EDAS) and Analytic Hierarchy Process (AHP) are frequently used MCDM methods, yet their fuzzy extensions in the literature are incapable of representing the reliability of experts’ fuzzy preferences, which may have important effects on the results. The first goal of this study is to extend the EDAS method by using Z-fuzzy numbers to reinforce its representation ability of fuzzy linguistic expressions. The second goal is to propose a decision making methodology for the solution of fuzzy MCDM problems by using Z-fuzzy AHP method for determining the criteria weights and Z-fuzzy EDAS method for the selection of the best alternative. The contribution of the study is to present an MCDM based decision support tool for the managers under vague and imprecise data, which also considers the reliability of these data. The applicability of the proposed model is presented with an application to wind energy investment problem aiming at the selection of the best wind turbine. Finally, the effectiveness and competitiveness of the proposed methodology is demonstrated by making a comparative analysis with the Z-fuzzy TOPSIS method. The results show that the proposed methodology can not only represent experts’ evaluation information extensively, but also reveal a logical and consistent sequence related to wind turbine alternatives using reliability information.

This study introduces a new multi-criteria group decision-making model in organ transplant transportation networks under uncertain situations. A new combined weighting approach is presented to obtain expert weights with various kinds of opinions by integrating similarity measure and subjective judgments of experts. Also, the CRITIC approach is given to obtain transportation criteria weights. Finally, a novel integrated ranking approach is proposed to calculate the rank of each alternative based on ideal point solution and relative preference relation (RPR) methods. This study regards an interval-valued intuitionistic fuzzy set to cope with the vagueness of uncertain conditions in a real case study.

In this paper, at first, we define the notion of general fuzzy automaton over a field; we call this automaton vector general fuzzy automaton (VGFA). Moreover, we present the concept of max-min vector general fuzzy automaton. We show that if two max-min VGFA are similar, they constitute an isomorphism. After that, we prove that if two VGFA constitute an isomorphism with threshold α, they are equivalent with threshold α, where $\alpha \in [0,1]$. Also, some examples are given to clarify these new notions.

Experience shows that Agile project management tools such as Atlassian Jira capture the state of EAS projects by relying solely on expert judgement that is not supported by any knowledge model. Therefore, the assessment of project content against strategic objectives and business domain features are not supported by any tool. This is one of the reasons why Agile project management still does not provide sufficient EAS project delivery results. In order to address this problem, the Enterprise Application Software (EAS) development using Agile project management is summarized in a conceptual model. The model highlights the knowledge used and indicates its nature (empirical or causal digitized). The modified Agile management process we have developed and described in previous works is based on causal knowledge models that supports EAS development and Agile management processes. The purpose of this article is to specify knowledge repository to ensure the Agile management solutions of an EAS project are aligned with strategic goals and business domain causality. It is worth noticing that strategic goals have been identified and specified as capabilities using some enterprise architecture framework (NAF, MODAF, ArchiMate, etc.). The novelty of the proposed method is incorporating the business domain causal knowledge modelling approach into the Agile project management process. The causal knowledge unit is considered as a Management Transaction (MT), which includes closed loop dependence of its components. The modified Agile activity hierarchy (theme, initiative, epic, user story) defines the required content of their mutual interactions. An important new results obtained are the conceptual model of causal knowledge base (KB) and specification of enhanced Agile management tool components: project management database and project state assesment knowledge base. Causal KB includes specification of causal knowledge unit (MT metamodel) and specifications of traditional and causal Agile hierarchy meta-models. These conceptual models define the causal knowledge components necessary to evaluate the state of Agile activities in the EAS development project using intelligent Agile project management tool.

Due to the increasing importance of evaluating the quality of health care services using the patient-centred approach, this study aimed to propose a novel framework by combining the SERVQUAL model and multi-attribute decision-making (MADM) methods using interval-valued triangular fuzzy numbers (IVTFN). In this study, after an initial overview of related work and expert opinions, a list of the most important dimensions and indicators for measuring the quality of health care services was extracted and localized. Then, to determine the importance of each of the identified factors, one of MADM’s acceptable methods called step-wise weight assessment ratio analysis (SWARA) was used. Then, in order to use the developed framework for comparing different health centres and ranking them, after collecting evaluation data in the form of linguistic variables, another practical method in the field of MADM has been used, namely, Additive Ratio Assessment (ARAS) method. The dimensions and sub-dimensions identified are, on the one hand, appropriate to the conditions of the case study and, on the other hand, the findings from the implementation show that among the dimensions of health service quality, responsiveness and then reliability has the highest rank in this case. Also, the use of IVTFN, on the one hand, eliminates the problems related to the use of Likert scale in other quality assessment methods and, on the other hand, reduces the possibility of facing imperfect knowledge of data which is a common problem in the field of qualitative evaluations. Utilizing the results of this study can significantly help decision makers in their choice of strategies to improve service quality. Furthermore, improving the quality of services can play an important role in promoting the competitiveness and performance of health care providers by increasing patient satisfaction with the services received. Also, as a side effect, the developed framework can be used to compare the performance of different hospitals and health centres, as well as their ranking.

Multiple Criteria Decision-Making (MCDM) is one of the most reliable and applicable decision-making tools to address real-life complex and multi-dimensional problems in accordance with the concepts of sustainable development and circular economy. Although there have been several literature reviews on several MCDM methods, there is a research gap in conducting a literature review on the Multi-Attributive Border Approximation area Comparison (MABAC) as a useful technique to deal with intelligent decision-making systems. This study attempts to present a comprehensive literature review of 117 articles on recent developments and applications of MABAC. Future outlook is provided considering challenges and current trends.

We study the burst ratio of packet loss processes in networking. This parameter characterizes the inclination of packet losses to form long, consecutive sequences. Such long sequences of losses may have a negative impact on multimedia streams, particularly those of real-time type. In packet networks, the burst ratio is often elevated due to overflows of packet buffers, which are present in all routers and switches. In the article, we investigate the burst ratio in the per-flow manner, i.e. individually for every flow of packets traversing a network node. We first confront all the per-flow burst ratios with each other, as well as with the burst ratio computed for the multiplexed traffic. Next, we study the influence of different features of the system on these burst ratios. In particular, the influence of rates of flows and their proportions, the standard deviation of interarrival times, the capacity of the buffer, the system load and the distribution of the service time, is studied. Special attention is paid to models with non-Poisson flows, which are not analytically tractable.