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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.

This contribution presents a brief survey of clipping and intersection algorithms in ${E^{2}}$ and ${E^{3}}$ with a nearly complete list of relevant references. Some algorithms use the projective extension of the Euclidean space and vector-vector operations, which support GPU and SSE use.

The focus of this paper is on the criteria weight approximation in Multiple Criteria Decision Making (MCDM). An approximate weighting method produces the weights that are surrogates for the exact values that cannot be elicited directly from the DM. In this field, a very famous model is Rank Order Centroid (ROC). The paper shows that there is a drawback to the ROC method that could be resolved. The paper gives an idea to develop a revised version of the ROC method called Improved ROC (IROC). The behaviour of the IROC method is investigated using a set of simulation experiments. The IROC method could be employed in situations of time pressure, imprecise information, etc. The paper also proposes a methodology including the application of the IROC method in a group decision making mode, to estimate the weights of the criteria in a tree-shaped structure. The proposed methodology is useful for academics/managers/decision makers who want to deal with MCDM problem. A study case is examined to show applicability of the proposed methodology in a real-world situation. This case is engine/vehicle selection problem, that is one of the fundamental challenges of road transport sector of any country.

The coordinated integration of heterogeneous TinyML-enabled elements in highly distributed Internet of Things (IoT) environments paves the way for the development of truly intelligent and context-aware applications. In this work, we propose a hierarchical ensemble TinyML scheme that permits system-wide decisions by considering the individual decisions made by the IoT elements deployed in a certain scenario. A two-layered TinyML-based edge computing solution has been implemented and evaluated in a real smart-agriculture use case, permitting to save wireless transmissions, reduce energy consumption and response times, at the same time strengthening data privacy and security.