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The domain of decision‐making theory has made significant progress, especially within the industrial and management fields. Diverse decision‐making challenges such as multicriteria decision‐making (MCDM), multiattribute decision‐making (MADM), and multiattribute group decision‐making (MAGDM) have been thoroughly examined, equipping decision‐makers with effective strategies for tackling these issues. In the context of the automotive industry, a specific hurdle emerges when identifying the most suitable site for establishing a warehouse to store goods destined for multiple destinations. This article addresses a scenario amid uncertainty, leveraging substantial data. Neutrosophic sets (NSs) emerge as a comprehensive tool for effectively managing the imprecision inherent in such data. Among these sets, single‐valued neutrosophic sets (SVNSs) stand out due to their adeptness in handling inconsistent or incomplete data. The octagonal single‐valued neutrosophic numbers (OSVNNs) has a new tool for representing the uncertainty information in a simplified manner. Octagonal structure allows for representing different distinctions of truth, neutrality, and falsity of any complex situation and it is an efficient tool to compare different options based on their level of ambiguity. They have a wide range of applications in various fields, becoming increasingly important in addressing many difficult and uncertain issues. The article aims to propose a new ranking function for OSVNNs to convert the OSVNN data into precise values, drawing from the existing mean interval method (MIM). The conventional decision‐making approaches such as weighted sum model (WSM), weighted product model (WPM), technique for order of preference by similarity to ideal solution (TOPSIS), and VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) are employed to obtain the optimal warehouse location using OSVN application. Furthermore, it demonstrates the application along with the proposed ranking function using the software MATLAB to determine the most favorable alternatives. Finally, sensitivity analyses are performed to assess how different scenarios could impact the optimal location selection for automotive logistics. [ABSTRACT FROM AUTHOR]

This work proposes a new unsupervised method to evaluate the behavior of urban green areas in the presence of heatwave scenarios by analyzing three indices extracted from satellite data: the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Moisture Index (NDMI), and Land Surface Temperature (LST). The aim of this research is to analyze the behavior of urban vegetation types during heatwaves through the analysis of these three indices. To evaluate how these indices characterize urban green areas during heatwaves, an unsupervised classification method of the three indices is proposed that uses the Elbow method to determine the optimal number of classes and the Jenks classification algorithm. Each class is assigned a Gaussian fuzzy set and the green urban areas are classified using zonal statistics operators. The membership degree of the corresponding fuzzy set is calculated to assess the reliability of the classification. Finally, for each type of greenery, the frequencies of types of green areas belonging to NDVI, NDMI, and LST classes are analyzed to evaluate their behavior during heatwaves. The framework was tested in an urban area consisting of the city of Naples (Italy). The results show that some types of greenery, such as deciduous forests and olive groves, are more efficient, in terms of health status and cooling effect, than other types of urban green areas during heatwaves; they are classified with NDVI and NDMI values of mainly High and Medium High, and maximum LST values of Medium Low. Conversely, uncultivated areas show critical behaviors during heatwaves; they are classified with maximum NDVI and NDMI values of Medium Low and maximum LST values of Medium High. The research results represent a support to urban planners and local municipalities in designing effective strategies and nature-based solutions to deal with heat waves in urban settlements. [ABSTRACT FROM AUTHOR]

This paper introduced a novel concept of q‐rung orthopair fuzzy (q‐ROF) metric spaces, in connection with the idea of q‐ROF sets, which generalizes well‐known concepts of fuzzy metric spaces and intuitionistic fuzzy metric spaces. An elaborated example has been provided representing the increase in selection space due to the q‐ROF metric and limitations for the case of intuitionistic fuzzy metric space. We have presented the definitions of introduced concepts along with examples, so they are way more understandable for readers. Some basic topological results have been defined and proved for q‐ROF metric space. The concept of q‐ROF Menger boundedness is introduced, and its application in game theory is presented. Also we have presented the significance of introduced metric compared to existing versions of fuzzy metric in the consequent sections. Baire's theorem and uniform limit theorem are also established in the perspective of q‐ROF metric spaces. An application of the obtained results in the field of game theory is also presented to draw the attention of investigators searching new fields of research. [ABSTRACT FROM AUTHOR]

Analyzing the uncertainty of outcomes based on estimates of the data's membership degrees to fuzzy sets is essential for making decisions. These fuzzy sets are often designated by experts as strong fuzzy partitions of the data domain with trapezoidal fuzzy numbers. Some indices of the fuzzy set's fuzziness provide an assessment of the degree of uncertainty of the results. It is feasible to bring the fuzzy sets' fuzziness below a tolerable level by suitably redefining the strong fuzzy partition. Significant differences in the original fuzzy partition, however, result in disparities concerning the decision maker's approximative reasoning and the interpretability of the results. In light of this, we provide in this study a technique applied to trapezoidal strong fuzzy partitions that, while not appreciably altering the original fuzzy partition, reduces the fuzziness of its fuzzy sets. The fuzziness of the fuzzy sets is assessed using the De Luca and Termini fuzzy entropy. An iterative process is then executed, with the aim of modifying the cores of the trapezoidal fuzzy partitions to decrease their fuzziness. This technique is tested on datasets containing average daily temperatures measured in various cities. The findings demonstrate that this approach strikes a great balance between the goal of lessening the fuzziness of the fuzzy sets and the goal of not appreciably altering the original fuzzy partition. [ABSTRACT FROM AUTHOR]

This research presents a GIS-based framework used to detect urban heat islands and determine which urban settlement elements are most critical when heatwave risks exist. The proposed method uses the Iterative Self-Organizing Data Analysis (ISODATA) clustering algorithm applied to the satellite land surface temperature distribution recorded during heatwaves for the detection of urban heat islands. A pixel classification confidence level maximization approach, obtained by running a maximum likelihood classification algorithm, is performed to determine the optimal number of clusters. The areas labeled as hotspots constitute the detected urban heat islands (UHIs). This method was tested on an urban settlement set up by the municipality of Naples (Italy). Comparison tests were performed with other urban heat island detection methods such as standard deviation thresholding and Getis-Ord Gi* hotspot detection; indices measuring the density of buildings, the percentage of permeable open spaces, and vegetation cover are taken into consideration to evaluate the accuracy of the urban heat islands detected. These tests highlight that the proposed method provides the most accurate results. It could be an effective tool to support the decision maker in evaluating which urban areas are the most critical during heatwave scenarios. [ABSTRACT FROM AUTHOR]