The demand for renewable energy has significantly increased over the last decade with increased attention to the preservation of the environment and sustainable, optimal resource management. As traditional sources of energy production are depleting at an alarming rate and causing long-lasting environmental damage, it is essential to explore green and cost-effective methodologies for meeting energy demand. With each country having different geographical, political, social, and natural factors, the problem arises of which renewable energy should be utilized for optimal resource management. This multi-criteria decision making (MCDM) challenge is tackled by applying a dynamic fuzzy hypersoft set-based Method for the evaluation of currently deployed Renewable Energy systems and providing a decision support system for the installation of new ones based on the factors mentioned above for Turkey. As the installation of new renewable energy projects and the evaluation of old ones is significantly influenced by human judgment, it leaves great room for uncertainty primarily because of the psychological factors of the expert. The novel concept of Fuzzy Hypersoft Sets (FHSs) and their Entropy (EN) and TOPSIS-based operations are first discussed with reference to the problem at hand. The presented structure is superior to the ones in the literature by allowing access to data parameters as sub-parametric values while utilizing the versatility of Fuzzy structures to deal with uncertainty. The technique has great potential to serve as a potential decision support system in any setting. For now, hypothetical expert ratings are used to illustrate the working of the dynamic structure along with a sensitivity analysis to investigate the primary criterion weights in sorting. The evaluation of currently deployed renewable energy systems using our methodology revealed an average improvement in system performance compared to traditional methods. Furthermore, the decision support system for the installation of new projects based on geographical, political, social, and natural factors exhibited a potential increase in overall system efficiency. These numeric outcomes highlight the effectiveness and practical applicability of our approach in optimizing resource management and fostering sustainable energy practices.

• Klíčová slova
• Impreciseness, Information measures, Multi-argument, Multi-attribute, Renewable energy, Vagueness,
• Publikační typ
• časopisecké články MeSH

A useful expansion of the intuitionistic fuzzy set (IFS) for dealing with ambiguities in information is the Pythagorean fuzzy set (PFS), which is one of the most frequently used fuzzy sets in data science. Due to these circumstances, the Aczel-Alsina operations are used in this study to formulate several Pythagorean fuzzy (PF) Aczel-Alsina aggregation operators, which include the PF Aczel-Alsina weighted average (PFAAWA) operator, PF Aczel-Alsina order weighted average (PFAAOWA) operator, and PF Aczel-Alsina hybrid average (PFAAHA) operator. The distinguishing characteristics of these potential operators are studied in detail. The primary advantage of using an advanced operator is that it provides decision-makers with a more comprehensive understanding of the situation. If we compare the results of this study to those of prior strategies, we can see that the approach proposed in this study is more thorough, more precise, and more concrete. As a result, this technique makes a significant contribution to the solution of real-world problems. Eventually, the suggested operator is put into practise in order to overcome the issues related to multi-attribute decision-making under the PF data environment. A numerical example has been used to show that the suggested method is valid, useful, and effective.

• Klíčová slova
• Aczel-Alsina operations, MADM, Pythagorean fuzzy Aczel-Alsina average aggregation operators, Pythagorean fuzzy elements,
• Publikační typ
• časopisecké články MeSH

Aluminum is a widely popular material due to its low cost, low weight, good formability and capability to be machined easily. When a non-metal such as ceramic is added to aluminum alloy, it forms a composite. Metal Matrix Composites (MMCs) are emerging as alternatives to conventional metals due to their ability to withstand heavy load, excellent resistance to corrosion and wear, and comparatively high hardness and toughness. Aluminum Matrix Composites (AMCs), the most popular category in MMCs, have innumerable applications in various fields such as scientific research, structural, automobile, marine, aerospace, domestic and construction. Their attractive properties such as high strength-to-weight ratio, high hardness, high impact strength and superior tribological behavior enable them to be used in automobile components, aviation structures and parts of ships. Thus, in this research work an attempt has been made to fabricate Aluminum Alloys and Aluminum Matrix Composites (AMCs) using the popular synthesis technique called stir casting and join them by friction stir welding (FSW). Dissimilar grades of aluminum alloy, i.e., Al 6061 and Al 1100, are used for the experimental work. Alumina and Silicon Carbide are used as reinforcement with the aluminum matrix. Mechanical and corrosion properties are experimentally evaluated. The FSW process is analyzed by experimentally comparing the welded alloys and welded composites. Finally, the best suitable FSW combination is selected with the help of a Multi-Attribute Decision Making (MADM)-based numerical optimization technique called Weighted Aggregated Sum Product Assessment (WASPAS).

• Klíčová slova
• alloys, aluminum, composites, friction stir welding, multi-attribute decision making, optimization, parameters, properties, stir casting,
• Publikační typ
• časopisecké články MeSH

This study introduces an advanced approach for ranking international football players, addressing the inherent uncertainties in performance evaluations. By integrating dual possibility theory and Pythagorean fuzzy sets, the model accommodates varying degrees of ambiguity and imprecision in player attributes. Additionally, the use of hypersoft set theory enriches the analysis by capturing the multifaceted nature of player evaluations. The proposed aggregation operators refine the synthesis of diverse information sources, leading to a comprehensive and nuanced assessment. This research significantly enhances player evaluation methodologies, providing a more adaptable framework for a fair assessment of international football talent. A practical example illustrates the application of dual-possibility Pythagorean fuzzy hypersoft sets (DP-PFHSS). A numerical technique is proposed for solving multi-criteria decision-making (MCDM) challenges with known dual possibility information using the proposed aggregation operators. This decision-making algorithm effectively determines a football player's worth, contributing to the overall ranking and evaluation process. The approach aids in scouting and recruitment by facilitating talent identification and informed player signings. Graphical analysis, comparing existing and proposed methods using average and geometric operators, demonstrates the superiority of the proposed approach in the players evaluation, indicating that F 1 is in the top ranking.

• Klíčová slova
• Comparison, Dual possibility, Hypersoft set, Pythagorean fuzzy soft set, Ranking and decision making,
• Publikační typ
• časopisecké články MeSH

Regional innovation performance is an important indicator for decision-making regarding the implementation of policies intended to support innovation. However, patterns in regional innovation structures are becoming increasingly diverse, complex and nonlinear. To address these issues, this study aims to develop a model based on a multi-output neural network. Both intra- and inter-regional determinants of innovation performance are empirically investigated using data from the 4th and 5th Community Innovation Surveys of NUTS 2 (Nomenclature of Territorial Units for Statistics) regions. The results suggest that specific innovation strategies must be developed based on the current state of input attributes in the region. Thus, it is possible to develop appropriate strategies and targeted interventions to improve regional innovation performance. We demonstrate that support of entrepreneurship is an effective instrument of innovation policy. We also provide empirical support that both business and government R&D activity have a sigmoidal effect, implying that the most effective R&D support should be directed to regions with below-average and average R&D activity. We further show that the multi-output neural network outperforms traditional statistical and machine learning regression models. In general, therefore, it seems that the proposed model can effectively reflect both the multiple-output nature of innovation performance and the interdependency of the output attributes.

• MeSH
• organizační inovace * MeSH
• rozhodování MeSH
• společenská třída MeSH
• Publikační typ
• časopisecké články MeSH
• Geografické názvy
• Evropa MeSH

This study proposes a model to integrate the Multi-Attribute Decision-Making (MADM) method into the Analysis phase of the Six Sigma (DMAIC) method to improve product quality and optimize processing conditions during high-frequency quenching heat treatment. One of the breakthroughs of the study is the combination of Industry 4.0 technology and the implementation of Shunt Active Power Filter (SAPF) to improve power quality, reduce harmonic distortion (THD), ensure product hardness of 58-62 HRC, and thermal permeability of 1.8-2.2 mm according to standards. Previously, many studies only focused on improving the heat treatment process but did not fully integrate MADM, Six Sigma, and Industry 4.0 technology, nor did any study consider the combination of SAPF to control power quality during high-frequency quenching. Another gap is the lack of quantitative assessment of operator satisfaction after improvement using PLS-SEM. The study applied the Six Sigma DMAIC model combined with MADM to analyze and rank factors affecting product quality. In the improvement phase, the Taguchi method was used to optimize processing conditions, minimizing errors in the production process. At the same time, Industry 4.0 technology and RFID systems were integrated to control production conditions in real time, ensuring the accuracy and reliability of the process. Power quality was improved thanks to the implementation of SAPF, helping to control harmonic distortion (THD) below 5% according to the IEEE 519:2022 standard, minimizing the negative impact of voltage on the heat treatment process. In addition, the study also applied PLS-SEM to measure operator satisfaction after implementing the improved system. The research results show that the rate of substandard products has decreased sharply from 90 to 1%, ensuring hardness of 58-62 HRC and thermal permeability of 1.8-2.2 mm. Power quality is better controlled, with the THD value reduced from more than 34% to less than 5%, meeting the IEEE 519:2022 standard. As a result, production costs are optimized, helping to minimize the waste of raw materials and energy. After implementing the improved system, operators' satisfaction levels have also increased significantly, reflected in the PLS-SEM measurement indicators. More importantly, this research model is not only effectively applied in the precision engineering industry but also has the potential to be expanded to many other industries, especially small and medium-sized manufacturing enterprises, helping them to increase productivity and improve product quality in the context of Industry 4.0.

• Klíčová slova
• DMAIC, MADM, Manufacturing cost, PLS-SEM, Power energy,
• Publikační typ
• časopisecké články MeSH