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The purpose of this research is to investigate a novel three-stage flow shop scheduling problem with an ambiguous processing time. The uncertain information is characterized by Pentagonal fuzzy numbers. To solve the problem, in this paper, two different strategies are proposed; one relies on the idea of a ranking function, and the other on the close interval approximation of the pentagonal fuzzy number. For persons that need to be more specific in their requirements, the close interval approximation of the Pentagonal fuzzy number is judged to be the best appropriate approximation interval. Regarding the rental cost specification, these methods are used to reduce the rental cost for the concerned devices. In addition, a comparison of our suggested approach's computed total processing time, total machine rental cost, and machine idle time to the existing approach is introduced. A numerical example is shown to clarify the benefits of the two strategies and to help the readers understand it better. [ABSTRACT FROM AUTHOR]

In the present paper, we introduce new subclasses of bi-starlike and bi-convex functions of complex order associated with Erdély–Kober-type integral operator in the open unit disc and find the estimates of initial coefficients in these classes. Moreover, we obtain Fekete-Szegő inequalities for functions in these classes. Some of the significances of our results are pointed out as corollaries. [ABSTRACT FROM AUTHOR]

In recent years, the Internet of Things (IoT) has led to the spread of cloud computing devices in all commercial, industrial and agricultural sectors. The use of cloud computing environment services is increasing exponentially with all technology applications based on IoT. Fog computing has led to addressing issues in cloud computing environments. Fog computing reduces load balancing, processing, bandwidth, and storage as data file replication from the cloud to the network closest to sensors in different geographic locations. There are three critical issues:—what data should be replicated?—when should the data be replicated? and—where the new replicas should be placed? These three main open questions must be tackled for data replication in the cloud environments. This strategy, the identification and mode of the data replication problem are designed as a multi-objective optimization with modern meta-heuristic optimization method. Therefore, a new hybrid method using Arithmetic Optimization Algorithm (AOA) and the salp swarm algorithm (SSA) is proposed in this paper. Firstly, a new hybrid metaheuristic method, using the Arithmetic Optimization Algorithm (AOA) and the salp swarm algorithm (SSA), is proposed to handle the problem of selection and placement data replication in fog computing. Secondly, the Floyd algorithm is used to strategy the least cost path, distance, and data transmission in different geographic locations. The performance of the designed AOASSA strategy to tackling the data replication problem is evaluated using different datasets of different sizes. To validate the AOASSA strategy, a set of experiments was carried out to validate the proposed strategy AOASSA. Experiment results show the superiority of AOASSA over its competitors in terms of performance measures, such as least cost path, distance, and bandwidth. [ABSTRACT FROM AUTHOR]

Recently, the concept of the internet of things and its services has emerged with cloud computing. Cloud computing is a modern technology for dealing with big data to perform specified operations. The cloud addresses the problem of selecting and placing iterations across nodes in fog computing. Previous studies focused on original swarm intelligent and mathematical models; thus, we proposed a novel hybrid method based on two modern metaheuristic algorithms. This paper combined the Aquila Optimizer (AO) algorithm with the elephant herding optimization (EHO) for solving dynamic data replication problems in the fog computing environment. In the proposed method, we present a set of objectives that determine data transmission paths, choose the least cost path, reduce network bottlenecks, bandwidth, balance, and speed data transfer rates between nodes in cloud computing. A hybrid method, AOEHO, addresses the optimal and least expensive path, determines the best replication via cloud computing, and determines optimal nodes to select and place data replication near users. Moreover, we developed a multi-objective optimization based on the proposed AOEHO to decrease the bandwidth and enhance load balancing and cloud throughput. The proposed method is evaluated based on data replication using seven criteria. These criteria are data replication access, distance, costs, availability, SBER, popularity, and the Floyd algorithm. The experimental results show the superiority of the proposed AOEHO strategy performance over other algorithms, such as bandwidth, distance, load balancing, data transmission, and least cost path. [ABSTRACT FROM AUTHOR]

Recently, the properties of analytic functions have been mainly discussed by means of a fuzzy subset and a q-difference operator. We define certain new subclasses of analytic functions by using the fuzzy subordination to univalent functions whose range is symmetric with respect to the real axis. We introduce the family of linear q-operators and define various classes associated with these operators. The inclusion results and various integral properties are the main investigations of this article. [ABSTRACT FROM AUTHOR]

Road traffic accidents (RTAs) pose a significant hazard to the security of the general public, especially in developing nations. A daily average of more than three thousand fatalities is recorded worldwide, rating it as the second most prevalent cause of death among people aged 5–29. Precise and reliable decisionmaking techniques are essential for identifying the most effective approach to mitigate road traffic incidents. This research endeavors to investigate this specific concern. The Fermatean fuzzy set (FFS) is a strong and efficient method for addressing ambiguity, particularly when the concept of Pythagorean fuzzy set fails to provide a solution. This research presents two innovative aggregation operators: the Fermatean fuzzy ordered weighted averaging (FFOWA) operator and the Fermatean fuzzy dynamic ordered weighted geometric (FFOWG) operator. The salient characteristics of these operators are discussed and important exceptional scenarios are thoroughly delineated. Furthermore, by implementing the suggested operators, we develop a systematic approach to handle multiple attribute decisionmaking (MADM) scenarios that involve Fermatean fuzzy (FF) data. In order to show the viability of the developed method, we provide a numerical illustration encompassing the determination of the most effective approach to alleviate road traffic accidents. Lastly, we conduct a comparative evaluation of the proposed approach in relation to a number of established methodologies. [ABSTRACT FROM AUTHOR]

Hybrid nanofluids are capable of being used as a carrier for delivering drugs to targeted areas in the body. For this purpose, the Casson hybrid nanofluids (HNFs) flow is suggested between the gap of two tubes. The study involves analyzing blood-based hybrid nanofluids (HNFs) that consist of silver (Ag) and titanium dioxide (TiO2) nanoparticles for applications of drug delivery. Titanium oxide nanoparticles, or TiO2 NPs, have strong photoactivity, low toxicity, and excellent biocompatibility, making them promising candidates for cancer treatment. The attractiveness of silver nanoparticles (AgNPs) for cancer therapy is due to their unique properties. Graphic representations are provided based on the simulations of non-dimensional velocity, temperature, and skin friction under physical parameter variations, for the applications of drug delivery are investigated and discussed. The control volume finite element method (CVFEM), is used to solve the problem. The new strategy of artificial neural networks (ANN) is also used to solve the transform equations. Nanoparticles increase drug stability and extend shelf life by protecting them from degradation or inactivation. [ABSTRACT FROM AUTHOR]

This study attempts to introduce continuous static games with fuzzy parameters. Piecewise quadratic fuzzy numbers (PQFNs) are used to represent the fuzzy parameters. The close interval approximation of the PQFNs, which is one of the most accurate approximations, is applied. To overcome the problem, Stackelberg leaders with min-max follower's solutions are examined. The given solution is accompanied by a parametric analysis. An algorithm for identifying the first-kind stability set is presented. Finally, to demonstrate and clarify the method's steps, a numerical example is given [ABSTRACT FROM AUTHOR]

This study aims to characterize and clarify a pentagonal fuzzy continuous static game (PF-CSG) that constraints and cost functions are fuzzy numbers. Pentagonal fuzzy numbers characterize their fuzzy parameters. The α − Pareto optimal solution concept has specified. The decomposition approach has applied to decompose the problem into subproblems each of them having smaller and independent subproblems. In addition, the Nash equilibrium solution concept was used to obtain the solutions of these subproblems. The advantages of this study are the players independently without collaboration with any of the others and that each player seeks to minimize the cost function. Also, the information available to each player consists of the cost function and constraints. An illustrated numerical example has discussed for proper understanding and interpretation of the proposed concept. [ABSTRACT FROM AUTHOR]

Hypersoft set is a novel area of interest which is able to tackle the real-world scenarios where classification of parameters into their respective sub-parametric values in the form of overlapping sets is mandatory. It employs a new approximate mapping which considers such sets in the form of sub-parametric tuples as its domain. The existing soft set-like structures are insufficient to tackle such kind of situations. This research intends to establish a novel concept of parameterization of fuzzy set under hypersoft set environment with uncertain components of intuitionistic fuzzy set and neutrosophic set. Two novel structures, i.e., fuzzy parameterized intuitionistic fuzzy hypersoft set (fpifhs -set) and fuzzy parameterized neutrosophic hypersoft set (fpnhs -set), are developed by employing algebraic techniques like theoretic, analytical, pictorial, and algorithmic techniques. After characterizing the elementary properties and set-theoretic operations of fpifhs -set and fpnhs -set, two novel algorithms are proposed to solve real-life decision-making COVID-19 problem. The results of both algorithms are compared with related already established models through certain evaluating features to judge the advantageous aspects of the proposed study. The generalization of the proposed models is discussed by describing some of their particular cases. [ABSTRACT FROM AUTHOR]

Generally, the transportation of cargo plays a vital role in effective supply chain management. For the safe transportation of the goods, the Returnable Containers (RCs) are provided by the container management organization to the consignee as rental basis. Unfortunately, some of the RCs are returned as delay in time for some situations. This study explores a situation of container management organization that unexpected delay of empty containers and then suggests a restoring strategy for shortfall units. As a strategy of restoring the shortfall units, this research considers the leasing option instead of buying the new RCs. This paper examines the container inventory model with the consideration of the delayed return of containers under the fuzzy arena. Because the container's return rate is unpredictable and uncertain, the container inventory model is formulating under fuzzy environment. This research describes a container inventory model with price-sensitive demand. In this model, the container return rate as well as the proportion of repairable from received RCs as Triangular Fuzzy Numbers (TFNs). The optimal duration of inspection of the container and the optimal duration of the lease of an RC are obtained. The expected total cost using the fuzzy return rate and the fuzzy proportion of repairable RCs is computed. The container inventory model under fuzzy arena is proposed to attain the approximate outcome for the unpredictable and uncertain parameters. The impact of the fuzzy proportion of retuned RCs, fuzzy reparable from received RCs, and the changes in some parameters on optimal solutions are performed in the sensitivity analysis. [ABSTRACT FROM AUTHOR]

The main focus of this work is to investigate the impact of varying milling times (2 to 18 h) on the structural and mechanical properties of the developed Ti-Nb-Mo alloy. The morphology, phase composition, microstructure, and mechanical behavior of milled and sintered Ti-25Nb-25Mo alloy samples were characterized systematically using x-ray diffraction, scanning electron microscope, optical microscope, and Vicker microhardness. It was noted that the quantity of the β-Ti phase increased as the milling time increased. After 12 h of milling, the synthesized alloys exhibited a spherical morphology and texture with homogeneous distribution. The milled alloys' structural evolution and morphological changes were found to be dependent on their milling duration. Morphological analysis revealed that the crystallite size and mean pore size decreased when the milling duration increased, reaching minimum values of 51 nm and < 1 μm, after 12 and 18 h respectively. As the milling time increased, the grain size decreased, resulting in an increase in density, microhardness, and elastic modulus. Ti-25Nb-25Mo will presents good anti-wear ability and higher resistance to plastic deformation due to enhanced mechanical characteristics (H/E, and H3/E2). Hence, the developed Ti-25Nb-25Mo alloys with reduced elastic modulus and desirable mechanical properties were found to be a promising option for biomedical applications. [ABSTRACT FROM AUTHOR]

Fractional transportation problem that includes source and destination may have fractional objective functions in real-world applications to maximize the profitability ratio like profit/cost or profit/time. We refere to such transportation problems as fractional transportation problem. The paper considers the interval-valued neutrosophic numbers and its aritemematic operations. This paper deals with fractional transportation problem having discounting cost in neutrosophic environment, where the supply, demand and transportation costs are uncertain. The problem is considered by introducing all the parameters as neutrosophic numbers. Using the benefits of the score function definition, the problem is transformed into the corresponding deterministic form which can be illustrated by any method. and hence by applying of least cost method with the help of Kuhn-Tucker' optimality conditions, the optimal solution is resulted. Our strategy is to assess the issue and can rank different sort of neutrosophic numbers. To claify the proposed technique, a numerical example is given to show the adequacy of the new model. [ABSTRACT FROM AUTHOR]

The Decision-Theoretic Rough Set model stands as a compelling advancement in the realm of rough sets, offering a broader scope of applicability. This approach, deeply rooted in Bayesian theory, contributes significantly to delineating regions of minimal risk. Within the Decision-Theoretic Rough Set paradigm, the universal set undergoes a tripartite division, where distinct regions emerge and losses are intelligently distributed through the utilization of membership functions. This research endeavors to present an enhanced and more encompassing iteration of the Decision-Theoretic Rough Set framework. Our work culminates in the creation of the Generalized Intuitionistic Decision-Theoretic Rough Set (GI-DTRS), a fusion that melds the principles of Decision-Theoretic Rough Sets and intuitionistic fuzzy sets. Notably, this synthesis bridges the gaps that exist within the conventional approach. The innovation lies in the incorporation of an error function tailored to the hesitancy grade inherent in intuitionistic fuzzy sets. This integration harmonizes seamlessly with the contours of the membership function. Furthermore, our methodology deviates from established norms by constructing similarity classes based on similarity measures, as opposed to relying on equivalence classes. This shift holds particular relevance in the context of aggregating information systems, effectively circumventing the challenges associated with the process. To demonstrate the practical efficacy of our proposed approach, we delve into a concrete experiment within the information technology domain. Through this empirical exploration, the real-world utility of our approach becomes vividly apparent. Additionally, a comprehensive comparative analysis is undertaken, juxtaposing our approach against existing techniques for aggregation and decision modeling. The culmination of our efforts is a well-rounded article, punctuated by the insights, recommendations, and future directions delineated by the authors. [ABSTRACT FROM AUTHOR]

Q-rung orthopair fuzzy sets have been proven to be highly effective at handling uncertain data and have gained importance in decision-making processes. Torra's hesitant fuzzy model, on the other hand, offers a more generalized approach to fuzzy sets. Both of these frameworks have demonstrated their efficiency in decision algorithms, with numerous scholars contributing established theories to this research domain. In this paper, recognizing the significance of these frameworks, we amalgamated their principles to create a novel model known as Q-rung orthopair hesitant fuzzy sets. Additionally, we undertook an exploration of Aczel–Alsina aggregation operators within this innovative context. This exploration resulted in the development of a series of aggregation operators, including Q-rung orthopair hesitant fuzzy Aczel–Alsina weighted average, Q-rung orthopair hesitant fuzzy Aczel–Alsina ordered weighted average, and Q-rung orthopair hesitant fuzzy Aczel–Alsina hybrid weighted average operators. Our research also involved a detailed analysis of the effects of two crucial parameters: λ , associated with Aczel–Alsina aggregation operators, and N, related to Q-rung orthopair hesitant fuzzy sets. These parameter variations were shown to have a profound impact on the ranking of alternatives, as visually depicted in the paper. Furthermore, we delved into the realm of Wireless Sensor Networks (WSN), a prominent and emerging network technology. Our paper comprehensively explored how our proposed model could be applied in the context of WSNs, particularly in the context of selecting the optimal gateway node, which holds significant importance for companies operating in this domain. In conclusion, we wrapped up the paper with the authors' suggestions and a comprehensive summary of our findings. [ABSTRACT FROM AUTHOR]

In the present investigation, we introduce a new class of meromorphic functions defined in the punctured unit disk Δ * : = { ϑ ∈ C : 0 < | ϑ | < 1 } by making use of the Erdély–Kober operator I ς , ϱ τ , κ which unifies well-known classes of the meromorphic uniformly convex function with positive coefficients. Coefficient inequalities, growth and distortion inequalities, in addition to closure properties are acquired. We also set up a few outcomes concerning convolution and the partial sums of meromorphic functions in this new class. We additionally state some new subclasses and its characteristic houses through specializing the parameters that are new and no longer studied in association with the Erdély–Kober operator thus far. [ABSTRACT FROM AUTHOR]

The correlation between two disparate variables conquers a significant habitation in statistics. The concept of correlation coefficient (CC) is one of the well-known indicators, but it is not used in interval-valued intuitionistic fuzzy hypersoft set (IVIFHSS) information. It is a generalization of interval-valued intuitionistic fuzzy soft sets and a refined extension of intuitionistic fuzzy hypersoft sets. However, using the CC and weighted correlation coefficient (WCC) has not yet been explored for IVIFHSS information. The core objective of this research is to present the correlation coefficient (CC) and weighted correlation coefficient (WCC) for interval-valued intuitionistic fuzzy hypersoft sets (IVIFHSS) and their mandatory properties. A prioritization technique for order preference by similarity to the ideal solution (TOPSIS) is developed based on proposed correlation measures. To ensure the symmetry of the developed scheme, we consider mathematical clarifications of correlation contractions. Based on assessments, it conceded vibrant multi-attribute decision-making (MADM) methodology with the most substantial significance. In addition, a statistical illustration is designated to regulate the operative usage of a decision-making configuration in thermal energy storage techniques. The productivity of the advocated algorithm is more reliable than existing replicas to control the favorable configurations of the premeditated study. [ABSTRACT FROM AUTHOR]

In the past, investors used their own or others' experiences to achieve their goals. With the development of financial management, investors' choices became more scientific. They could select the optimal choice by using different models and combining the results with their experiences. In portfolio optimization, the main issue is the optimal selection of the assets and securities that can be provided with a certain amount of capital. In the present study, the problem of optimization, i.e., maximizing stock portfolio returns and minimizing risk, has been studied. Therefore, this study discussed comprehensive modeling for the optimal selection of stock portfolios using multi-criteria decision-making methods in companies listed on the Tehran Stock Exchange. A sample of 79 companies listed on the Tehran Stock Exchange was used to conduct this research. After simulating the data and programming them with MATLAB software, the cumulative data analysis model was performed, and 24 companies were selected. This research data were collected from the financial statements of companies listed on the Tehran Stock Exchange in 2020. The primary purpose of this study was a comprehensive modeling for the optimal selection of stock portfolios using multi-criteria decision-making methods in companies listed on the Tehran Stock Exchange. The index in the Tehran Stock Exchange can be used to provide a comprehensive and optimal model for the stock portfolio; different multi-index decision-making methods (TOPSIS method), the taxonomy method (Taxonomy), ARAS method, VIKOR method, The COPRAS method and the WASPAS method can all identify the optimal stock portfolio and the best stock portfolio for the highest return. [ABSTRACT FROM AUTHOR]

This paper is related to notions adapted from fuzzy set theory to the field of complex analysis, namely fuzzy differential subordinations. This work aims to present new fuzzy differential subordinations for which the fuzzy best dominant and fuzzy best subordinate are given, respectively. The original theorems proved in the paper generate interesting corollaries for particular choices of functions acting as fuzzy best dominant. Here, in this article, fuzzy differential subordination results are obtained using a new integral operator introduced in this paper for meromorphic function, such that the newly-defined integral operator is starlike and convex, respectively. [ABSTRACT FROM AUTHOR]

The aim of the current study is to evaluate the effect of iron content (0, 2, 4, 6 and 10 wt%) on the structural, tribological and photocatalytical properties of a nanostructured ternary alloy Ti-6Al-XFe, prepared by high energy milling. The alloys' characteristics such as lattice parameters, powder morphologies, surface roughness, relative density/porosity, and microhardness, were evaluated using X-ray diffraction (XRD), scanning electron microscope (SEM), surface profilometry, porosimeter and micro durometer, respectively. The W-H method was utilized to determine the crystallite size. Micro strain was also calculated, which is produced in the lattice due to the diffusion of iron atoms. The photocatalytical characterization was conducted by measuring their absorbance as a function of time using spectrophotometer of visible and ultraviolet light in the wavelength range of 500–800 nm. The tribological characterization was performed using an oscillating tribometer under wet conditions, simulating the human body environment using Phosphate Buffered Saline (PBS) solution with neutral pH 7.4, under different applied loads of 2, 6 and 10 N, respectively. Results showed that the addition of Fe has a significant effect on the structural properties of the developed alloys. The lattice parameter (a α) decreased with increasing Fe content from 2.9493 Å (0 wt% Fe) to 2.9491 Å (10 wt% Fe), while the average grain size increased considerably from 6.965 nm (0 wt% Fe) to 44.42 nm (10 wt% Fe). The wear test results showed that, friction coefficient and wear rate considerably decreased due to the formation of protective films such as TiO 2. The photocatalytical characterization showed that, the degradation of methylene blue (MB) increased with increasing Fe content. The Ti-6Al-4Fe -catalyst gave the best degree of degradation of 90.76% within 60 min, which meant that the decolorization process could be operated rapidly at a relatively low cost without UV irradiation. • HiPed Ti-6Al-XFe alloy was prepared with varying Fe content • The lattice parameter and micro strain decreased with increasing Fe. • Alloy with highest Fe content (10 wt%) showed the lowest friction and wear rate. • The improvement is attributed to the enhancement of mechanical properties resulting from the refinement of grain size and reduction in porosity. • The degree of degradation of methylene blue (MB) increased with increased Fe [ABSTRACT FROM AUTHOR]

Pythagorean fuzzy soft set (PFSS) is the most powerful and effective extension of Pythagorean fuzzy sets (PFS) which deals with the parametrized values of the alternatives. It is also a generalization of intuitionistic fuzzy soft set (IFSS) which provides us better and precise information in the decision-making process comparative to IFSS. The core objective of this work is to construct some algebraic operations for PFSS such as OR-operation, AND-operation, and necessity and possibility operations. Furthermore, some fundamental properties have been established for PFSS utilizing the developed operations. Moreover, a decision-making technique has been offered for PFSS based on a score matrix. To demonstrate the validity of the proposed approach, a numerical example has been presented. Finally, to ensure the practicality of the established approach, a comprehensive comparative analysis has been presented. The obtained results show that our developed approach is most effective and delivers better information comparative to prevailing techniques. [ABSTRACT FROM AUTHOR]

REFERENCES 1 Siddique I., Zulqarnain R. M., Ali R., Alburaikan A., Iampan A., Abd El-Wahed Khalifa H. A decision-making approach based on score matrix for pythagorean fuzzy soft set. In the article titled "A Decision-Making Approach Based on Score Matrix for Pythagorean Fuzzy Soft Set" [[1]], there was an error in the fourth listed affiliation. [Extracted from the article]