Development of hamming and hausdorff distance metrics for cubic intuitionistic fuzzy hypersoft set in cement storage quality control: Development and evaluation.

Quality control is paramount in product manufacturing as it ensures consistent production to meet customer expectations, regulatory requirements and maintain a company's reputation and profitability. Distance measures within fuzzy sets serve as powerful tools for quality control, allowing for data comparison and identification of potential defects or outliers within a system. This study aims to develop a hybrid concept by combining a Cubic Intuitionistic Fuzzy Set (CIFS) with Soft Set (SS) and extending it to Cubic Intuitionistic Fuzzy Hypersoft Set (CIFHSS). CIFHSS enables handling multiple distinct attributes at the sub-attribute level within a cubic set environment. The concept includes operations like internal, partial internal, external, complement, direct sum, and product. Additionally, six distance metrics are defined within CIFHSS and applied to establish a quality control management system for industrial applications. The versatility of CIFHSS in quality control management stems from its ability to capture and model uncertainty, vagueness, and imprecision in data. This makes it an effective tool for decision-making, risk analysis, and process optimization across a wide range of industrial applications.

Theoretical framework for a decision support system for micro-enterprise supermarket investment risk assessment using novel picture fuzzy hypersoft graph.

Risk evaluation has always been of great interest for individuals wanting to invest in various businesses, especially in the marketing and product sale centres. A finely detailed evaluation of the risk factor can lead to better returns in terms of investment in a particular business. Considering this idea, this paper aims to evaluate the risk factor of investing in different nature of products in a supermarket for a better proportioning of investment based on the product's sales. This is achieved using novel Picture fuzzy Hypersoft Graphs. Picture Fuzzy Hypersoft set (PFHSs) is employed in this technique, a hybrid structure of Picture Fuzzy set and Hypersoft Set. These structures work best for evaluating uncertainty using membership, non-membership, neutral, and multi-argument functions, making them ideal for Risk Evaluation studies. Also, the concept of the PFHS graph with the help of the PFHS set is introduced with some operations like the cartesian product, composition, union, direct product, and lexicographic product. This method presented in the paper provides new insight into product sale risk analysis with a pictorial representation of its associated factors.

Bipolar picture fuzzy hypersoft set-based performance analysis of abrasive textiles for enhanced quality control.

Abrasive textiles have widespread industrial applications in the fields of polishing, finishing, deburring, and cleaning of various surfaces. Effective decision making and performance analysis are crucial in the development and manufacturing of abrasive textiles, as it enables manufacturers to evaluate and optimize the performance of these materials for specific applications and to make informed decisions about their production processes. For that purpose, this work aims to introduce an innovative bipolar picture fuzzy hypersoft set (BPFHSS) which is composed of two picture fuzzy hyper soft sets; one of them gives us the positive information, and the other gives us the negative information, for each membership degree, neutral membership, and non-membership degree. The properties of the designed structure and discussed alongside a thorough discussion on the De-Morgan's laws. Also, the bipolar picture fuzzy hypersoft weighted geometric (BPFHSWG) operator is defined for the BPFHSS framework to aggregate bipolar picture fuzzy hypersoft numbers (BPFHSN) information. This research highlights the importance of considering inconsistent, bipolar, and multiple sub-attribute information in decision-making processes by using the defined operators to develop an algorithm for a multi-attribute analysis for quality control of manufacture of abrasive textiles.

A Framework for Susceptibility Analysis of Brain Tumours Based on Uncertain Analytical Cum Algorithmic Modeling.

Susceptibility analysis is an intelligent technique that not only assists decision makers in assessing the suspected severity of any sort of brain tumour in a patient but also helps them diagnose and cure these tumours. This technique has been proven more useful in those developing countries where the available health-based and funding-based resources are limited. By employing set-based operations of an arithmetical model, namely fuzzy parameterised complex intuitionistic fuzzy hypersoft set (FPCIFHSS), this study seeks to develop a robust multi-attribute decision support mechanism for appraising patients' susceptibility to brain tumours. The FPCIFHSS is regarded as more reliable and generalised for handling information-based uncertainties because its complex components and fuzzy parameterisation are designed to deal with the periodic nature of the data and dubious parameters (sub-parameters), respectively. In the proposed FPCIFHSS-susceptibility model, some suitable types of brain tumours are approximated with respect to the most relevant symptoms (parameters) based on the expert opinions of decision makers in terms of complex intuitionistic fuzzy numbers (CIFNs). After determining the fuzzy parameterised values of multi-argument-based tuples and converting the CIFNs into fuzzy values, the scores for such types of tumours are computed based on a core matrix which relates them with fuzzy parameterised multi-argument-based tuples. The sub-intervals within [0, 1] denote the susceptibility degrees of patients corresponding to these types of brain tumours. The susceptibility of patients is examined by observing the membership of score values in the sub-intervals.

Investigating catalytic oxidative desulfurization of model fuel using hollow PW12/TiO2@MgCO3 and performance optimization via box-behnken design.

A facile and eco-friendly synthesis of PW12/TiO2@MgCO3 hollow tubes (PW12·âˆ¼· H3[PW12O40] = polyoxometalate) using a soluble and reusable MgCO3·3H2O micro-rods template was reported for the first time. The resultant hollow tubes were characterized by Fourier transform infrared spectroscopy (FT-IR), UV-visible spectroscopy, powder X-ray diffraction (PXRD), energy-dispersive X-ray spectroscopy (EDX), and scanning electron microscopy (SEM), which indicated that the [PW12O40]3- structure remained intact within the hollow tubes. Furthermore, the specific surface area (88.982 m2/g) and average pore size (2.6 nm) of the PW12/TiO2@MgCO3 hollow tubes were calculated using the Brunauer-Emmett-Teller (BET) analysis. This study explored the catalytic performance of PW12/TiO2@MgCO3 hollow tubes using a three-level Box-Behnken design (BBD), through which optimization curves were designed. The desulfurization of model fuel using hollow tubes was optimally performed when the catalyst dose, time, temperature, and oxidant/sulfur (O/S) were 20-80 gm, 80-120 min, 25-80 °C and 3-8 molar ratio, respectively. These results were further processed, and the experiments were replicated twenty-nine times using a model based on two quadratic polynomials to create a response surface methodology (RSM). This permits a mathematical correlation linking the desulfurization and experimental parameters. The optimal performance of reaction mixture was evaluated to be 80 mg for catalyst concentration, 25 °C of temperature, reaction time of 100 min, and 5.5 for oxidant/sulfur molar ratio from 20 mL of octane simulation oil containing 350 ppm dibenzothiophene (DBT). The predicted desulfurization rate of the model fuel under these optimal conditions was 95.3%. The correspondence between the experimental results and predicted values was verified based on regression analysis, with an R2 value greater than 0.99. These hollow tubes could be used for their desulfurization properties ten times a row without significantly reducing catalytic activity.

Photocatalytic degradation of industrial dye using hybrid filler impregnated poly-sulfone membrane and optimizing the catalytic performance using Box-Behnken design.

Mixed Matrix Membranes have gained significant attention over the past few years due to their diverse applications, unique hybrid inorganic filler and polymeric properties. In this article, the impregnation of nano-hybrid filler (polyoxometalates (∼POMs) encapsulated into the metal-organic framework (MOF) âˆ¼ PMOF) on the polysulfone membrane (∼PSF) was done, resulting in a mix matrix membrane (∼PMOF@PSF). The developed structure was characterized by Fourier transform infrared (FT-IR), powder X-ray diffraction (PXRD), thermogravimetric analysis (TGA), scanning electron microscopy (SEM), and transmission electron microscopes (TEM). The results confirmed that the nano-hybrid filler was successfully fabricated on the surface of PSF. Different loading ratios of nano-hybrid filler (5%, 10%, 20%, 30%, and 40%) were used for impregnation. The study's objective was to enhance catalytic performance using optimization curves designed using a three-level Box-Behnken Design (BBD) simulation. The photodegradation of Methylene Blue (∼MB) was studied against PMOF@PSF30% and was found to perform optimally when the concentration of catalyst, time of degradation, and temperature were 0.05-0.15 gm, 40-120 min, and 30-70 °C respectively. These experiments were replicated 15 times, and obtained results were further processed using a two-quadratic polynomial model to develop response surface methodology (RSM), which allowed for a functional relationship between the decolorization and experimental parameters. The optimal performance of the reaction mixture was calculated to be 0.15 gm for concentration, 70 °C for temperature, with an 80 min reaction time. Under these optimal conditions, the predicted decolorization of MB was 98.09%. Regression analysis with R2 > 0.99 verified the fit of experimental results with predicted values. The PMOF@PSF PSF30% demonstrated excellent reusability as its dye degradation properties were significantly unaffected after ten cycles.