In this paper, we introduce the concept of coupled type and cyclic coupled type fuzzy cone contraction mappings in fuzzy cone metric spaces. We establish some coupled fixed point results without the mixed monotone property, and also present some coupled fixed results using the partial order metric in the said space. We present some strong coupled fixed point theorems using cyclic coupled type fuzzy cone contraction mappings in fuzzy cone metric spaces. Moreover, we present an application of nonlinear integral equations for the existence of a unique solution to support our work.

Abstractq-ROPFLS, including numeric and linguistic data, has a wide range of applications in handling uncertain information. This article aims to investigate q-ROPFL correlation coefficient based on the proposed information energy and covariance formulas. Moreover, considering that different q-ROPFL elements may have varying criteria weights, the weighted correlation coefficient is further explored. Some desirable characteristics of the presented correlation coefficients are also discussed and proven. In addition, some theoretical development is provided, including the concept of composition matrix, correlation matrix, and equivalent correlation matrix via the proposed correlation coefficients. Then, a clustering algorithm is expanded where data is expressed in q-ROPFL form with unknown weight information and is explained through an illustrative example. Besides, detailed parameter analysis and comparative study are performed with the existing approaches to reveal the effectiveness of the framed algorithm.

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The detection of diseases affecting wheat is very important as it relates to the issue of food security, which poses a serious threat to human life. Recently, farmers have heavily relied on modern systems and techniques for the control of the vast agricultural areas. Computer vision and data processing play a key role in detecting diseases that affect plants, depending on the images of their leaves. In this article, Fuzzy- logic based Histogram Equalization (FHE) is proposed to enhance the contrast of images. The fuzzy histogram is applied to divide the histograms into two subparts of histograms, based on the average value of the original image, then equalize them freely and independently to conserve the brightness of the image. The proposed method was evaluated using two well-known parameters: Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR). The best results were reflected by MSE = 0.071 and PSNR =39.58 for the Mildew Powdery disease. It is impressive to recognize that the proposed method yielded clear positive outcomes through producing better contrast enhancement while preserving the details of the original image, as confirmed by the subjective metrics.

This paper presents three volume of fluid (VoF)-based methods for large eddy simulations of atomizing sprays with different treatments of the unresolved interface. The turbulent filtered VoF model uses conventional turbulent viscosity models to close the combined interfacial and turbulent sub-grid fluctuations. The hybrid turbulence filtering and artificial compression model includes an additional artificial compression term that is applied along regions where the liquid–air interface is continuous, while conventional turbulence filtering is activated in regions with discrete liquid objects. The new explicit volume diffusion model (EVD) is based on the concept of averaging the VoF equations over explicitly defined physical volumes that are independent of the numerical grid. Closure models of the sub-volume flux and stress terms introduce explicit volume diffusion and explicit volume viscosity that are physically defined and linked to the volume size. Numerical convergence is achieved by reducing the grid size while keeping the explicit volume size constant. The models are tested for two experimental atomizing spray cases with different Weber numbers. The superior numerical convergence of the EVD model is demonstrated by analysis of the mean and rms of the volume fraction and velocity fields. Two models for the surface tension force are investigated for the EVD simulations. Compared with the simple surface tension model which neglects sub-grid fluctuations, an improved volume-averaged model based on fractal properties of wrinkled sub-volume interfaces gives better predictions of the mean volume fraction relative to the experimental data but requires selection of a model constant.