Graph-based analysis of DNA sequence comparison in closed cotton species: A generalized method to unveil genetic connections.

Graph theory provides a systematic method for modeling and analysing complicated biological data as an effective bioinformatics tool. Based on current trends, the number of DNA sequences in the DNA database is growing quickly. To determine the origin of a species and identify homologous sequences, it is crucial to detect similarities in DNA sequences. Alignment-free techniques are required for accurate measures of sequence similarity, which has been one of the main issues facing computational biologists. The current study provides a mathematical technique for comparing DNA sequences that are constructed in graph theory. The sequences of each DNA were divided into pairs of nucleotides, from which weighted loop digraphs and corresponding weighted vectors were computed. To check the sequence similarity, distance measures like Cosine, Correlation, and Jaccard were employed. To verify the method, DNA segments from the genomes of ten species of cotton were tested. Furthermore, to evaluate the efficacy of the proposed methodology, a K-means clustering method was performed. This study proposes a proof-of-model that utilises a distance matrix approach that promises impressive outcomes with future optimisations to be made to the suggested solution to get the hundred percent accurate result. In the realm of bioinformatics, this paper highlights the use of graph theory as an effective tool for biological data study and sequence comparison. It's expected that further optimization in the proposed solution can bring remarkable results, as this paper presents a proof-of-concept implementation for a given set of data using the proposed distance matrix technique.

Oblique stagnation point flow of magnetized Maxwell fluid over a stretchable Riga plate with Cattaneo-Christov heat flux and convective conditions.

The current work deals with the oblique stagnation point flow phenomenon of a rate-type Maxwell fluid with the significance of the Cattaneo-Christov double diffusion theory. The Cattaneo-Christov theory is illustrated through the modified form of Fourier's and Fick's laws. The steady magnetized flow mechanism is observed in two dimensions through a stretchable convective Riga plate. In the mass and heat transfer analysis, the consequences of chemical reactions and thermal radiation are also incorporated. With the contribution of relevant dimensionless quantities, the setup of dimensionless equations is acquired which further takes the form of nonlinear equations. The physical significance of the numerous parameters on different features of the flow phenomenon is graphically exhibited. The interesting physical quantities are computed and numerically evaluated relative to the pertinent parameters. This study reveals that the thermal relaxation time parameter lowers the rate of heat transfer, and the thermal Biot number enhances the rate of heat transport. Moreover, the Deborah number minimizes the flow field of both tangential and axial velocities.

Computations for efficient thermal performance of Go + AA7072 with engine oil based hybrid nanofluid transportation across a Riga wedge.

The demand for efficient heat transportation for the reliable functioning of mechanical processes is rising. The hybrid nanofluid emulsion is a related new concept in this research field. This communication pertains to mass and thermal transportation of Graphene oxide (Go) + AA7072 to be dissolved homogeneously in the bulk engine oil. In order to demonstrate the effectiveness of this hybrid nanofluid, a simple nanofluid Go/engine oil is also discussed. The flow of fluids occurs due to stretch in the wedge adjusted with Riga surface. The design of a hybrid nanofluid manifests the novelty of the work. The system of partial differential equations that are based on conservation principles of energy, momentum, and mass are transmuted to ordinary differential form. Numerical simulation is carried out on the Matlab platform by employing the Runge-Kutta approach along with a shooting tool. The influential parameters are varied to disclose the nature of physical quantities. The flow is accelerated with higher attributes of the modified Hartmann number, but it decelerates against the Weinberg number. The fluid's temperature rises with increment, in the concentration of nano-entities. The velocity for hybrid nanofluids is slower than that of mono nanofluids and the temperature distribution for hybrid nanofluids is greater than that of mono nanofluids. The fluid temperature increases with the concentration ϕ2 of AA7072.

Multiple slip effects on time dependent axisymmetric flow of magnetized Carreau nanofluid and motile microorganisms.

This presented work investigate the bio-convections effects of the magnetized time dependent axisymmetric flow of Carreau-nanomaterial performances with multiple slip effects over a stretching sheet. The momentum, heat, concentration and density of motile micro-organism are renovated into the system of equation via using well known similarity revolution. Well known Mathematical computational techniques and software (i.e. bvp4c and MATLAB) are used to draw graphical and tabular results. Velocity profile equation [Formula: see text], energy equation [Formula: see text], volumetric nanoparticles [Formula: see text], density motile microorganism [Formula: see text].The Carreau viscosity model is use to reduce the viscosity of fluid when [Formula: see text] and [Formula: see text]. Besides we moderate this into power law index with [Formula: see text] and [Formula: see text] partial slip condition of velocity is also instigated at the surface. Gravity dependent gyrotactic nanoparticles are utilized for well observing axisymmetric flow with convective boundary layer condition and comparatively better heat transfer rate result and applicable to maximum realistic approach.

WITHDRAWN: A Study on Variable Viscosity and Activation Energy for Unsteady MHD Bioconvection of Nano Fluid Over a Sheet with Stretch and Electric Field

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Novel Decision Modeling for Manufacturing Sustainability under Single-Valued Neutrosophic Hesitant Fuzzy Rough Aggregation Information.

We developed a multicriteria decision-making method based on the list of novel single-valued neutrosophic hesitant fuzzy rough (SV-NHFR) weighted averaging and geometric aggregation operators to address the uncertainty and achieve the sustainability of the manufacturing business. In addition, a case study on choosing the optimum elements for a sustainable manufacturing sector was carried out. The proposed decision support method is then compared to other relevant methodologies, and a validity test is performed to show the reliability and validity of the new methodology. Sustainability is one of the most important issues the world economy is facing today. Several industrial businesses have incurred large financial losses as a result of their ignorance of sustainability issues. Manufacturers in industrialized countries have done a decent job of making sure that their businesses are sustainable over the long run. Modern companies use a lot of modern technologies. These include blockchain, artificial intelligence (AI), the Internet of Things (IoT), big data analytics (BDA), and fuzzy logic (fuzziness). These modern technologies support the continuation of life, either directly or indirectly. Therefore, it is of utmost importance to concentrate on those elements that encourage the adoption of sustainability. The goal of this study is to provide a framework for using cutting-edge technology to increase the adoption of sustainability in manufacturing firms. Under the guidance of single-valued neutrosophic hesitant fuzzy rough (SV-NHFR) aggregate information, it was advised to place a strong emphasis on addressing sustainability, waste management, environmental protection, manufacturing cost savings, and chemicals and resources. The results suggest that the proposed technique can solve the inadequacy of the existing decision method by the SV-NHFR aggregation operators in terms of decision adaptability.

Diabetic retinopathy techniques in retinal images: A review.

The diabetic retinopathy is the main reason of vision loss in people. Medical experts recognize some clinical, geometrical and haemodynamic features of diabetic retinopathy. These features include the blood vessel area, exudates, microaneurysm, hemorrhages and neovascularization, etc. In Computer Aided Diagnosis (CAD) systems, these features are detected in fundus images using computer vision techniques. In this paper, we review the methods of low, middle and high level vision for automatic detection and classification of diabetic retinopathy.We give a detailed review of 79 algorithms for detecting different features of diabetic retinopathy during the last eight years.