Uncovering the stochastic dynamics of solitons of the Chaffee-Infante equation.

In this paper, we apply stochastic differential equations with the Wiener process to investigate the soliton solutions of the Chaffee-Infante (CI) equation. The CI equation, a fundamental model in mathematical physics, explains concepts such as wave propagation and diffusion processes. Exact soliton solutions are obtained through the application of the modified extended tanh (MET) method. The obtained wave figures in 3D, 2D, and contour are highly localized and determine an individual frequency shift under the behavior of sharp peak, periodic wave, and singular soliton. The MET method shows to be a valuable analytical tool for obtaining soliton solutions, essential for understanding the dynamics of nonlinear wave phenomena. Numerical simulations enable us to explore soliton solutions in two and three dimensions, shedding light on their properties over time. Our results have wide applications in various domains, including stochastic processes and nonlinear dynamics, impacting advancements in physics, engineering, finance, biology, and beyond.

m5c-iDeep: 5-Methylcytosine sites identification through deep learning.

5-Methylcytosine (m5c) is a modified cytosine base which is formed as the result of addition of methyl group added at position 5 of carbon. This modification is one of the most common PTM that used to occur in almost all types of RNA. The conventional laboratory methods do not provide quick reliable identification of m5c sites. However, the sequence data readiness has made it feasible to develop computationally intelligent models that optimize the identification process for accuracy and robustness. The present research focused on the development of in-silico methods built using deep learning models. The encoded data was then fed into deep learning models, which included gated recurrent unit (GRU), long short-term memory (LSTM), and bi-directional LSTM (Bi-LSTM). After that, the models were subjected to a rigorous evaluation process that included both independent set testing and 10-fold cross validation. The results revealed that LSTM-based model, m5c-iDeep, outperformed revealing 99.9 % accuracy while comparing with existing m5c predictors. In order to facilitate researchers, m5c-iDeep was also deployed on a web-based server which is accessible at https://taseersuleman-m5c-ideep-m5c-ideep.streamlit.app/.

Innovative thermal management in the presence of ferromagnetic hybrid nanoparticles.

In the present work, a simple intelligence-based computation of artificial neural networks with the Levenberg-Marquardt backpropagation algorithm is developed to analyze the new ferromagnetic hybrid nanofluid flow model in the presence of a magnetic dipole within the context of flow over a stretching sheet. A combination of cobalt and iron (III) oxide (Co-Fe2O3) is strategically selected as ferromagnetic hybrid nanoparticles within the base fluid, water. The initial representation of the developed ferromagnetic hybrid nanofluid flow model, which is a system of highly nonlinear partial differential equations, is transformed into a system of nonlinear ordinary differential equations using appropriate similarity transformations. The reference data set of the possible outcomes is obtained from bvp4c for varying the parameters of the ferromagnetic hybrid nanofluid flow model. The estimated solutions of the proposed model are described during the testing, training, and validation phases of the backpropagated neural network. The performance evaluation and comparative study of the algorithm are carried out by regression analysis, error histograms, function fitting graphs, and mean squared error results. The findings of our study analyze the increasing effect of the ferrohydrodynamic interaction parameter ß to enhance the temperature and velocity profiles, while increasing the thermal relaxation parameter α decreases the temperature profile. The performance on MSE was shown for the temperature and velocity profiles of the developed model about 9.1703e-10, 7.1313ee-10, 3.1462e-10, and 4.8747e-10. The accuracy of the artificial neural networks with the Levenberg-Marquardt algorithm method is confirmed through various analyses and comparative results with the reference data. The purpose of this study is to enhance understanding of ferromagnetic hybrid nanofluid flow models using artificial neural networks with the Levenberg-Marquardt algorithm, offering precise analysis of key parameter effects on temperature and velocity profiles. Future studies will provide novel soft computing methods that leverage artificial neural networks to effectively solve problems in fluid mechanics and expand to engineering applications, improving their usefulness in tackling real-world problems.

Exploring optical solitary wave solutions in the (2+1)-dimensional equation with in-depth of dynamical assessment.

The current study explores the (2+1)-dimensional Chaffee-Infante equation, which holds significant importance in theoretical physics renowned reaction-diffusion equation with widespread applications across multiple disciplines, for example, ion-acoustic waves in optical fibres, fluid dynamics, electromagnetic wave fields, high-energy physics, coastal engineering, fluid mechanics, plasma physics, and various other fields. Furthermore, the Chaffee-Infante equation serves as a model that elucidates the physical processes of mass transport and particle diffusion. We employ an innovative new extended direct algebraic method to enhance the accuracy of the derived exact travelling wave solutions. The obtained soliton solutions span a wide range of travelling waves like bright-bell shape, combined bright-dark, multiple bright-dark, bright, flat-kink, periodic, and singular. These solutions offer valuable insights into wave behaviour in nonlinear media and find applications in diverse fields such as optical fibres, fluid dynamics, electromagnetic wave fields, high-energy physics, coastal engineering, fluid mechanics, and plasma physics. Soliton solutions are visually present by manipulating parameters using Wolfram Mathematica software, graphical representations allow us to study solitary waves as parameters change. Observing the dynamics of the model, this study presents sensitivity in a nonlinear dynamical system. The applied mathematical approaches demonstrate its ability to identify reliable and efficient travelling wave solitary solutions for various nonlinear evolution equations.

Exploiting and Enhancing the Heat Transport Rate of MoS2-Ag/EO Hybrid Nanofluid Flow via a Stretching Cylinder using Extended Yamada-Ota and Xue Models.

The current model offers valuable insights for materials science, heat exchangers, renewable energy production, nanotechnology, manufacturing, medicinal treatments, and environmental engineering. The findings of this study have the potential to improve material design, increase heat transfer efficiency across various systems, enhance energy conversion processes, and drive advancements in nanotechnology, medicinal treatments, and engineering design. The goal of the current research is to analyze the effects of thermal radiation and the volume fraction of nanoparticles in MoS2-Ag/engine oil-based hybrid nanofluid flow passing through a cylinder. After performing a substantial similarity transformation, the nonlinear dimensionless framework is recast as ODEs. The Yamada-Ota and Xue models are then applied to the dimensionless equation setup, which is numerically solved using the BVP4C approach. The resulting velocity and temperature fields, corresponding to various parameters, are examined and compared across both models. This investigation demonstrates a significant variation in heat transfer rates between the Yamada-Ota and Xue models, with the former having a larger impact. The velocity and temperature fields decrease as the magnetic field parameter increases in both nanofluids. However, as the magnetic field parameter values grow, the velocity fields in the two nanofluids behave differently. The Yamada-Ota and Xue models are used to determine the behavior of the hybrid nanofluid flow over a nonlinear extended cylinder. In all situations, the velocity and temperature fields exhibit superior decay characteristics.

Identification of 6-methyladenosine sites using novel feature encoding methods and ensemble models.

N6-methyladenosine (6 mA) is the most common internal modification in eukaryotic mRNA. Mass spectrometry and site-directed mutagenesis, two of the most common conventional approaches, have been shown to be laborious and challenging. In recent years, there has been a rising interest in analyzing RNA sequences to systematically investigate mutated locations. Using novel methods for feature development, the current work aimed to identify 6 mA locations in RNA sequences. Following the generation of these novel features, they were used to train an ensemble of models using methods such as stacking, boosting, and bagging. The trained ensemble models were assessed using an independent test set and k-fold cross validation. When compared to baseline predictors, the suggested model performed better and showed improved ratings across the board for key measures of accuracy.

A novel probabilistic q-rung orthopair linguistic neutrosophic information-based method for rating nanoparticles in various sectors.

The idea of probabilistic q-rung orthopair linguistic neutrosophic (P-QROLN) is one of the very few reliable tools in computational intelligence. This paper explores a significant breakthrough in nanotechnology, highlighting the introduction of nanoparticles with unique properties and applications that have transformed various industries. However, the complex nature of nanomaterials makes it challenging to select the most suitable nanoparticles for specific industrial needs. In this context, this research facilitate the evaluation of different nanoparticles in industrial applications. The proposed framework harnesses the power of neutrosophic logic to handle uncertainties and imprecise information inherent in nanoparticle selection. By integrating P-QROLN with AO, a comprehensive and flexible methodology is developed for assessing and ranking nanoparticles according to their suitability for specific industrial purposes. This research contributes to the advancement of nanoparticle selection techniques, offering industries a valuable tool for enhancing their product development processes and optimizing performance while minimizing risks. The effectiveness of the proposed framework are demonstrated through a real-world case study, highlighting its potential to revolutionize nanoparticle selection in HVAC (Heating, Ventilation, and Air Conditioning) industry. Finally, this study is crucial to enhance nanoparticle selection in industries, offering a sophisticated framework probabilistic q-rung orthopair linguistic neutrosophic quantification with an aggregation operator to meet the increasing demand for precise and informed decision-making.

m1A-Ensem: accurate identification of 1-methyladenosine sites through ensemble models.

BACKGROUND: 1-methyladenosine (m1A) is a variant of methyladenosine that holds a methyl substituent in the 1st position having a prominent role in RNA stability and human metabolites. OBJECTIVE: Traditional approaches, such as mass spectrometry and site-directed mutagenesis, proved to be time-consuming and complicated. METHODOLOGY: The present research focused on the identification of m1A sites within RNA sequences using novel feature development mechanisms. The obtained features were used to train the ensemble models, including blending, boosting, and bagging. Independent testing and k-fold cross validation were then performed on the trained ensemble models. RESULTS: The proposed model outperformed the preexisting predictors and revealed optimized scores based on major accuracy metrics. CONCLUSION: For research purpose, a user-friendly webserver of the proposed model can be accessed through https://taseersuleman-m1a-ensem1.streamlit.app/ .

Microbic flow analysis of nano fluid with chemical reaction in microchannel with flexural walls under the effects of thermophoretic diffusion.

The current investigation examines the peristaltic flow, in curved conduit, having complaint boundaries for nanofluid. The effects of curvature are taken into account when developing the governing equations for the nano fluid model for curved channels. Nonlinear & coupled differential equations are then simplified by incorporating the long wavelength assumption along with smaller Reynolds number. The homotopy perturbation approach is used to analytically solve the reduced coupled differential equations. The entropy generation can be estimated through examining the contributions of heat and fluid viscosities. The results of velocity, temperature, concentration, entropy number, and stream functions have been plotted graphically in order to discuss the physical attributes of the essential quantities. Increase in fluid velocity within the curved conduit is noticed for higher values of thermophoresis parameter and Brownian motion parameter further entropy generation number is boosted by increasing values of Grashof number.

Biological structural study of emerging shaped nanoparticles for the blood flow in diverging tapered stenosed arteries to see their application in drug delivery.

The magnetic force effects and differently shaped nano-particles in diverging tapering arteries having stenoses are being studied in current research via blood flow model. There hasn't been any research done on using metallic nanoparticles of different shapes with water as the base fluid. A radially symmetric but axially non-symmetric stenosis is used to depict the blood flow. Another significant aspect of our research is the study of symmetrical distribution of wall shearing stresses in connection with resistive impedance, as well as the rise of these quantities with the progression of stenosis. Shaping nanoparticles in accordance with the understanding of blood flow in arteries offers numerous possibilities for improving drug delivery, targeted therapies, and diagnostic imaging in the context of cardiovascular and other vascular-related diseases. Exact solutions for different flow quantities namely velocity, temperature, resistance impedance, boundary shear stress, and shearing stress at the stenosis throat, have been assessed. For various parameters of relevance for Cu-water, the graphical results of several types of tapered arteries (i.e. diverging tapering) have been explored.

Electro osmotically interactive biological study of thermally stratified micropolar nanofluid flow for Copper and Silver nanoparticles in a microchannel.

A novel mathematical analysis is established that summits the key features of peristaltic propulsion for a non-Newtonian micropolar fluid with the electroosmosis and heat transfer enhancement using nanoparticles. In such physiological models, the channel have a symmetric configuration in accordance with the biological problem. Being mindful of this fact, we have disclosed an integrated analysis on symmetric channel that incorporates major physiological applications. The creeping flow inference is reviewed to model this realistic problem. Flow equations are model using cartesian coordinates and simplified using long wave length and low Reynolds number approximation. Nonlinear linear couple equations are solving numerically. We have studied the variation in the properties of nanofluid developed by two different types of nanoparticles (i.e. Cu and Ag nanoparticles). Graphical illustrations are unveiled to highlight the physical aspects of nanoparticles and flow parameters. The exploration demonstrates that the micro-rotation of the nano-liquid elements enhances the thermal conductivity of the fluid movement. The effect of micropolar fluid parameters on mean flow and pressure variables is also presented.

Entropy generation in a ciliary flow of an Eyring-Powell ternary hybrid nanofluid through a channel with electroosmosis and mixed convection.

Drug delivery systems, where the nanofluid flow with electroosmosis and mixed convection can help in efficient and targeted drug delivery to specific cells or organs, could benefit from understanding the behavior of nanofluids in biological systems. In current work, authors have studied the theoretical model of two-dimensional ciliary flow of blood-based (Eyring-Powell) nanofluid model with the insertion of ternary hybrid nanoparticles along with the effects of electroosmosis, magnetohydrodynamics, thermal radiations, and mixed convection. Moreover, the features of entropy generation are also taken into consideration. The system is modeled in a wave frame with the approximations of large wave number and neglecting turbulence effects. The problem is solved numerically by using the shooting method with the assistance of computational software "Mathematica" for solving the governing equation. According to the temperature curves, the temperature will increase as the Hartman number, fluid factor, ohmic heating, and cilia length increase. It is also disclosed that ternary hybrid nanoparticles result in a change in flow rate when other problem parameters are varied, and the same is true for temperature graphs. Engineers and scientists can make better use of nanofluid-based cooling systems in electronics, automobiles, and industrial processes with the aid of the study's findings.