Showing papers in "alexandria engineering journal in 2022"

A new comparative study on the general fractional model of COVID-19 with isolation and quarantine effects

Abstract: A generalized version of fractional models is introduced for the COVID-19 pandemic, including the effects of isolation and quarantine. First, the general structure of fractional derivatives and integrals is discussed; then the generalized fractional model is defined from which the stability results are derived. Meanwhile, a set of real clinical observations from China is considered to determine the parameters and compute the basic reproduction number, i.e., R0≈6.6361. Additionally, an efficient numerical technique is applied to simulate the new model and provide the associated numerical results. Based on these simulations, some figures and tables are presented, and the data of reported cases from China are compared with the numerical findings in both classical and fractional frameworks. Our comparative study indicates that a particular case of general fractional formula provides a better fit to the real data compared to the other classical and fractional models. There are also some other key parameters to be examined that show the health of society when they come to eliminate the disease.

On a new and generalized fractional model for a real cholera outbreak

Abstract: In this paper, a new mathematical model involving the general form of Caputo fractional derivative is studied for a real case of cholera outbreak. Fundamental properties of the new model including the equilibrium points as well as the basic reproduction number are explored. Also, an efficient approximation scheme on the basis of product-integration rule is established to solve the new model. Several kernel functions for the general fractional derivative are tested, and the results are compared with the real data of a cholera outbreak in Yemen. As a consequence, we find a special case in which the aforesaid outbreak is described better, for the corresponding numerical simulations are closer to the real data than the other classical and fractional frameworks. Next, we apply the most realistic model to investigate the effect of vaccination on the considered cholera outbreak. Simulation results show that earlier vaccination could reduce the number of infected individuals effectively, so mortality would have been reduced considerably if the vaccination had been performed earlier.

A review of the hybrid artificial intelligence and optimization modelling of hydrological streamflow forecasting

Abstract: Ever since the first introduction of Artificial Intelligence into the field of hydrology, it has further generated immense interest in researching aspects for further improvements to hydrology. This can be seen in the rising number of related works published. This culminated further with the combination of pioneering optimization techniques. Who would have thought that the birds and the bees can offer advances in the mathematical sciences and so have the ants too? The ingenuity of humans is spelled out in the algorithms that mimic many natural activities, like pack hunting by the wolves! This review paper serves to broadcast more of the intriguing interest in newfound procedures in optimal forecasting. Reservoirs are the main and most efficient water storage facilities for managing uneven water distribution. However, due to the major global climate changes which affect rainfall trend and weather, it has been a necessity to find an alternative solution for effective conventional water balance. A multifunctional reservoir operation appears to require the operator to make wise decisions to achieve an optimal reservoir operation. One of the most important aspects of all this is the forecasting of streamflows. For this, Artificial Intelligence (AI) seems to be the best alternative solution; as in the past three decades, there has been a drastic increase in building and developing AI models for forecasting and modelling unstable patterns in various hydrological fields. Nevertheless, AI models are also required to be optimized in tandem to achieve the best result, leading thus to the desirous forming of hybrid models between a standalone AI model and optimization techniques. This comprehensive study categorizes machine learning into three main categories, together with the optimization techniques, and will next explore the various AI model used for different hydrology fields along with the most common optimization techniques. Summarization of findings under every section is provided. Some advantages and disadvantages found through literature reviews are summarized for ease of reference. Finally, future recommendations and overall conclusions drawn from the results of researchers are included. This current review focuses on papers from high-impact factor publications based on 10 years starting from (2009 to 2020).

Unsteady mixed convection flow of magneto-Williamson nanofluid due to stretched cylinder with significant non-uniform heat source/sink features

Abstract: This analysis reports an unsteady and incompressible flow of Williamson nanoliquid in presence of variable thermal characteristics are persuaded by a permeable stretching cylinder. The flow field investigation is established with the effect of mixed convection and non-uniform heat source/sink on flow and heat transfer. On the cylinder surface, the analysis is inspected with utilization of zero mass flux constraints. By using the appropriate similarity variables, the framed equations for the energy, momentum and mass is converted into non-linear ODEs. The numerical communication of the boundary value problem is successfully implemented using a computer algorithm programmed into the fifth Runge-Kutta scheme. Additionally, the wall shear factor and rate of heat transfer are calculated in two different cases namely, with curvature and without curvature. In addition, the results obtained are confirmed by making comparisons with previously published articles and we found an excellent match that guarantees the indemnity of current communication. A comprehensive change in velocity, temperature and concentration is examined for involved parameters like local Weissenberg number, space dependent heat source constant, magnetic number, curvature constant, thermophoretic parameter, buoyancy parameter, Brownian motion parameter, Prandtl number, Schmidt number, unsteadiness parameter, reaction rate parameter, activation energy parameter and temperature difference parameter. A reduction in velocity is observed for unsteady parameter and buoyancy constant. An enhanced nanofluid temperature is noted for space dependent heat source parameter, time dependent heat source parameter and unsteady parameter. Moreover, the nanofluid concentration is increases for temperature difference parameter while reverse observations are noticed for chemical reaction rate.

Differential evolution: A recent review based on state-of-the-art works

Abstract: Differential evolution (DE) is a popular evolutionary algorithm inspired by Darwin’s theory of evolution and has been studied extensively to solve different areas of optimisation and engineering applications since its introduction by Storn in 1997. This study aims to review the massive progress of DE in the research community by analysing the 192 articles published on this subject from 1997 to 2021, particularly studies in the past five years. The methodology used to search for relevant DE papers and an overview of the original DE are firstly explained. Recent advances in the modifications proposed to enhance the effectiveness and efficiency of the original DE are reviewed by analysing the strengths and weaknesses of each published work, followed by the potential applications of these DE variants in solving different real-world engineering problems. In contrast to most existing DE review papers, additional analyses are performed in this survey by investigating the impacts of various parameter settings on given DE variants to identify their optimal values required for solving certain problem classes. The qualities of modifications incorporated into selected DE variants are also evaluated by measuring the performance gains achieved in terms of search accuracy and/or efficiency against the original DE. The additional surveys conducted in this study are anticipated to provide more insightful perspectives for both beginners and experts of DE research, enabling their better understanding about current research trends and new motivations to outline appropriate strategic planning for future development works.

Unsteady mixed convection flow of magneto-Williamson nanofluid due to stretched cylinder with significant non-uniform heat source/sink features

Abstract: This analysis reports an unsteady and incompressible flow of Williamson nanoliquid in presence of variable thermal characteristics are persuaded by a permeable stretching cylinder. The flow field investigation is established with the effect of mixed convection and non-uniform heat source/sink on flow and heat transfer. On the cylinder surface, the analysis is inspected with utilization of zero mass flux constraints. By using the appropriate similarity variables, the framed equations for the energy, momentum and mass is converted into non-linear ODEs. The numerical communication of the boundary value problem is successfully implemented using a computer algorithm programmed into the fifth Runge-Kutta scheme. Additionally, the wall shear factor and rate of heat transfer are calculated in two different cases namely, with curvature and without curvature. In addition, the results obtained are confirmed by making comparisons with previously published articles and we found an excellent match that guarantees the indemnity of current communication. A comprehensive change in velocity, temperature and concentration is examined for involved parameters like local Weissenberg number, space dependent heat source constant, magnetic number, curvature constant, thermophoretic parameter, buoyancy parameter, Brownian motion parameter, Prandtl number, Schmidt number, unsteadiness parameter, reaction rate parameter, activation energy parameter and temperature difference parameter. A reduction in velocity is observed for unsteady parameter and buoyancy constant. An enhanced nanofluid temperature is noted for space dependent heat source parameter, time dependent heat source parameter and unsteady parameter. Moreover, the nanofluid concentration is increases for temperature difference parameter while reverse observations are noticed for chemical reaction rate.

Review of artificial neural networks for gasoline, diesel and homogeneous charge compression ignition engine

Abstract: In automotive applications, artificial neural network (ANN) is now considered as a favorable prediction tool. Since it does not need an understanding of the system or its underlying physics, an ANN model can be beneficial especially when the system is too complicated, and it is too costly to model it using a simulation program. Therefore, using ANN to model an internal combustion engine has been a growing research area in the last decade. Despite its promising capabilities, the use of ANN for engine applications needs deeper examination and further improvement. Research in ANN may reach its maturity and be saturated if the same approach is applied repeatedly with the same network type, training algorithm and input–output parameters. This review article critically discusses recent application of ANN in ICE. The discussion does not only include its use in the conventional engine (gasoline and diesel engine), but it also covers the ANN application in advanced combustion technology i.e., homogeneous charge compression ignition (HCCI) engine. Overall, ANN has been successfully applied and it now becomes an indispensable tool to rapidly predict engine performance, combustion and emission characteristics. Practical implications and recommendations for future studies are presented at the end of this review.

Banana stem based activated carbon as a low-cost adsorbent for methylene blue removal: Isotherm, kinetics, and reusability

Abstract: A low-cost activated carbon from the banana stem (ACBS) was produced to contribute to environmental preservation in removing methylene blue from wastewater. It is originated from abundant agricultural waste and produced at moderate pyrolysis temperature and short pyrolisis time. In the ACBS production, the banana stem was impregnated with H3PO4 solution as the activating agent and followed by pyrolysis at 400 °C for a rapid time of 15 min. The treatment significantly improved the ACBS surface area to 837.453 m2/g. The influence of the ACBS dose and initial concentration of dye solution at various contact times were investigated in this study. The utilization of ACBS in low doses exhibited high removal efficiency of methylene blue (0.05 to 0.3 g/100 mL). It can remove methylene blue completely in 90 min of adsorption with an initial concentration of 50 g/mL. High removal efficiencies are still also demonstrated at higher initial concentrations with 99.762% removal for the initial concentration of 200 g/mL. Equilibrium adsorption data had the best agreement to the Freundlich isotherm model and pseudo-second-order kinetics model. It is predicted that ACBS has a maximum adsorption capacity of 101.01 mg/g. ACBS is an environmentally benign and favorable adsorbent in methylene blue removal and also effective for repeated usage up to 6 consecutive times with no desorption step.

Computational assessment of hybrid nanofluid flow with the influence of hall current and chemical reaction over a slender stretching surface

Abstract: The current study addresses the flow of steady electrically conducting hybrid nanofluid (HNF) across an impermeable slender stretchable sheet. The flow distribution takes into consideration the effects of variable magnetic fields, heat production, Hall current and chemical reactions. A computational model is established for the purpose to amplify the energy communication rate and enhance the productivity and performance of thermal energy propagation for several industrial and biological purposes. The hybrid nanofluid is comprised of silver and magnesium oxide nanomaterials in the working fluid water. Among transition metals and alloys, magnesium oxide and silver nanoparticles (NPs) have been extensively documented to have broad-spectrum antibacterial properties. Silver NPs are the most extensively employed inorganic NP, having several applications in biomaterial detection and antibacterial actions. The scenario has been expressed as a system of PDEs. Which are simplified to the system of ODEs through similarity replacements. The computing approach PCM is used to subsequently evaluate the acquired 1st order differential equations. The outcomes are checked with the bvp4c package and existing literature for consistency and validity. It has been noticed that the axial velocity profile enhances with the effect of Hall current m and velocity power index constraint n, while reducing with the variation of nanoparticles volume friction ϕ1,ϕ2 and slender sheet wall thickness parameter δ.

Automatic diagnosis of COVID-19 disease using deep convolutional neural network with multi-feature channel from respiratory sound data: Cough, voice, and breath

Abstract: The problem of respiratory sound classification has received good attention from the clinical scientists and medical researcher’s community in the last year to the diagnosis of COVID-19 disease. The Artificial Intelligence (AI) based models deployed into the real-world to identify the COVID-19 disease from human-generated sounds such as voice/speech, dry cough, and breath. The CNN (Convolutional Neural Network) is used to solve many real-world problems with Artificial Intelligence (AI) based machines. We have proposed and implemented a multi-channeled Deep Convolutional Neural Network (DCNN) for automatic diagnosis of COVID-19 disease from human respiratory sounds like a voice, dry cough, and breath, and it will give better accuracy and performance than previous models. We have applied multi-feature channels such as the data De-noising Auto Encoder (DAE) technique, GFCC (Gamma-tone Frequency Cepstral Coefficients), and IMFCC (Improved Multi-frequency Cepstral Coefficients) methods on augmented data to extract the deep features for the input of the CNN. The proposed approach improves system performance to the diagnosis of COVID-19 disease and provides better results on the COVID-19 respiratory sound dataset.

Heat transfers thermodynamic activity of a second-grade ternary nanofluid flow over a vertical plate with Atangana-Baleanu time-fractional integral

Abstract: The impact of the Atangana-Baleanu (AB) time-fractional integral on second-grade fluid with ternary nanoparticle suspension across an infinite vertical plate was studied in this paper. By generalized Fourier's law, the generalized fractional constitutive equation for the thermal flux explains a thermal process with memory. Closed-form solutions are calculated using Laplace transform and represented using Lorenzo and Hartley G–functions and integral forms. The numerical effects of physical and fractional parameters are presented.

Comparative analysis of Gated Recurrent Units (GRU), long Short-Term memory (LSTM) cells, autoregressive Integrated moving average (ARIMA), seasonal autoregressive Integrated moving average (SARIMA) for forecasting COVID-19 trends

Abstract: Several machine learning and deep learning models were reported in the literature to forecast COVID-19 but there is no comprehensive report on the comparison between statistical models and deep learning models. The present work reports a comparative time-series analysis of deep learning techniques (Recurrent Neural Networks with GRU and LSTM cells) and statistical techniques (ARIMA and SARIMA) to forecast the country-wise cumulative confirmed, recovered, and deaths. The Gated Recurrent Units (GRU), Long Short-Term Memory (LSTM) cells based on Recurrent Neural Networks (RNN), ARIMA and SARIMA models were trained, tested, and optimized to forecast the trends of the COVID-19. We deployed python to optimize the parameters of ARIMA which include (p, d, q) representing autoregressive and moving average terms and parameters of SARIMA model include additional seasonal terms which are denoted by (P, D, Q). Similarly, for LSTM and GRU based RNN models’ parameters (number of layers, hidden size, learning rate and number of epochs) are optimized by deploying PyTorch machine learning framework. The best model was chosen based on the lowest Mean Square Error (MSE) and Root Mean Squared Error (RMSE) values. For most of the time-series data of the countries, deep learning-based models LSTM and GRU outperformed statistical ARIMA and SARIMA models, with an RMSE values that are 40 folds less than that of the ARIMA models. But for some countries statistical (ARIMA, SARIMA) models outperformed deep learning models. Further, we emphasize the importance of various factors such as age, preventive measures and healthcare facilities etc. that play vital role on the rapid spread of COVID-19 pandemic.

Automatic Diagnosis of COVID-19 Disease using Deep Convolutional Neural Network with Multi-Feature Channel from Respiratory Sound Data: Cough, Voice, and Breath

Abstract: The problem of respiratory sound classification has received good attention from the clinical scientists and medical researcher’s community in the last year to the diagnosis of COVID-19 disease. The Artificial Intelligence (AI) based models deployed into the real-world to identify the COVID-19 disease from human-generated sounds such as voice/speech, dry cough, and breath. The CNN (Convolutional Neural Network) is used to solve many real-world problems with Artificial Intelligence (AI) based machines. We have proposed and implemented a multi-channeled Deep Convolutional Neural Network (DCNN) for automatic diagnosis of COVID-19 disease from human respiratory sounds like a voice, dry cough, and breath, and it will give better accuracy and performance than previous models. We have applied multi-feature channels such as the data De-noising Auto Encoder (DAE) technique, GFCC (Gamma-tone Frequency Cepstral Coefficients), and IMFCC (Improved Multi-frequency Cepstral Coefficients) methods on augmented data to extract the deep features for the input of the CNN. The proposed approach improves system performance to the diagnosis of COVID-19 disease and provides better results on the COVID-19 respiratory sound dataset.

Effect of magnetic field and thermal radiation on natural convection in a square cavity filled with TiO2 nanoparticles using Tiwari-Das nanofluid model

Abstract: Numerical investigation on natural convection heat transfer of Tiwari - Das model nanofluid inside a square cavity with thermal radiation and magnetic field is carried out in this analysis. Ethylene Glycol E G is considered as base fluid and T i O 2 (Titanium Oxide) considered as nanoparticles for the present investigation. The side horizontal walls of cavity are assumed to be adiabatic and isothermal conditions on both sides walls are considered in this analysis. The finite difference method is implemented to solve the governing non-linear partial differential equations representing momentum and temperature equations. The sway of volume fraction parameter ( 0.01 ≤ ϕ ≤ 0.09 ) , magnetic field parameter ( 1.0 ≤ M ≤ 3.0 ) , Rayleigh number ( 100 ≤ R a ≤ 1000 ) , radiation parameter ( 0.1 ≤ R ≤ 0.9 ) , Reynolds number ( 0 . 1 ≤ R e ≤ 0 . 5 ) and Prandtl number ( 5.2 ≤ P r ≤ 7.2 ) on T i O 2 - E G nanofluid flow and heat transfer is illustrated through graphs. Furthermore, the codes of average Nusselt number with dissimilar values of pertinent parameters are also calculated and results are depicted through graphs. The result shows that, temperature of T i O 2 - E G nanofluid escalates inside the cavity with higher values of (M). Higher heat can be transferred from hot wall to cold wall when radiation parameter (R) intensifies.

Magnetohydrodynamics (MHD) boundary layer flow of hybrid nanofluid over a moving plate with Joule heating

Abstract: The proficiency of hybrid nanoparticles in augmenting the heat transfer has fascinated many researchers to further analysing the working fluid. The present paper is focused on the MHD hybrid nanofluid flow with heat transfer on a moving plate with Joule heating. The combination of metal (Cu) and metal oxide (Al2O3) nanoparticles with water (H2O) as the base fluid is used for the analysis. Similarity transformation reduces the complexity of the PDEs into a system of ODEs, which is then solved numerically using the function bvp4c from MATLAB for different values of the governing parameters. Two solutions are obtained when the plate is moved oppositely from the free stream flow. Analysis of flow stability unveils the first solution as the real physical solution, which is realizable in practice. From physical perspective, the real solution must be available for all cases of λ which affirms the finding from stability analysis. An upsurge of suction’s strength and magnetic parameter enhances the heat transfer operation and extends the critical value λ c . Meanwhile, there is no change on the critical value when the Eckert number is added. This study is important in determining the thermal behavior of Cu-Al2O3/H2O when the physical parameters like magnetic field and Joule heating are embedded. The results are new and original with many practical applications in the modern industry.

Grasping posture of humanoid manipulator based on target shape analysis and force closure

Abstract: With the diversity of manipulator grasping methods and the complexity of the unstructured environment, the grasping planning of the target object is very complicated. However, the external factor for the manipulator grasping is primarily the external shape of the target object. Therefore, it is of great significance to establish a grasping system based on target shape analysis. This paper proposes a method for determining the grasping posture of manipulator based on shape analysis and force closure. The irregular or complex objects are reduced to a combination of some basic shapes. The 3D data points of the object are split into blocks and each part is fitted to sphere, cylinder or rectangle by a best-fit algorithm. This allows the grasping posture of the manipulator to be determined quickly. The grasping characteristics of the object are analyzed and the grasping surface is described qualitatively by means of a superellipse. The grasping of objects is simplified according to the stable grasping condition of force closure. The force spiral space for grasping defines the grasping quality of the manipulator. The best grasping posture is obtained by evaluating the indicators. This method eliminates complex training processes, reduces the complexity of robotic grasping and highly universal.

Self-adjusting force/bit blending control based on quantitative factor-scale factor fuzzy-PID bit control

Abstract: In order to improve the operation performance and environmental adaptability of the manipulator, it is essential to control the position and force of the manipulator simultaneously. This paper proposed a hybrid control method of auto-dynamic bit based on bit control to realize flexible grasping of manipulator in unknown environment. The constraint conditions and control types of the hybrid force/bit control are analyzed firstly, and the dynamic model when contacting with the environment is established. Then, an improved position controller based on conventional fuzzy-PID control strategy is introduced to realize the hybrid force/bit control based on the position control by combining constraint estimation method and impedance force control. Finally, the control performance of the force/bit hybrid controller is verified by simulation experiments. The results show that the designed control strategy has relatively ideal force control precision and higher trajectory tracking ability, and the manipulator model has better compliance.

On doubly stratified bioconvective transport of Jeffrey nanofluid with gyrotactic motile microorganisms

Abstract: Nanofluids have already proven great potential in the thermal amplification of several manufacturing industries and have been widely employed in energy technologies in recent times. Therefore, the current framework investigates the characteristics of the Jeffrey nanofluid flow with the influence of activation energy and motile microorganisms over a sheet is considered. The influence of the magnetic field is another important physical parameter in the flow analysis and has been regarded in this review. The remarkable properties of nanofluid are demonstrated by thermophoresis and Brownian motion characteristics. Thermophoresis has relevance in mass transport processes in many higher temperature gradient operating systems. An appropriate similarities transformation is utilized make convenient to partial differential equation into ordinary differential equations. The well-known shooting tactic is utilized to estimate numerical outcomes of obtained ordinary system of flow. The governing dimensionless equations are integrated subject to the aid of the bvp4c scheme in the built-in software of MATLAB to find out the solution. The procedure assumed employing MATLAB. The characteristics of physical parameters against the velocity of fluid, temperature, concentration, and motile microorganisms are elaborated through graphs and tables. The dynamic physical declaration of attained results reveals that buoyancy ratio parameter and bioconvection Rayleigh number plays a vibrant role in the declining flow of Jeffery nanofluid while contrasting nature is analyzed for mixed convection parameter. The increasing behavior of temperature is observed for larger variations of heat sink/source parameters and thermophoresis parameter while it shows conflicting nature for increasing values of Prandtl number. The concentration of nanoparticle is reduced with the advanced values of Prandtl number, Lewis number and Brownian motion, perceived from the results. The mounting valuation of the Peclet number and Bioconvection Lewis number caused a reduction in motile microorganism concentration. The envisaged hypotheses could be beneficial for modifications to extrusion systems, bio-mimetic systems, efficient energy generation, bio-molecules and improved production systems.

Banana stem based activated carbon as a low-cost adsorbent for methylene blue removal: Isotherm, kinetics, and reusability

Abstract: A low-cost activated carbon from the banana stem (ACBS) was produced to contribute to environmental preservation in removing methylene blue from wastewater. It is originated from abundant agricultural waste and produced at moderate pyrolysis temperature and short pyrolisis time. In the ACBS production, the banana stem was impregnated with H 3 PO 4 solution as the activating agent and followed by pyrolysis at 400 °C for a rapid time of 15 min. The treatment significantly improved the ACBS surface area to 837.453 m 2 /g. The influence of the ACBS dose and initial concentration of dye solution at various contact times were investigated in this study. The utilization of ACBS in low doses exhibited high removal efficiency of methylene blue (0.05 to 0.3 g/100 mL). It can remove methylene blue completely in 90 min of adsorption with an initial concentration of 50 g/mL. High removal efficiencies are still also demonstrated at higher initial concentrations with 99.762% removal for the initial concentration of 200 g/mL. Equilibrium adsorption data had the best agreement to the Freundlich isotherm model and pseudo-second-order kinetics model. It is predicted that ACBS has a maximum adsorption capacity of 101.01 mg/g. ACBS is an environmentally benign and favorable adsorbent in methylene blue removal and also effective for repeated usage up to 6 consecutive times with no desorption step.

Effect of magnetic field and thermal radiation on natural convection in a square cavity filled with TiO2 nanoparticles using Tiwari-Das nanofluid model

Abstract: Numerical investigation on natural convection heat transfer of Tiwari - Das model nanofluid inside a square cavity with thermal radiation and magnetic field is carried out in this analysis. Ethylene GlycolEGis considered as base fluid and TiO2 (Titanium Oxide) considered as nanoparticles for the present investigation. The side horizontal walls of cavity are assumed to be adiabatic and isothermal conditions on both sides walls are considered in this analysis. The finite difference method is implemented to solve the governing non-linear partial differential equations representing momentum and temperature equations. The sway of volume fraction parameter(0.01≤ϕ≤0.09), magnetic field parameter(1.0≤M≤3.0), Rayleigh number (100≤Ra≤1000), radiation parameter (0.1≤R≤0.9), Reynolds number (0.1≤Re≤0.5) and Prandtl number (5.2≤Pr≤7.2) on TiO2-EG nanofluid flow and heat transfer is illustrated through graphs. Furthermore, the codes of average Nusselt number with dissimilar values of pertinent parameters are also calculated and results are depicted through graphs. The result shows that, temperature of TiO2-EG nanofluid escalates inside the cavity with higher values of (M). Higher heat can be transferred from hot wall to cold wall when radiation parameter (R) intensifies.

Magnetohydrodynamics (MHD) boundary layer flow of hybrid nanofluid over a moving plate with Joule heating

Abstract: The proficiency of hybrid nanoparticles in augmenting the heat transfer has fascinated many researchers to further analysing the working fluid. The present paper is focused on the MHD hybrid nanofluid flow with heat transfer on a moving plate with Joule heating. The combination of metal (Cu) and metal oxide (Al 2 O 3 ) nanoparticles with water (H 2 O) as the base fluid is used for the analysis. Similarity transformation reduces the complexity of the PDEs into a system of ODEs, which is then solved numerically using the function bvp4c from MATLAB for different values of the governing parameters. Two solutions are obtained when the plate is moved oppositely from the free stream flow. Analysis of flow stability unveils the first solution as the real physical solution, which is realizable in practice. From physical perspective, the real solution must be available for all cases of λ which affirms the finding from stability analysis. An upsurge of suction’s strength and magnetic parameter enhances the heat transfer operation and extends the critical value λ c . Meanwhile, there is no change on the critical value when the Eckert number is added. This study is important in determining the thermal behavior of Cu-Al 2 O 3 /H 2 O when the physical parameters like magnetic field and Joule heating are embedded. The results are new and original with many practical applications in the modern industry.

Radiation effect on MHD Casson fluid flow over an inclined non-linear surface with chemical reaction in a Forchheimer porous medium

Abstract: The numerical study of the impact of thermal radiation, chemical reaction, and heat source on MHD Casson fluid flow over a nonlinear inclined stretching surface with velocity slip in a Forchheimer porous medium is presented in this paper. The controlling equations are converted into nonlinear ODE's with appropriate similarity variables. Numerical solutions of the nonlinear ODE’s are solved by the Runge-Kutta method along with the shooting technique with MATLAB. It is vital to investigate the flow of Casson fluids (such drilling muds, clay coatings, various suspensions and certain lubricating oils, thermoplastic melts, and a variety of colloids) in the incidence of heat transfer in order to optimize the preparation of toffee, chocolate, and other delicacies. Numerical findings were given via graphs and tables for various intervals of the physical variables involved for velocity, temperature, and concentration profiles in addition to this, the coefficient of skin friction, Nusselt number, and local Sherwood number are also discussed. It is inferred from the graphs that the temperature of the plate decreases with increasing the values of the radiation parameter and Forchheimer porous medium parameter. The concentration is decreased in the presence of chemical reaction and Schmidt number. To ensure the validity of our findings, we compared them to previously published work and found significant agreement.

A novel ant colony algorithm for solving shortest path problems with fuzzy arc weights

Abstract: The shortest path (SP) problem constitutes one of the most prominent topics in graph theory and has practical applications in many research areas such as transportation, network communications, emergency services, and fire stations services, to name just a few. In most real-world applications, the arc weights of the corresponding SP problems are represented by fuzzy numbers. The current paper presents a fuzzy-based Ant Colony Optimization (ACO) algorithm for solving shortest path problems with different types of fuzzy weights. The weights of the fuzzy paths involving different kinds of fuzzy arcs are approximated using the α-cut method. In addition, a signed distance function is used to compare the fuzzy weights of paths. The proposed algorithm is implemented on three increasingly complex numerical examples and the results obtained compared with those derived from a genetic algorithm (GA), a particle swarm optimization (PSO) algorithm and an artificial bee colony (ABC) algorithm. The results confirm that the fuzzy-based enhanced ACO algorithm could converge in about 50% less time than the alternative metaheuristic algorithms.

On doubly stratified bioconvective transport of Jeffrey nanofluid with gyrotactic motile microorganisms

Abstract: Nanofluids have already proven great potential in the thermal amplification of several manufacturing industries and have been widely employed in energy technologies in recent times. Therefore, the current framework investigates the characteristics of the Jeffrey nanofluid flow with the influence of activation energy and motile microorganisms over a sheet is considered. The influence of the magnetic field is another important physical parameter in the flow analysis and has been regarded in this review. The remarkable properties of nanofluid are demonstrated by thermophoresis and Brownian motion characteristics. Thermophoresis has relevance in mass transport processes in many higher temperature gradient operating systems. An appropriate similarities transformation is utilized make convenient to partial differential equation into ordinary differential equations. The well-known shooting tactic is utilized to estimate numerical outcomes of obtained ordinary system of flow. The governing dimensionless equations are integrated subject to the aid of the bvp4c scheme in the built-in software of MATLAB to find out the solution. The procedure assumed employing MATLAB. The characteristics of physical parameters against the velocity of fluid, temperature, concentration, and motile microorganisms are elaborated through graphs and tables. The dynamic physical declaration of attained results reveals that buoyancy ratio parameter and bioconvection Rayleigh number plays a vibrant role in the declining flow of Jeffery nanofluid while contrasting nature is analyzed for mixed convection parameter. The increasing behavior of temperature is observed for larger variations of heat sink/source parameters and thermophoresis parameter while it shows conflicting nature for increasing values of Prandtl number. The concentration of nanoparticle is reduced with the advanced values of Prandtl number, Lewis number and Brownian motion, perceived from the results. The mounting valuation of the Peclet number and Bioconvection Lewis number caused a reduction in motile microorganism concentration. The envisaged hypotheses could be beneficial for modifications to extrusion systems, bio-mimetic systems, efficient energy generation, bio-molecules and improved production systems.

Dynamics of water conveying SWCNT nanoparticles and swimming microorganisms over a Riga plate subject to heat source/sink

Abstract: Electromagnetohydrodynamic (EMHD) is very important because of its numerous advantages such as flow control in fluidics networks, fluid pumping, thermal reactors, mixing, fluid stirring, liquid chromatography, and micro coolers. Based on the above applications in this article discussed the electromagnetic forces on the SWCNT/water flow with microorganisms over a Riga plate subject to slip effects. In addition, the uniform heat source/sink effect is used in the energy equation, as well as the thermophoretic effect in the concentration equation. The governing nonlinear system of partial differential equations (PDEs) was reduced to ordinary differential equations (ODEs) by applying the appropriate similarity variables. Hence, Runge-Kutta-Fehlberg (RKF-45) method was applied to numerically solve the extremely nonlinear system. Based on the analysis of the results, it is worth concluding that raising the role of slip effects lowers the velocity, temperature, and concentration curves, while increasing the solid volume fraction increases the temperature, concentration, and motile microorganism density.

Fabrication of ZnO-Ag bimetallic nanoparticles by laser ablation for anticancer activity

Abstract: The collidal bimentalic nanoparticles of ZnO-Ag were prepared by laser ablation technqiue. The sysenthized bimetalic nanoparticles were characterized by UV–Vis spectrophotometry, Scanning Electron Microscopy (SEM), Energy Dispersive X-ray spectrometry (EDX), Raman spectroscopy, X-ray Photoelectron Spectroscopy (XPS), and Photo-Luminescence (PL). These techniques confirmed the formation of the bimetalic nanocompiste and showed that the size distribution of the synthesized bimetallic nanoparticles varied from 30 to 130 nm. The anticancer activity was validated by measuring the cell cytotocicty by MTT (3-(4, 5-Dimethylthiazol-2-yl)-2, 5-Diphenyltetrazolium Bromide) test applying HCT-116 and HELA cancer cell line. The cell lines’ sensitivity was the highest at 10 µg/mL of ZnO-Ag composite. This indicates that the bimetallic composite ZnO-Ag prepared by laser ablation technique is suitable for cancer treatment.

Power transformer insulation system: A review on the reactions, fault detection, challenges and future prospects

Abstract: Power transformer is one of the main equipment in power transmission and distribution network. Thus, it is important to ensure optimal operation of power transformer for an efficient supply of energy to utilities. One of the main components of a power transformer is the transformer insulation system, namely, transformer insulation oil and transformer insulation paper. This review provides an in-depth discussion on the reactions that occur in the insulation system of the power transformer. These include, oxidation, hydrolysis, pyrolysis, partial discharge, and arcing. The reaction mechanisms, conditions and the relationship between these reactions are thoroughly analysed in this review. Apart from that, this review also provides an inclusive discussion on the state-of-the-art methods used to monitor the byproducts formed from the mentioned reactions. These methods were developed to overcome the limitation of conventional methods that are complex and costly. Moreover, it presents an impartial evaluation of the challenges and prospects in making the power transformer monitoring system more efficient in terms of cost and time. Information corroborated in this review is expected to provide an important roadmap for future research in monitoring the condition of power transformer.

A novel improved marine predators algorithm for combined heat and power economic dispatch problem

Abstract: This paper proposes an improved marine predators’ optimization algorithm (IMPOA) for solving the combined heat and power (CHP) economic dispatch problem. This problem provides optimal scheduling of heat and power generation supplies and pursues to minimize the overall fuel cost (OFC) supply of cogeneration units considering their operational constraints. Four test systems are considered to check the performance of both the MPOA and the proposed IMPOA. The first test system is small sized which involve 5-unit, whereas the second system is medium sized which contains 48-unit system. The third and fourth test systems are large sized systems. The third test system includes 84-unit, which are divided into 40 power-only units, 20 heat only units, and 24 CHP units. The fourth test system includes 96-unit, which are divided into 52 power-only units, 20 heat-only units, and 24 CHP units. The obtained results clearly show the capability, efficiency, and feasibility of the IMPOA with respect to other relevant optimization techniques for optimal solutions of small, medium and large-scale systems. Additionally, the convergence characteristics of the proposed IMPOA are stable and the arrival of the optimal solution is faster than the conventional MPOA.

Significance of magnetic field and activation energy on the features of stratified mixed radiative-convective couple-stress nanofluid flows with motile microorganisms

Abstract: The current non-homogeneous nanofluid flow model is carried out to scrutinize the performance of the generalized Fourier's and Fick's laws on the MHD bioconvective aspects of couple-stress nanofluid flows through a convectively heated stretching sheet in the presence of activation energy and multiple stratified boundary conditions. Herein, both the concentrations of solid nanoparticles and motile microorganisms are incorporated explicitly into the nonlinear differential expressions describing the present non-Newtonian nanofluid flow model. Besides, the combined thermal influence of the Cattaneo-Christov heat flux and thermal radiation are also discussed. From a practical point of view, the couple-stress nanofluids are useful for examining different types of thermophysical and rheological features, since this kind of enhanced fluids can clarify realistically the dynamical behavior of various liquids, like the human blood and some polymeric suspensions. For reducing the mathematical complexity of the present physical problem, several effective similarity transformations are introduced formally to simplify the resulting partial differential equations (PDEs) into a nonlinear coupled structure of ordinary differential equations (ODEs). Moreover, the transformed dimensionless self-similarity equations are then numerically solved using the built-in shooting technique with the aid of the bvp4c solver MATLAB package. Furthermore, The obtained results are authenticated with an outstanding agreement. In this respect, the engineering quantities of interest are computed extensively with a higher level of accuracy and then summarized tabularly. To illustrate the impacts of the embedded physical parameters on the profiles of velocity, temperature, nanoparticles concentration, and microorganisms concentration, various illustrations are done successfully along with detailed elucidations. As the main findings, it is found that the temperature distribution and the microorganisms concentration profile can be enhanced with the higher values of the bioconvection Rayleigh number. Similarly, it is revealed that the nanoparticles concentration sketch and the microorganisms concentration profile can be boosted up for the higher magnitudes of the buoyancy ratio parameter.

Predicting kerf quality characteristics in laser cutting of basalt fibers reinforced polymer composites using neural network and chimp optimization

Abstract: In this study, an optimized artificial intelligence model is developed to predict the kerf quality characteristics in laser cutting of basalt fibers reinforced polymer composites. The model is composed of Long Short-Term Memory (LSTM) and Chimp Optimization Algorithm (CHOA). The latter is used as an internal optimizer to obtain the optimal parameters of the network model. The developed model was compared with three other models, namely standalone LSTM, LSTM optimized using Heap-Based Optimizer (HBO), and LSTM optimized using Manta Ray Foraging Optimization (MRFO). All models were trained and tested using experimental data considering five process control factors (cutting speed, air pressure, pulse frequency, pulse width and lamp current) and three process response (kerf width, kerf taper and kerf deviation). Response surface methodology was used to design the experimental plan. The accuracy of the models was evaluated and compared to each other using different statistical measures. LSTM-CHOA succeeded to predict kerf quality characteristics of the cut composites despite of their heterogeneous and anisotropic structure and it outperformed the three other models. The root mean squared error of the predicted kerf width, kerf deviation and kerf taper using LSTM-CHOA decreased by about 27.43%, 60% and 56.6%, respectively, compared with that of standalone LSTM.