Physicomechanical, Wettability, Corrosion, Thermal, and Microstructural Morphology Characteristics of Carbonized and Uncarbonized Bagasse Ash Waste-Reinforced Al-0.45Mg-0.35Fe-0.25Si-Based Composites: Fabrications and Characterizations.

An effort was being made to incorporate waste bagasse ash (WBA) both in carbonized and uncarbonized form into the formulation of Al6063 matrix-based metal matrix composites (MMC's) by partially substituting ceramic particles for them. In the process of developing composites, comparative research on carbonized WBA and uncarbonized bagasse powder was carried out in the role of reinforcement. Microstructure investigations revealed that carbonized WBA particles were properly distributed throughout the aluminum-base metal matrix alloy. They also had the appropriate level of wettability. The reinforcement of carbonized WBA particles in AA6063-based matrix material had a maximum tensile strength of 110 MPa and a maximal hardness of 39 BHN when 3.75 wt % of the particles were used. The deterioration in tensile strength (6.25 wt % of WBA) and the appearance of porosity and blowholes can be enumerated by tensile fractography-based scanning electron microscopy (SEM) analysis. The reinforcement of carbonized WBA particles in AA6063-based matrix material was found to have a maximal percent elongation of 14.42% and the highest fracture toughness of 15 Joules when 1.25 wt % of the particles were employed. For AA6063/3.75 wt % carbonized WBA-based MMC's, the minimum percent porosity was determined to be 5.83, and the minimum thermal expansion was found to be 45 mm3. As the percentage of reinforcement in bagasse-reinforced composites increases, the density of the material, the amount of corrosion loss, and the cost all decrease gradually. The AA6063 matrix, with a composition of 3.75 wt % carbonized WBA-based MMC's, had satisfactory specific strength and corrosion loss. The AA6063 alloy composite's microstructure analysis revealed that carbonized WBA enhanced the material's mechanical characteristics, contributing to its excellent mechanical capabilities. The results of the corrosion test showed that carbonized WBA-reinforced composites exhibited reduced weight loss due to corrosion, whereas uncarbonized bagasse powder was an inappropriate reinforcement. The SEM analysis of AA6063 alloy/3.75 wt % carbonized WBA ash reinforcement-based MMC's exposed to a 3.5 wt % NaCl solution has exhibited the development of corrosion pits as a result of localized attack by the corrosive environment. The thermal expansion test showed that the composite with uncarbonized bagasse powder as reinforcement have a high shrinkage rate in comparison with the composite with 3.75 wt %. The composite's mechanical characteristics and thermal stability were enhanced by the presence of hard phases like SiO2, Al2O3, Fe2O3, CaO, and MgO, as revealed by X-ray diffraction analysis. This made it suitable for use in a variety of applications.

Exploring Microstructural, Interfacial, Mechanical, and Wear Properties of AlSi7Mg0.3 Composites with TiMOVWCr High-Entropy Alloy Powder.

This study explored the impact of varying weight percentages of TiMoVWCr high-entropy alloy (HEA) powder addition on A356 composites produced using friction stir processing (FSP). Unlike previous research that often focused on singular aspects, such as mechanical properties, or microstructural analysis, this investigation systematically examined the multifaceted performance of A356 composites by comprehensively assessing the microstructure, interfacial bonding strength, mechanical properties, and wear behavior. The study identified a uniform distribution of TiMoVWCr HEA powder in the composition A356/2%Ti2%Mo2%V2%W2%Cr, highlighting the effectiveness of the FSP technique in achieving homogeneous dispersion. Strong bonding between the reinforcement and matrix material was observed in the same composition, indicating favorable interfacial characteristics. Mechanical properties, including tensile strength and hardness, were evaluated for various compositions, demonstrating significant improvements across the board. The addition of 2%Ti2%Mo2%V2%W2%Cr powder enhanced the tensile strength by 36.39%, while hardness improved by 62.71%. Similarly, wear resistance showed notable enhancements ranging from 35.56 to 48.89% for different compositions. Microstructural analysis revealed approximately 1640.59 grains per square inch for the A356/2%Ti2%Mo2%V2%W2%Cr processed composite at 500 magnifications. In reinforcing Al composites with Ti, Mo, V, W, and Cr high-entropy alloy (HEA) particles, each element imparted distinct benefits. Titanium (Ti) enhanced strength and wear resistance, molybdenum (Mo) contributed to improved hardness, vanadium (V) promoted hardenability, tungsten (W) enhanced wear resistance, and chromium (Cr) provided wear resistance and hardness. Anticipating the potential applications of the developed composite, the study suggests its suitability for the aerospace sector, particularly in casting lightweight yet high-strength parts such as aircraft components, engine components, and structural components, underlining the significance of the investigated TiMoVWCr HEA powder-modified A356 composites.

Physicomechanical, morphological and tribo-deformation characteristics of lightweight WC/AZ31B Mg-matrix biocomposites for hip joint applications.

Exploring high strength materials with a higher concentration of reinforcements in the alloy proves to be a challenging task. This research has explored magnesium-based composites (AZ31B alloy) with tungsten carbide reinforcements, enhancing strength for medical joint replacements via league championship optimisation. The primary objective is to enhance medical joint replacement biomaterials employing magnesium-based composites, emphasising the AZ31B alloy with tungsten carbide reinforcements. The stir casting method is utilised in the manufacture of magnesium matrix composites (MMCs), including varied percentages of tungsten carbide (WC). The mechanical characteristics, such as micro-hardness, tensile strength, and yield strength, have been assessed and compared with computational simulations. The wear studies have been carried out to analyse the tribological behaviour of the composites. Additionally, this study investigates the prediction of stress and the distribution of forces inside bone and joint structures, therefore offering significant contributions to the field of biomedical research. This research contemplates the use of magnesium-based MMCs for the discovery of biomaterials suitable for medical joint replacement. The study focuses on the magnesium alloy AZ31B, with particles ranging in size from 40 to 60 microns used as the matrix material. Moreover, the outcomes have revealed that when combined with MMCs based on AZ31B-magnesium matrix, the WC particle emerges as highly effective reinforcements for the fabrication of lightweight, high-strength biomedical composites. This study uses the league championship optimisation (LCO) approach to identify critical variables impacting the synthesis of Mg MMCs from an AZ31B-based magnesium alloy. The scanning electron microscopy (SEM) images are meticulously analysed to depict the dispersion of WC particulates and the interface among the magnesium (Mg) matrix and WC reinforcement. The SEM analysis has explored the mechanisms underlying particle pull-out, the characteristics of inter-particle zones, and the influence of the AZ31B matrix on the enhancement of the mechanical characteristics of the composites. The application of finite element analysis (FEA) is being used in order to make predictions regarding the distribution of stress and the interactions of forces within the model of the hip joint. This study has compared the physico-mechanical and tribological characteristics of WC to distinct combinations of 0%, 5%, 10% and 15%, and its impact on the performance improvements. SEM analysis has confirmed the findings' improved strength and hardness, particularly when 10%-15% of WC was incorporated. Following the incorporation of 10% of WC particles within Mg-alloy matrix, the outcomes of the study has exhibited enhanced strength and hardness, which furthermore has been evident by utilising SEM analysis. Using ANSYS, structural deformation and stress levels are predicted, along with strength characteristics such as additional hardness of 71 HRC, tensile strength of 140-150 MPa, and yield strength closer to 100-110 MPa. The simulations yield significant insights into the behaviour of the joint under various loading conditions, thus enhancing the study's significance in biomedical environments.

Investigation of surface hardness, thermostability, tribo-corrosion, and microstructural morphological properties of microwave-synthesized high entropy alloy FeCoNiMnCu coating claddings on steel.

Deposition of high entropy alloy FeCoNiMnCu on SS-304 was carried out by microwave energy for application in "solid oxide fuel-cell (SOFC) interconnects". The ball-milling has been performed by taking "Fe, Co, Ni, Mn, and Cu" in equal 20 wt. % of before deposited on SS-304 substrate. The deposited steel with 20% Fe 20% Co 20% Ni 20% Mn 20% Cu high entropy alloy (HEA) was exposed to thermal-exposure in the air for up to 10 weeks at 800 °C. The uniform cladding distribution of 20% Fe 20% Co 20% Ni 20% Mn 20% Cu HEA particles can be apparently observed on SS-304 substrate by utilizing Scanning Electron Microscope (SEM), and Optical microscopy analysis. Homogeneity in the interfacial layer was evident by employing Scanning Electron Microscope (SEM) characterization. Results have indicated that after the thermal exposure of deposited steel with 20% Fe 20% Co 20% Ni 20% Mn 20% Cu in the air for up to ten weeks at 800 °C, a "protective Cr2O3 layer", and "high-entropy spinel coating" of (Fe, Co, Ni, Mn, Cu)3O4 have been formed. During microwave cladding, the emergence of harder-phases has contributed to the raised hardness. The wear behavior after coating of 20% Fe 20% Co 20% Ni 20% Mn 20% Cu HEA on SS-304 substrate has significantly enhanced due to the strengthened wear resistance and hardness of the coatings. Findings have exhibited that the formation of (Fe, Co, Ni, Mn, Cu)3O4 phase is a potential coating material for "SOFC interconnects" applications. Moreover, the cladding of SS304 with a composition of 20% Fe, 20% Co, 20% Ni, 20% Mn, and 20% Cu has demonstrated remarkable stability under thermal expansion studies. As the findings have revealed that the composite cladding has efficiently withstand significant variations in volume when subjected to elevated temperatures for a prolonged period of time, thus, exhibiting its superior thermal stability for SOFC-interconnect applications. Furthermore, the SEM images of the cladding surface, surface hardness, and tribocorrosion behavior of the coated material have been observed to identify the 20% Fe 20% Co 20% Ni 20% Mn 20% Cu HEA coating effect on SS-304 steel-substrate.

Plant extract preparation and green synthesis of silver nanoparticles using Swertia chirata: Characterization and antimicrobial activity against selected human pathogens.

Herbal medicinal plants have been used for centuries in traditional medicine, and it is interesting to see how modern research has identified the active compounds responsible for their therapeutic effects. The green synthesis of silver nanoparticles using herbal medicinal plants, such as Swertia chirata, is particularly noteworthy due to its antimicrobial properties. In the current study, the Swertia chirata plant was collected for the first time from the region of Murree, Punjab, Pakistan. After collection, extracts were prepared in different solvents (ethanol, methanol, chloroform, and distilled water), and silver nanoparticles were synthesized by reducing silver nitrate (AgNO3). The UV-visible spectrophotometer, SEM, and EDX were used to characterize the synthesized nanoparticles in terms of their size and shape. The phytochemical analysis of crude extract was performed to determine the presence of different kinds of phytochemicals. The antibacterial activity of plant extracts and the silver nanoparticles were then assessed using the agar well diffusion method against various pathogenic bacteria. The results showed that the plant contains several phytochemicals with remarkable antioxidant potential. The antibacterial analysis revealed that silver nanoparticles and the plant extracts exhibited a significant zone of inhibition against human pathogenic bacteria (Escherichia coli, S. capitis, B. subtilis, and Pseudomonas aeruginosa) as compared to the cefixime and norfloxacin. This implies that the nanoparticles have the potential to be used in nano-medicine applications, such as drug delivery systems, as well as for their antibacterial, antifungal, and antiviral activities. Additionally, the development and application of materials and technologies at the nanometer scale opens possibilities for the creation of novel drugs and therapies. Overall, the study highlights the promising potential of herbal medicinal plants found in Murree, Punjab, Pakistan, and green-synthesized silver nanoparticles in various fields of medicine and nanotechnology.

Enhancing tribo-mechanical, microstructural morphology, and corrosion performance of AZ91D-magnesium composites through the synergistic reinforcements of silicon nitride and waste glass powder.

The present investigation has employed recycled waste glass powder (WGP) and silicon nitride (Si3N4) as reinforcing-agents within AZ91D-matrix composites. The composites were fabricated by employing the vacuum stir casting technique to mitigate the effects of oxidation and to ensure homogeneity, uniformity, and superior wettability among the AZ91D-matrix and reinforcements. A microscopic study provided confirmation of a uniform dispersion of WGP and Si3N4 particles throughout the AZ91D-matrix. The tensile strength of the AZ91D/WGP/Si3N4 composites rise with the inclusion of WGP particulates by up to 1.5 percent in AZ91D/7.5% Si3N4. However, the tensile strength of the AZ91D/9%Si3N4 composite have showed maximum value as compared to other chosen formulations/combinations in the current investigation. The tensile strength of AZ91D/1.5% WGP/7.5% Si3N4 composites has strengthened up to 12.13 percent with the comparison of base alloy AZ91D-matrix. In A1 formulated composite, the amount of WGP particulate has enhanced the hardness of the AZ91D-alloy by up to 1.5 percent. Findings, nevertheless has exhibited that the A6 formulated composite had superior outcomes in terms of hardness. The incorporation of "reinforcing-constituent particulates" with 1.5%WGP + 7.5%Si3N4 combination within the AZ91D-matrix, has further increased fatigue-strength by around 57.84 percent. A weight-loss of 0.312 mg was being unveiled for the A1 formulated fabricated composite. The weight-loss for the A6 formulated fabricated composite, however, was reported to be 0.294 mg. At 5 N loads, 2 m/s sliding speed, and 1000 m of sliding distance, the developed 1.5%WGP/7.5%Si3N4/AZ91D composites was reported to have a rate of wear, and frictional coefficient of 0.0025 mm3/m and 0.315, respectively. The investigation employing scanning electron microscopy (SEM) identified the presence of corrosion pits on the surfaces that had undergone corrosion. These pits were found to be a result of localised surface assaults occurring in corrosive environments. Additionally, SEM pictures of the worn surfaces indicated the emergence of microcracks, which may be associated to the conditions of cyclic loading. Moreover, the tensile-fractography examination for the developed 1.5%WGP/7.5%Si3N4/AZ91D composites has exhibited the brittle fracture failure, including cracks and debonding phenomena. In addition, the EDS spectra-analysis have revealed an apparent existence of the observed Mg-peak, Si-peak, Al-peak, Ca-peak, and O-peak for the 1.5%WGP/7.5%Si3N4/AZ91D composites. Furthermore, the utilisation of X-ray diffraction analysis effectively determined the existence of hard phases inside the AZ91D-matrix, which significantly contributed to the reported enhancement in wear resistance. The development of harder-phases has included, α-Mg, Al12Mg17, SiO2, Si3N4, MgO, and CaO phases within the composite has been accountable for the enhancement of the tribomechanical, and wear-resistance characteristics of the AZ91D/WGP/Si3N4 composites. The Si3N4 has been discovered to have a substantial impact on enhancing mechanical performance and raising the resistance to wear.

Exploring the relationship between air quality index and lung cancer mortality in India: predictive modeling and impact assessment.

The Air Quality Index (AQI) in India is steadily deteriorating, leading to a rise in the mortality rate due to Lung Cancer. This decline in air quality can be attributed to various factors such as PM 2.5, PM 10, and Ozone (O3). To establish a relationship between AQI and Lung Cancer, several predictive models including Linear Regression, KNN, Decision Tree, ANN, Random Forest Regression, and XGBoost Regression were employed to estimate pollutant levels and Air Quality Index in India. The models relied on publicly available state-wise Air Pollution Dataset. Among all the models, the XGBoost Regression displayed the highest accuracy, with pollutant level estimations reaching an accuracy range of 81% to 98% during training and testing. The second-highest accuracy range was achieved by Random Forest. The paper also explores the impact of increasing pollution levels on the rising mortality rate among lung cancer patients in India.

Hybrid deep learning model based smart IOT based monitoring system for Covid-19.

Recently, COVID-19 becomes a hot topic and explicitly made people follow social distancing and quarantine practices all over the world. Meanwhile, it is arduous to visit medical professionals intermittently by the patients for fear of spreading the disease. This IoT-based healthcare monitoring system is utilized by many professionals, can be accessed remotely, and provides treatment accordingly. In context with this, we designed an IoT-based healthcare monitoring system that sophisticatedly measures and monitors the parameters of patients such as oxygen level, blood pressure, temperature, and heart rate. This system can be widely used in rural areas that are linked to the nearest city hospitals to monitor the patients. The collected data from the monitoring system are stored in the cloud-based data storage and for the classification our approach proposes an innovative Recurrent Convolutional Neural Network (RCNN) based Puzzle optimization algorithm (PO). Based on the outcome further treatments are made with the assistance of physicians. Experimental analyses are made and analyzed the performance with state-of-art works. The availability of more data storage capacity in the cloud can make physicians access the previous data effortlessly.

Empirical models for compressive and tensile strength of basalt fiber reinforced concrete.

When molten magma solidifies, basalt fiber (BF) is produced as a byproduct. Due to its remaining pollutants that could affect the environment, it is regarded as a waste product. To determine the compressive strength (CS) and tensile strength (TS) of basalt fiber reinforced concrete (BFRC), this study will develop empirical models using gene expression programming (GEP), Artificial Neural Network (ANN) and Extreme Gradient Boosting (XG Boost). A thorough search of the literature was done to compile a variety of information on the CS and TS of BFRC. 153 CS findings and 127 TS outcomes were included in the review. The water-to-cement, BF, fiber length (FL), and coarse aggregates ratios were the influential characteristics found. The outcomes showed that GEP can accurately forecast the CS and TS of BFRC as compared to ANN and XG Boost. Efficiency of GEP was validated by comparing Regression (R2) value of all three models. It was shown that the CS and TS of BFRC increased initially up to a certain limit and then started decreasing as the BF % and FL increased. The ideal BF content for industrial-scale BF reinforcement of concrete was investigated in this study which could be an economical solution for production of BFRC on industrial scale.

Fault tolerant trust based task scheduler using Harris Hawks optimization and deep reinforcement learning in multi cloud environment.

Cloud Computing model provides on demand delivery of seamless services to customers around the world yet single point of failures occurs in cloud model due to improper assignment of tasks to precise virtual machines which leads to increase in rate of failures which effects SLA based trust parameters (Availability, success rate, turnaround efficiency) upon which impacts trust on cloud provider. In this paper, we proposed a task scheduling algorithm which captures priorities of all tasks, virtual resources from task manager which comes onto cloud application console are fed to task scheduler which takes scheduling decisions based on hybridization of both Harris hawk optimization and ML based reinforcement algorithms to enhance the scheduling process. Task scheduling in this research performed in two phases i.e. Task selection and task mapping phases. In task selection phase, all incoming priorities of tasks, VMs are captured and generates schedules using Harris hawks optimization. In task mapping phase, generated schedules are optimized using a DQN model which is based on deep reinforcement learning. In this research, we used multi cloud environment to tackle availability of VMs if there is an increase in upcoming tasks dynamically and migrate tasks to one cloud to another to mitigate migration time. Extensive simulations are conducted in Cloudsim and workload generated by fabricated datasets and realtime synthetic workloads from NASA, HPC2N are used to check efficacy of our proposed scheduler (FTTHDRL). It compared against existing task schedulers i.e. MOABCQ, RATS-HM, AINN-BPSO approaches and our proposed FTTHDRL outperforms existing mechanisms by minimizing rate of failures, resource cost, improved SLA based trust parameters.

Rheological study of Hall current and slip boundary conditions on fluid-nanoparticle phases in a convergent channel.

Purpose: the purpose of this theoretical study was to analyze the heat transfer in the fluid-particle suspension model under the effects of a porous medium, magnetic field, Hall effects, and slip boundary conditions in a convergent channel with the addition of electrokinetic phenomena. The Darcy-Brinkman (non-Darcy porous medium) model was used to assess the effects of the porous medium. Methodology: the rheological equations of both models were transformed into a dimensionless form to obtain the exact solutions of the fluid and particle phase velocities, pressure gradient, volumetric flow rate, stream function, temperature distribution, and heat-transfer rate. To obtain an exact solution to the models, the physical aspects of the parameters are discussed, analyzed, and reported through graphs, contour plots, and in tabular form. Findings: mixing in hafnium particles in a viscous fluid provide 1.2% more cooling compared to with a regular fluid. A reduction of the streamlines was observed with the contribution of the slip condition. The utilization of the Darcy parameters upgraded both the fluid flow and temperature profiles, while the heat-transfer rate decreased by up to 3.3% and 1.7% with the addition of a magnetic field and porous medium, respectively. Originality: the current study is an original work of the authors and has not been submitted nor published elsewhere.

Deploying efficient net batch normalizations (BNs) for grading diabetic retinopathy severity levels from fundus images.

Diabetic retinopathy (DR) is one of the main causes of blindness in people around the world. Early diagnosis and treatment of DR can be accomplished by organizing large regular screening programs. Still, it is difficult to spot diabetic retinopathy timely because the situation might not indicate signs in the primary stages of the disease. Due to a drastic increase in diabetic patients, there is an urgent need for efficient diabetic retinopathy detecting systems. Auto-encoders, sparse coding, and limited Boltzmann machines were used as a few past deep learning (DL) techniques and features for the classification of DR. Convolutional Neural Networks (CNN) have been identified as a promising solution for detecting and classifying DR. We employ the deep learning capabilities of efficient net batch normalization (BNs) pre-trained models to automatically acquire discriminative features from fundus images. However, we successfully achieved F1 scores above 80% on all efficient net BNs in the EYE-PACS dataset (calculated F1 score for DeepDRiD another dataset) and the results are better than previous studies. In this paper, we improved the accuracy and F1 score of the efficient net BNs pre-trained models on the EYE-PACS dataset by applying a Gaussian Smooth filter and data augmentation transforms. Using our proposed technique, we have achieved F1 scores of 84% and 87% for EYE-PACS and DeepDRiD.

Entropy optimized flow of Sutterby nanomaterial subject to porous medium: Buongiorno nanofluid model.

Owing to enhanced thermal impact of nanomaterials, different applications are suggested in engineering and industrial systems like heat transfer devices, energy generation, extrusion processes, engine cooling, thermal systems, heat exchanger, chemical processes, manufacturing systems, hybrid-powered plants etc. The current communication concerns the optimized flow of Sutterby nanofluid due to stretched surface in view of different thermal sources. The investigation is supported with the applications of external heat source, magnetic force and radiative phenomenon. The irreversibility investigation is deliberated with implementation of thermodynamics second law. The thermophoresis and random movement characteristics are also studied. Additionally, first order binary reaction is also examined. The nonlinear system of the governing problem is obtained which are numerically computed by s method. The physical aspects of prominent flow parameters are attributed graphically. Further, the analysis for entropy generation and Bejan number is focused. It is observed that the velocity profile increases due to Reynolds number and Deborah number. Larger Schmidt number reduces the concentration distribution. Further, the entropy generation is improved against Reynolds number and Brinkman parameter.

Predicting compressive strength of eco-friendly plastic sand paver blocks using gene expression and artificial intelligence programming.

Plastic sand paver blocks provide a sustainable alternative by using plastic waste and reducing the need for cement. This innovative approach leads to a more sustainable construction sector by promoting environmental preservation. No model or Equation has been devised that can predict the compressive strength of these blocks. This study utilized gene expression programming (GEP) and multi-expression programming (MEP) to develop empirical models to forecast the compressive strength of plastic sand paver blocks (PSPB) comprised of plastic, sand, and fibre in an effort to advance the field. The database contains 135 results for compressive strength with seven input parameters. The R2 values of 0.87 for GEP and 0.91 for MEP for compressive strength reveal a relatively significant relationship between predicted and actual values. MEP outperformed GEP by displaying a higher R2 and lower values for statistical evaluations. In addition, a sensitivity analysis was conducted, which revealed that the sand grain size and percentage of fibres play an essential part in compressive strength. It was estimated that they contributed almost 50% of the total. The outcomes of this research have the potential to promote the reuse of PSPB in the building of green environments, hence boosting environmental protection and economic advantage.

Free convective oscillatory flow due to inclined perpendicular shield subject to the thermos-diffusion and suction effects.

An unsteady free convective flow of an electrically conducting viscous fluid due to accelerated inestimable inclined perpendicular shield has been presented in presence of heat and mass transfer phenomenon. The applications of thermos-diffusion and heat source are also incorporated. The chemical reaction consequences are considered in the concentration equation. The compelling meadow is considered to be homogeneous and practical perpendicular to the flow direction. Further, the oscillatory suction effects are also taken into observations for porous regime. The closed form expressions are resulted with implementation of perturbation approach. The non-dimensional expression for the proposed governing system is yield out with entertaining appropriate variables. The graphically influence of parameters is studied. Following to obtained observations, it is claimed that declining deviation in velocity is predicted with chemical reactive factor. Further, less thermal transport between container to fluid is noticed for radiative absorption parameter.

Simulation of Prandtl Nanofluid in the Mixed Convective Flow of Activation Energy with Gyrotactic Microorganisms: Numerical Outlook Features of Micro-Machines.

The physiological systems and biological applications that have arisen during the past 15 years depend heavily on the microscale and nanoscale fluxes. Microchannels have been utilized to develop new diagnostic assays, examine cell adhesion and molecular transport, and replicate the fluid flow microenvironment of the circulatory system. The various uses of MHD boundary flow in engineering and technology are extensive, ranging from MHD power generators and the polymer industry to MHD flow meters and pumps and the spinning of filaments. In this investigation, the (Magnetohydrodynamic) MHD flow of Prandtl nanofluid is investigated along with mixed convection, energy activation, microorganism, and chemical reaction. The flow model is considered through partial differential equations in dimensionless form which is then integrated numerically via considering the Bvp4c technique. The outcome is numerous emerging physical parameters over velocity profile, temperature, mass concentration, and microorganism with the separate pertinent quantities such as the Prandtl fluid parameter, elastic fluid parameter, magnetic field, mixed convection parameter, activation energy, chemical reaction, Brownian motion, thermophoretic force, Prandtl number, and Schmidt number. The friction factor, rate of heat transfer and Sherwood number, and density of microbes are revealed numerically and graphically. The outcomes indicate that the Prandtl fluid parameter and elastic fluid parameter tend to enhance the velocity profile. It is also noted that the Prandtl fluid parameter depreciates the thermal rate with the addition of the concentration profile while the opposite trend is recorded for activation energy. Obtained numerical outcomes are correspondingly compared with the current statistics in limiting cases and a close match is obtained.

Numerical analysis of magnetohydrodynamics Casson nanofluid flow with activation energy, Hall current and thermal radiation.

In this study we analyzed the flow, heat and mass transfer behavior of Casson nanofluid past an exponentially stretching surface under the impact of activation energy, Hall current, thermal radiation, heat source/sink, Brownian motion and thermophoresis. Transverse magnetic field with the assumption of small Reynolds number is implemented vertically. The governing partial nonlinear differential equations of the flow, heat and mass transfer are transformed into ordinary differential equations by using similarity transformation and solved numerically by using Matlab bvp4c package. The impact of each of the Hall current parameter, thermal radiation parameter, heat source/sink parameter, Brownian motion parameter, Prandtl number, thermophoresis parameter and magnetic parameter on velocity, concentration and temperature, is discussed through graphs. The skin friction coefficient along the x-and z-directions, the local Nusselt number and the Sherwood number are calculated numerically to look into the inside behavior of the emerging parameters. It is witnessed that the flow velocity is a diminishing function of the thermal radiation parameter and the behavior has observed in the case of Hall parameter. Moreover, mounting values of Brownian motion parameter reduce the nanoparticle concentration profile.

Impact of activation energy and variable properties on peristaltic flow through porous wall channel.

The current study discusses the peristaltic flow of Jeffrey fluid through a porous wall channel. Magnetohydrodynamic (MHD) effects are also considered while formulating the problem. Heat and mass transfers are discussed in the presence of activation energy and constant heat source/sink effects. A chemical reaction is also part of the analysis. The Lubrication approach is adopted for the simplification of resulting non-linear equations. MATHEMATICA command, NDSolve, is used to discuss the results graphically for various flow parameters like Hartman number [Formula: see text], porosity parameter [Formula: see text], slip parameters ([Formula: see text]), Schmidt [Formula: see text], Soret [Formula: see text] and Prandtl [Formula: see text] numbers, and many others. Parabolic behavior for velocity and sinusoidal nature for heat transfer and pressure gradient is noticed. Results indicate that the velocity is greatly affected by varying values of slip parameters (γ's) and Hartman number [Formula: see text]. Enhancing the viscoelastic nature of fluid causes an increase in velocity. Similar behavior is noticed for velocity and temperature profiles. The decreasing trend is shown by concentration when the value of the chemical reaction and temperature ratio parameters is enhanced. Thus, the study presented in the current analysis can be used to study many human physiological systems especially, the blood flow. Since Jeffrey's fluid exhibits the same characteristics as observed for blood.

Effects of activation energy and chemical reaction on unsteady MHD dissipative Darcy-Forchheimer squeezed flow of Casson fluid over horizontal channel.

The impact of chemical reaction and activation energy plays a vital role in the analysis of fluid dynamics and its thermal properties. The application of the flow of fluid is significantly considered in nuclear reactors, automobiles, manufacturing setups, electronic appliances etc. This study explores the impacts of activation energy and chemical reaction on the magnetohydrodynamic Darcy-Forchheimer squeezed Casson fluid flow through a porous material across the horizontal channel where the two parallel plates are assumed to be in motion. By using similarity variables, partial differential equations are converted to ordinary differential equations. Numerical method is applied using MATLAB to solve the problems and acquire the data for velocity field, thermal distribution, and concentration distribution. The graphs indicate that fluid velocity and temperature increases as the plates are brought closer. In addition, there was a correlation between a rise in the Hartmann number and a decrease in the fluid's velocity because of the existence of strong Lorentz forces. The temperature and the concentration of the liquid will increase due to the Brownian motion. When the Darcy-Forchheimer and activation energy parameters are both increased, the velocity and concentration decreases.

Analysis of the Thomson and Troian velocity slip for the flow of ternary nanofluid past a stretching sheet.

In this article, the flow of ternary nanofluid is analysed past a stretching sheet subjected to Thomson and Troian slip condition along with the temperature jump. The ternary nanofluid is formed by suspending three different types of nanoparticles namely [Formula: see text] and [Formula: see text] into water which acts as a base fluid and leads to the motion of nanoparticles. The high thermal conductivity and chemical stability of silver was the main cause for its suspension as the third nanoparticle into the hybrid nanofluid [Formula: see text]. Thus, forming the ternary nanofluid [Formula: see text]. The sheet is assumed to be vertically stretching where the gravitational force will have its impact in the form of free convection. Furthermore, the presence of radiation and heat source/sink is assumed so that the energy equation thus formed will be similar to most of the real life applications. The assumption mentioned here leads to the mathematical model framed using partial differential equations (PDE) which are further transformed to ordinary differential equations (ODE) using suitable similarity transformations. Thus, obtained system of equations is solved by incorporating the RKF-45 numerical technique. The results indicated that the increase in the suspension of silver nanoparticles enhanced the temperature and due to density, the velocity of the flow is reduced. The slip in the velocity decreased the flow speed while the temperature of the nanofluid was observed to be increasing.