Fractional Order Nonlinear Bone Remodeling Dynamics Using the Supervised Neural Network

This study aims to solve the nonlinear fractional-order mathematical model (FOMM) by using the normal and dysregulated bone remodeling of the myeloma bone disease (MBD). For the more precise performance of the model, fractional-order derivatives have been used to solve the disease model numerically. The FOMM is preliminarily designed to focus on the critical interactions between bone resorption or osteoclasts (OC) and bone formation or osteoblasts (OB). The connections of OC and OB are represented by a nonlinear differential system based on the cellular components, which depict stable fluctuation in the usual bone case and unstable fluctuation through the MBD. Untreated myeloma causes by increasing the OC and reducing the osteoblasts, resulting in net bone waste the tumor growth. The solutions of the FOMM will be provided by using the stochastic framework based on the Levenberg-Marquardt backpropagation (LVMBP) neural networks (NN), i.e., LVMBPNN. The mathematical performances of three variations of the fractional-order derivative based on the nonlinear disease model using the LVMPNN. The static structural performances are 82% for investigation and 9% for both learning and certification. The performances of the LVMBPNN are authenticated by using the results of the Adams-Bashforth-Moulton mechanism. To accomplish the capability, steadiness, accuracy, and ability of the LVMBPNN, the performances of the error histograms (EHs), mean square error (MSE), recurrence, and state transitions (STs) will be provided.     

Quantitative Evaluation of Cellular Uptake and Trafficking of Plain and Polyethylene Glycol‐Coated Gold Nanoparticles

This study addresses the cellular uptake and intracellular trafficking of 15‐nm gold nanoparticles (NPs), either plain (i.e., stabilized with citrate) or coated with polyethylene glycol (PEG), exposed to human alveolar epithelial cells (A549) at the air–liquid interface for 1, 4, and 24 h. Quantitative analysis by stereology on transmission electron microscopy images reveals a significant, nonrandom intracellular distribution for both NP types. No particles are observed in the nucleus, mitochondria, endoplasmic reticulum, or golgi. The cytosol is not a preferred cellular compartment for both NP types, although significantly more PEG‐coated than citrate‐stabilized NPs are present there. The preferred particle localizations are vesicles of different sizes (<150, 150–1000, >1000 nm). This is observed for both NP types and indicates a predominant uptake by endocytosis. Subsequent inhibition of caveolin‐ and clathrin‐mediated endocytosis by methyl‐β‐cyclodextrin (MβCD) results in a significant reduction of intracellular NPs. The inhibition, however, is more pronounced for PEG‐coated than citrate‐stabilized NPs. The latter are mostly found in larger vesicles; therefore, they are potentially taken up by macropinocytosis, which is not inhibited by MβCD. With prolonged exposure times, both NPs are preferentially localized in larger‐sized intracellular vesicles such as lysosomes, thus indicating intracellular particle trafficking. This quantitative evaluation reveals that NP surface coatings modulate endocytotic uptake pathways and cellular NP trafficking. Other nonendocytotic entry mechanisms are found to be involved as well, as indicated by localization of a minority of PEG‐coated NPs in the cytosol.     

Interpretation of infinite shear rate viscosity and a nonuniform heat sink/source on a 3D radiative cross nanofluid with buoyancy assisting/opposing flow

With respect to bionomical concerns and energy security, the performance of refrigeration systems should be enriched, which can be done by improving the characteristics of working liquids. Nanoliquids have attracted interest in the fields of engineering and industry due to their prominent thermophysical characteristics. Researchers have used nanoliquids as working liquids and noticed significant fluctuations in thermal execution. In this study,  our prime aim was to study the impact of thermal radiation and varying thermal conductivity on a cross-nanofuid with the addition of a nonuniform heat sink–source, chemical process, and activation energy (AE) together with effects of assisting and opposing buoyancy. Furthermore, the relationship of zero-mass flux together with the mechanism of thermophoresis and Brownian motion is considered. Traditionalistic transformations gave the ordinary differential equations (ODEs), which are further dealt with the approach of the Shooting Scheme to change the boundary value problem (BVP) into an initial value problem (IVP) and a numerical comparison is made with the Matlab solver package bvp4c. Bvp4c is based upon a collocation scheme, which yields numeric outcomes for nonlinear ODEs with IVP. Impacts of the involved parameters on mass transfer profile, heat, and momentum fields are shown through graphs. Mass transfer of the cross nanofluid increases with increasing values of AE parameter. Values of physical quantities like drag forces, rate of transport of heat and mass in the case of assisting/opposing flow are tabulated. The drag force magnitudes are greater for enhancing values of M, a, and n, while on the other hand, the opposing tendency is seen for We₁ and We₂. The magnitude of the rate of heat transport (Nusselt number) falls for greater values of m, σ, δ, and N_t, but in contrast, it accelerates for E, Pr, and n.     

Numerical solution of doubly singular nonlinear systems using neural networks-based integrated intelligent computing

Neural Computing and Applications - In this paper, a bio-inspired computational intelligence technique is presented for solving nonlinear doubly singular system using artificial neural networks...     

Improving sentiment analysis efficacy through feature synchronization

Multimedia Tools and Applications - Social media platforms are becoming a rich source of valuable information through sharing and publishing user generated reviews and comments. The identification...     

Drug Response Prediction of Liver Cancer Cell Line Using Deep Learning

Cancer is the second deadliest human disease worldwide with high mortality rate. Rehabilitation and treatment of this disease requires precise and automatic assessment of effective drug response and control system. Prediction of treated and untreated cancerous cell line is one of the most challenging problems for precise and targeted drug delivery and response. A novel approach is proposed for prediction of drug treated and untreated cancer cell line automatically by employing modified Deep neural networks. Human hepatocellular carcinoma (HepG2) cells are exposed to anticancer drug functionalized CFO@BTO nanoparticles developed by our lab. Prediction models are developed by modifying ResNet101 and exploiting the transfer learning concept. Last three layers of ResNet101 are re-trained for the identification of drug treated cancer cells. Transfer learning approach in an appropriate choice especially when there is limited amount of annotated data. The proposed technique is validated on acquired 203 fluorescent microscopy images of human HepG2 cells treated with drug functionalized cobalt ferrite@barium titanate (CFO@BTO) magnetoelectric nanoparticles in vitro. The developed approach achieved high prediction with accuracy of 97.5% and sensitivity of 100% and outperformed other approaches. The high performance reveals the effectiveness of the approach. It is scalable and fully automatic prediction approach which can be extended for other similar cell diseases such as lung, brain tumor and breast cancer.     

Inclined magnetized and energy transportation aspect of infinite shear rate viscosity model of Carreau nanofluid with multiple features over wedge geometry

Heat transference in fluid mechanism has a deep influence in real-life applications like hot-mix paving, recovery of energy, concrete heating, heat spacing, refineries, distillation, autoclaves, reactors, air conditioning, and so forth. In this attempt, findings related to energy exchange with features of infinite shear rate viscosity model of Carreau nanofluid by placing inclined magnetic dipole over the wedge are made. The main role in the transportation of heat is exercised by incorporating facts of r adiation, nonuniform heat sink source, Brownian motion, thermophoresis, and chemical reaction. The mathematical system of the infinite shear rate viscosity model of Carreau nanofluid gives a system of partial differential equations and furthermore, these are moved into ordinary differential equations. A numerical procedure is applied via shooting/bvp4c to obtain numerical results. Inclined magnetic dipole gives a lower velocity of Carreau nanofluid. Due to the relaxation time factor velocity of Carreau fluid gets down. A* causes to generate the heat internally, so due to this, temperature increases rapidly. The increasing rate of temperature is found very high for the growing Hartmann number. The rate of mass transport becomes low for gradual increment in the parameter of thermophoresis, wedge angle, and Prandtl. Inclined magnetic dipole gives a lower velocity of Carreau nanofluid. Due to the relaxation time factor, the velocity of the Carreau fluid goes down. The absence and presence of magnetic numbers have no influence on velocity, temperature, and concentration files for Le, R_d, θ_f, γ, We, β, Pr, Nb, Nt, A.     

On heated surface transport of heat bearing thermal radiation and MHD Cross flow with effects of nonuniform heat sink/source and buoyancy opposing/assisting flow

Heat transport subject to nonlinear thermal radiation has multiple applications in physics, industry, engineering field, and space technology, such as aerodynamic rockets, solar power technology, large open water reservoirs, and gas-cooled nuclear reactors. This effort studies the magnetohydrodynamic flow of cross fluid, which is a type of non-Newtonian, along a heated surface. Furthermore, the transportation of heat in the fluid is induced by  thermal radiation. Furthermore, the behavior of opposing/assisting flow and impact of nonuniform heat sink/source is scrutinized. The reserved suitable transformations are carried out to shift the ruling equations into nondimensional class. Through reserved transformations, two nonlinear partial differential equations are altered into corresponding nonlinear ordinary differential equations. Then a scheme of integration referred to as Runge–Kutta–Fehlberg is imposed to get a numerical solution of these. The impact of parameters are mentioned concisely on temperature and velocity profiles in the absence and presence of a magnetic parameter. It is proved that the presence of a magnetic field steps up the velocity and temperature as well.