Falkner–Skan Flow with Stream-Wise Pressure Gradient and Transfer of Mass over a Dynamic Wall

In this work, an important model in fluid dynamics is analyzed by a new hybrid neurocomputing algorithm. We have considered the Falkner–Skan (FS) with the stream-wise pressure gradient transfer of mass over a dynamic wall. To analyze the boundary flow of the FS model, we have utilized the global search characteristic of a recently developed heuristic, the Sine Cosine Algorithm (SCA), and the local search characteristic of Sequential Quadratic Programming (SQP). Artificial neural network (ANN) architecture is utilized to construct a series solution of the mathematical model. We have called our technique the ANN-SCA-SQP algorithm. The dynamic of the FS system is observed by varying stream-wise pressure gradient mass transfer and dynamic wall. To validate the effectiveness of ANN-SCA-SQP algorithm, our solutions are compared with state-of-the-art reference solutions. We have repeated a hundred experiments to establish the robustness of our approach. Our experimental outcome validates the superiority of the ANN-SCA-SQP algorithm.     

A Hybrid Metaheuristic Based on Neurocomputing for Analysis of Unipolar Electrohydrodynamic Pump Flow

A unipolar electrohydrodynamic (UP-EHD) pump flow is studied with known electric potential at the emitter and zero electric potential at the collector. The model is designed for electric potential, charge density, and electric field. The dimensionless parameters, namely the electrical source number (Es), the electrical Reynolds number (ReE), and electrical slip number (Esl), are considered with wide ranges of variation to analyze the UP-EHD pump flow. To interpret the pump flow of the UP-EHD model, a hybrid metaheuristic solver is designed, consisting of the recently developed technique sine–cosine algorithm (SCA) and sequential quadratic programming (SQP) under the influence of an artificial neural network. The method is abbreviated as ANN-SCA-SQP. The superiority of the technique is shown by comparing the solution with reference solutions. For a large data set, the technique is executed for one hundred independent experiments. The performance is evaluated through performance operators and convergence plots.     

MHD Stagnation Point on Nanofluid Flow and Heat Transfer of Carbon Nanotube over a Shrinking Surface with Heat Sink Effect

This study is to investigate the magnetohydrodynamic (MHD) stagnation point flow and heat transfer characteristic nanofluid of carbon nanotube (CNTs) over the shrinking surface with heat sink effects. Similarity equations deduced from momentum and energy equation of partial differential equations are solved numerically. This study looks at the different parameters of the flow and heat transfer using first phase model which is Tiwari-Das. The parameter discussed were volume fraction nanoparticle, magnetic parameter, heat sink/source parameters, and a different type of nanofluid and based fluids. Present results revealed that the rate of nanofluid (SWCNT/kerosene) in terms of flow and heat transfer is better than (MWCNT/kerosene) and (CNT/water) and regular fluid (water). Graphically, the variation results of dual solution exist for shrinking parameter in range λc<λ≤−1 for different values of volume fraction nanoparticle, magnetic, heat sink parameters, and a different type of nanofluid. However, a unique solution exists at −1<λ<1, and no solutions exist at λ<λc which is a critical value. In addition, the local Nusselt number decreases with increasing volume fraction nanoparticle when there exists a heat sink effect. The values of the skin friction coefficient and local Nusselt number increase for both solutions with the increase in magnetic parameter. In this study, the investigation on the flow and heat transfer of MHD stagnation point nanofluid through a shrinking surface with heat sink effect shows how important the application to industrial applications.     

AaTs-1: A Tetrapeptide from Androctonus australis Scorpion Venom,Inhibiting U87 Glioblastoma Cells Proliferation by p53 and FPRL-1 Up-Regulations

Glioblastoma is an aggressive cancer, against which medical professionals are still quite helpless, due to its resistance to current treatments. Scorpion toxins have been proposed as a promising alternative for the development of effective targeted glioblastoma therapy and diagnostic. However, the exploitation of the long peptides could present disadvantages. In this work, we identified and synthetized AaTs-1, the first tetrapeptide from Androctonus australis scorpion venom (Aa), which exhibited an antiproliferative effect specifically against human glioblastoma cells. Both the native and synthetic AaTs-1 were endowed with the same inhibiting effect on the proliferation of U87 cells with an IC₅₀ of 0.56 mM. Interestingly, AaTs-1 was about two times more active than the anti-glioblastoma conventional chemotherapeutic drug, temozolomide (TMZ), and enhanced its efficacy on U87 cells. AaTs-1 showed a significant similarity with the synthetic peptide WKYMVm, an agonist of a G-coupled formyl-peptide receptor, FPRL-1, known to be involved in the proliferation of glioma cells. Interestingly, the tetrapeptide triggered the dephosphorylation of ERK, p38, and JNK kinases. It also enhanced the expression of p53 and FPRL-1, likely leading to the inhibition of the store operated calcium entry. Overall, our work uncovered AaTs-1 as a first natural potential FPRL-1 antagonist, which could be proposed as a promising target to develop new generation of innovative molecules used alone or in combination with TMZ to improve glioblastoma treatment response. Its chemical synthesis in non-limiting quantity represents a valuable advantage to design and develop low-cost active analogues to treat glioblastoma cancer.     

Study of Nonlinear Models of Oscillatory Systems by Applying an Intelligent Computational Technique

In this paper, we have analyzed the mathematical model of various nonlinear oscillators arising in different fields of engineering. Further, approximate solutions for different variations in oscillators are studied by using feedforward neural networks (NNs) based on the backpropagated Levenberg–Marquardt algorithm (BLMA). A data set for different problem scenarios for the supervised learning of BLMA has been generated by the Runge–Kutta method of order 4 (RK-4) with the “NDSolve” package in Mathematica. The worth of the approximate solution by NN-BLMA is attained by employing the processing of testing, training, and validation of the reference data set. For each model, convergence analysis, error histograms, regression analysis, and curve fitting are considered to study the robustness and accuracy of the design scheme.     

Characterization of tri-o-methylcellulose by one- and two-dimensional NMR methods

Tri-O-methylcellulose was prepared from partially O-methylated cellulose and its chemical shifts (¹H and ¹³C), and proton coupling constants were assigned using the following NMR methods: (1) One-dimensional ¹H and ¹³C spectra of the title compound were used to assign functional groups and to compare with literature data; (2) double quantum filtered proton–proton correlation spectroscopy (¹H, ¹H DQF-COSY) was used to assign the chemical shifts of the network of 7 protons in the anhydroglucose portion of the repeat unit; (3) the heteronuclear single-quantum coherence (HSQC) spectrum was used to establish connectivities between the bonded protons and carbons; (4) the heteronuclear multiple-bond correlation (HMBC) spectrum was used to connect the hydrogens of the methyl ethers to their respective sugar carbons; (5) the combination of HSQC and HMBC spectra was used to assign the ¹³C shifts of the methyl ethers; (6) all spectra were used in combination to verify the assigned chemical shifts; (7) first-order proton coupling constants data (J_H,H in Hz) were obtained from the resolution-enhanced proton spectra. The NMR spectra of tri-O-methylcellulose and other cellulose ethers do not resemble the spectra of similarly substituted cellobioses. Although the ¹H and ¹³C shifts and coupling constants of 2,3,6-tri-O-methylcellulose closely resemble those of methyl tetra-O-methyl-β-D -glucoside, there are differences with regard to the chemical shifts and the order of appearances of the resonating nuclei of the methyl ether appendages and the proton at position 4 in the pyranose ring. H4 in tri-O-methylcellulose is deshielded by the acetal system comprising the β-1→4 linkage, and it resonates downfield. H4 in the permethylated glucoside is not as deshielded by the equitorial O-methyl group at C4, and it resonates upfield. The order of appearance of the ¹H and ¹³C resonances in the spectra of the tri-O-methylcellulose repeat unit (from upfield to downfield) are H2 < H3 < H5 < H6a < H3a < H2a < pro R H6B < H4 < pro S H6A ≪ H1 and C6a < C3a < C2a < C6 < C5 < C4 < C2 < C3 ≪ C1, respectively. Close examination of the pyranose ring coupling constants of the repeat unit in tri-O-methylcellulose supports the ⁴C₁ arrangement of the glucopyranose ring. Examination of the proton coupling constants about the C5-C6 bond (J_5,6A and J_5,6B) in the nuclear Overhauser effect difference spectra revealed that the C6 O-methyl group is predominantly in the gauche gauche conformation about the C5-C6 bond for the polymer in solution. © 1999 John Wiley & Sons, Inc.* J Polym Sci A: Polym Chem 37: 4019–4032, 1999     

Mathematical Analysis of Reaction–Diffusion Equations Modeling the Michaelis–Menten Kinetics in a Micro-Disk Biosensor

In this study, we have investigated the mathematical model of an immobilized enzyme system that follows the Michaelis–Menten (MM) kinetics for a micro-disk biosensor. The film reaction model under steady state conditions is transformed into a couple differential equations which are based on dimensionless concentration of hydrogen peroxide with enzyme reaction (H) and substrate (S) within the biosensor. The model is based on a reaction–diffusion equation which contains highly non-linear terms related to MM kinetics of the enzymatic reaction. Further, to calculate the effect of variations in parameters on the dimensionless concentration of substrate and hydrogen peroxide, we have strengthened the computational ability of neural network (NN) architecture by using a backpropagated Levenberg–Marquardt training (LMT) algorithm. NNs–LMT algorithm is a supervised machine learning for which the initial data set is generated by using MATLAB built in function known as “pdex4”. Furthermore, the data set is validated by the processing of the NNs–LMT algorithm to find the approximate solutions for different scenarios and cases of mathematical model of micro-disk biosensors. Absolute errors, curve fitting, error histograms, regression and complexity analysis further validate the accuracy and robustness of the technique.     

1,3,4-Oxadiazole, 1,3,4-thiadiazole and 1,2,4-triazole derivatives as potential antibacterial agents

Since the introduction of the first antibiotic (penicillin, 1942) into medical practice, to date, there has been an ongoing “race” between scientists creating new drugs and pathogenic bacteria. Antibiotic-bacteria are becoming progressively common, and to make matters worse, more and more bacteria are becoming resistant to all known antibiotics. The traditional method for this problem is to introduce new antibiotics that kill the resistant mutants. This specific “arms race” resulted into thousands of potentially active chemicals are synthesized in laboratories around the world every day.1,3,4-Oxadiazole; 1,3,4-thiadiazole; 1,2,4-triazole and some of their derivatives are involved in modifications at the following axes: First, attaching a thio-group into heterocyclic rings. Second, introducing different substitutions at position 5 which often are the residuals of the synthetic starting materials such as simple aliphatic, substituted aliphatic chains, aromatic carbocyclic and heterocyclic residues.     

Application of Euler Neural Networks with Soft Computing Paradigm to Solve Nonlinear Problems Arising in Heat Transfer

In this study, a novel application of neurocomputing technique is presented for solving nonlinear heat transfer and natural convection porous fin problems arising in almost all areas of engineering and technology, especially in mechanical engineering. The mathematical models of the problems are exploited by the intelligent strength of Euler polynomials based Euler neural networks (ENN’s), optimized with a generalized normal distribution optimization (GNDO) algorithm and Interior point algorithm (IPA). In this scheme, ENN’s based differential equation models are constructed in an unsupervised manner, in which the neurons are trained by GNDO as an effective global search technique and IPA, which enhances the local search convergence. Moreover, a temperature distribution of heat transfer and natural convection porous fin are investigated by using an ENN-GNDO-IPA algorithm under the influence of variations in specific heat, thermal conductivity, internal heat generation, and heat transfer rate, respectively. A large number of executions are performed on the proposed technique for different cases to determine the reliability and effectiveness through various performance indicators including Nash–Sutcliffe efficiency (NSE), error in Nash–Sutcliffe efficiency (ENSE), mean absolute error (MAE), and Thiel’s inequality coefficient (TIC). Extensive graphical and statistical analysis shows the dominance of the proposed algorithm with state-of-the-art algorithms and numerical solver RK-4.