Microdetermination of spermidine, 1,3-diaminopropane,and p-phenylenediamine with chloranil

Spermidine, 1,3-diaminopropane, and p-phenylenediamine form molecular complexes with chloranil at pH 9. The composition and stability of these complexes have been studied spectrophotometrically. The results indicate that complexes were of 1:1 type and their stability increases with the basicities. The method of complexation was used to determine spectrophotometrically a ppm amount of SD, DAP, and PhDA in solution. The accuracy and precision of the method were found to be very high.     

Szekeres's space-times have no Killing vectors

Szekeres obtained some exact solutions of Einstein's equations for dust which have applications in cosmology, in the theory of collapse, and in the study of gravitational radiation. It is shown that in their most general form these space-times have no Killing vectors.Dedicated to Achille Papapetrou on the occasion of his retirement.On leave from the Department of Mathematics, University of Malaya, Kuala Lumpur, Malaysia.Supported by the Public Services Department, Government of Malaysia.On leave from Centro Brasileiro de Pesquisas Fisicas.     

Conductivity studies of plasticized anhydrous PEO-KOH alkaline solid polymer electrolyte

Polyethylene oxide (PEO)–potassium hydroxide (KOH)-based alkaline solid polymer electrolyte films have been prepared by using methanol as solvent. The highest room temperature ionic conductivity of (2.1 ± 0.5) × 10⁻⁸ S cm⁻¹ was achieved for the composition of 70 wt% PEO:30 wt% KOH. The addition of plasticizer, ethylene carbonate, propylene carbonate, or polyethylene glycol to the highest conductivity of PEO–KOH system helps to increase the ambient ionic conductivity to the order of 10⁻⁶–10⁻⁴ S cm⁻¹. The log σ vs 1/T plot of PEO–KOH showed a small conductivity decrease at 50–60 °C range. The small decrease and the hysteresis that occur during the heating–cooling cycle was overcome by the presence of the plasticizer. X-ray diffraction observation supports the conductivity results.     

Correlation of Moments and Critical Phenomenon in Disassembly of Hot Nuclei

We used Monte Carlo method to generate the configurations of disassembly of hot nucleus Au* based on the Atomic Mass Table and the conservation of mass and charge number.The resulted charge distribution of fragments was then used to calculate the conditional moments.The logarithm correlation between second and third moments of experimental charge distribution is very well reproduced by theory.It seems that no connection to the critical phenomenon of concerned system exists.     

THE EFFECT OF THE FREEZE-OUT VOLUME IN THE MULTIFRAGMENTATION OF VERY HOT NUCLEAR REMNANT

In this paper a simplified method for the calculation of average Coulomb energy in statistical model of the disassembly processes of hot nuclei is proposed.The effect of freeze-out volume is studied based on the assumption of Gaussion-like distribution.It is indecated that the centre and dispersion of Gaussion-like distribution somewhat reflect the location and the renge of liquid-gas coexistence phase.     

200A GeV S+Au反应中光子产生的低横动量增强

用建立在LUND弦模型特别是PYTHIA事件产生器基础上的描写极端相对论性核-核碰撞的强子和弦级联模型--JPCIAE研究了入射能量为200A GeV的S+Au中心碰撞中光子产生.模型同时考虑了部分子QCD散射过程、强子末态相互作用以及强子衰变等光子产生反应道并作了协调处理.JPCIAE模型计算结果很好再现了WA93实验数据所呈现的低横动量增强效应.     

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.     

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.     

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.