Luminescent sensor membranes and sensor microplates are presented for continuous or high-throughput wide-range measurement of pH based on a europium probe.

The aim of this paper is to approximate multidimensional functions by using the type of Feedforward neural networks (FFNNs) which is called Greedy radial basis function neural networks (GRBFNNs). Also, we introduce a modification to the greedy algorithm which is used to train the greedy radial basis function neural networks. An error bound are introduced in Sobolev space. Finally, a comparison was made between the three algorithms (modified greedy algorithm, Backpropagation algorithm and the result is published in [16]).

This paper predicts the resilient modulus (Mr) for warm mix asphalt (WMA) mixtures prepared using aspha-min. Various predictor variables were analyzed, including asphalt cement types, asphalt contents, nominal maximum aggregate sizes (NMAS), filler content, test temperatures, and loading times. Univariate and multivariate analyses were conducted to examine the behavior of each predictor variable individually and collectively. Through univariate analysis, it was observed that Mr exhibited an inverse trend with asphalt cement grade, NMAS, test temperature, and load duration. Although Mr increased slightly with higher filler and asphalt content, the magnitude of this increase was minimal. Multivariate analysis revealed that the rate of change

Abstract

  Natural gas is characterized by features that made from it a fuel and a raw material for many industries. Deepening its position as a favorite fossil supplier between other types of fossil fuel is the efficiency, diversity of its uses, low costs and compatibility with the environment which leads to increasing of its uses then increased global demand. So, the natural gas must take its place as an important resource in Iraq and participate the oil in the economic development process of building and financing of the general budget.

  Iraq is planning to continue of increasing the export capacity of raw oil to meet ambitious production targets emanating from the mai

In high-dimensional semiparametric regression, balancing accuracy and interpretability often requires combining dimension reduction with variable selection. This study intro- duces two novel methods for dimension reduction in additive partial linear models: (i) minimum average variance estimation (MAVE) combined with the adaptive least abso- lute shrinkage and selection operator (MAVE-ALASSO) and (ii) MAVE with smoothly clipped absolute deviation (MAVE-SCAD). These methods leverage the flexibility of MAVE for sufficient dimension reduction while incorporating adaptive penalties to en- sure sparse and interpretable models. The performance of both methods is evaluated through simulations using the mean squared error and variable selection cri

The inhibitive action of Phenyl Thiourea (PTU) on the corrosion of mild steel in strong Hydrochloric acid, HCl, has been investigated by weight loss and potentiostatic polarization. The effect of PTU concentration, HCl concentration, and temperature on corrosion rate of mild steel were verified using 2 levels factorial design and surface response analysis through weight loss approach, while the electrochemical measurements were used to study the behavior of mild steel in 5-7N HCl at temperatures 30, 40 and 50 °C, in absence and presence of PTU. It was verified that all variables and their interaction were statistically significant. The adsorption of (PTU) is found to obey the Langmuir adsorption isotherm. The effect of temperature on th

The evolution in the field of Artificial Intelligent (AI) with its training algorithms make AI very important in different aspect of the life. The prediction problem of behavior of dynamical control system is one of the most important issue that the AI can be employed to solve it. In this paper, a Convolutional Multi-Spike Neural Network (CMSNN) is proposed as smart system to predict the response of nonlinear dynamical systems. The proposed structure mixed the advantages of Convolutional Neural Network (CNN) with Multi -Spike Neural Network (MSNN) to generate the smart structure. The CMSNN has the capability of training weights based on a proposed training algorithm. The simulation results demonstrated that the proposed

The present work concerns with simulating unsteady state equilibrium model for production of methyl oleate (biodiesel) from reaction of oleic acid with methanol using sulfuric acid as a catalyst in batch reactive distillation. MESHR equations of equilibrium model were solved using MATLAB (R2010a). The validity of simulation model was tested by comparing the simulation results with a data available in literature. UNIQUAC liquid phase activity coefficient model is the most appropriate model to describe the non-ideality of OLAC-MEOH-MEOL-H2O system. The chemical reactions rates results from EQ model indicating the rates are controlled by chemical kinetics. Several variables was studied such as molar ratio of methanol to oleic acid 4:1, 6:1