In this paper, an enhanced artificial potential field (EAPF) planner is introduced. This planner is proposed to rapidly find online solutions for the mobile robot path planning problems, when the underlying environment contains obstacles with unknown locations and sizes. The classical artificial potential field represents both the repulsive force due to the detected obstacle and the attractive force due to the target. These forces can be considered as the primary directional indicator for the mobile robot. However, the classical artificial potential field has many drawbacks. So, we suggest two secondary forces which are called the midpoint

Herein, an efficient inorganic/organic hybrid photocatalyst composed of zeolitic imidazolate framework (ZIF-67) decorated with Cd0.5Zn0.5S solid solution semiconductor was constructed. The properties of prepared ZIF- [email protected] nanocomposite and its components (ZIF-67 and Cd0.5Zn0.5S) were investigated using XRD, FESEM, EDX, TEM, DRS and BET methods. The photocatalytic activity of fabricated [email protected] nanocomposite were measured toward removal of methyl violet (MV) dye as a simulated organic contaminant. Under visible-light and specific conditions (photocatalyst dose 1 g/l, MV dye 10 mg/l, unmodified solution pH 6.7 and reaction time 60 min.), the acquired [email protected] photocatalyst showed advanced photocatalytic activity

This research involved synthesis of new β-Lactam derivative from Azo compound[4-amino-N-(pyrimidine-2-yl)-3-(pyrimidine-2-yldiazenyl) benzene sulfonamide] (S1) record previously by many steps. Starting conversion the free amino group in an azo comp. to chloro acetamide derivative(S2), then reacted it with urea to give the oxazole ring  derivative (S3) that which containing free amino group. The condensation reaction between the amino group and P-bromobenzaldehyde to produce Shiff base (B14). Finally staudinger's cyclo addition reaction go run between the Shiff base derivative (B14) and chloro acetyl chloride in the presence of tri ethyl amine (Et₃N) as Base catalyst and dioxane a

The main topic of this study is central around the independence of Jordanian central bank and the extent of the effectiveness at the bank in leading the monetary policy without interferences or pressures from side of the government.  the degree of independence of Jordanian central bank was  based on the following based hypothesis following ,there is relationship between the independence of the central bank and the legislative and economical indices. the most important recommendations are degree of independence of the Jordan central bank 43.5% is a good one, but it possible to reach a higher degree than this one by to making some modification on the Jordanian central bank law and by the central bank should be more rigid

A three-stage learning algorithm for deep multilayer perceptron (DMLP) with effective weight initialisation based on sparse auto-encoder is proposed in this paper, which aims to overcome difficulties in training deep neural networks with limited training data in high-dimensional feature space. At the first stage, unsupervised learning is adopted using sparse auto-encoder to obtain the initial weights of the feature extraction layers of the DMLP. At the second stage, error back-propagation is used to train the DMLP by fixing the weights obtained at the first stage for its feature extraction layers. At the third stage, all the weights of the DMLP obtained at the second stage are refined by error back-propagation. Network structures an

<span lang="EN-US">Diabetes is one of the deadliest diseases in the world that can lead to stroke, blindness, organ failure, and amputation of lower limbs. Researches state that diabetes can be controlled if it is detected at an early stage. Scientists are becoming more interested in classification algorithms in diagnosing diseases. In this study, we have analyzed the performance of five classification algorithms namely naïve Bayes, support vector machine, multi layer perceptron artificial neural network, decision tree, and random forest using diabetes dataset that contains the information of 2000 female patients. Various metrics were applied in evaluating the performance of the classifiers such as precision, area under the c

In recent years, Wireless Sensor Networks (WSNs) are attracting more attention in many fields as they are extensively used in a wide range of applications, such as environment monitoring, the Internet of Things, industrial operation control, electric distribution, and the oil industry. One of the major concerns in these networks is the limited energy sources. Clustering and routing algorithms represent one of the critical issues that directly contribute to power consumption in WSNs. Therefore, optimization techniques and routing protocols for such networks have to be studied and developed. This paper focuses on the most recent studies and algorithms that handle energy-efficiency clustering and routing in WSNs. In addition, the prime