Samarium ions (Sm +3), a rare-earth element, have a significant optical emission within the visible spectrum. PMMA samples, mixed with different ratios of SmCl3.6H2O, were prepared via the casting method. The composite was tested using UV-visible, photoluminescence and thermogravimetric analysis (TGA). The FTIR spectrometry of PMMA samples showed some changes, including variation in band intensity, location, and width. Mixed with samarium decreases the intensity of the CO and CH2 stretching bands and band position. A new band appeared corresponding to ionic bonds between samarium cations with negative branches in the polymer. These variations indicate complex links between the Sm +3 ion and oxygen in the ether group. The optical absorption

Information systems and data exchange between government institutions are growing rapidly around the world, and with it, the threats to information within government departments are growing. In recent years, research into the development and construction of secure information systems in government institutions seems to be very effective. Based on information system principles, this study proposes a model for providing and evaluating security for all of the departments of government institutions. The requirements of any information system begin with the organization's surroundings and objectives. Most prior techniques did not take into account the organizational component on which the information system runs, despite the relevance of

Astronomers have known since the invention of the telescope that atmospheric turbulence affects celestial images. So, in order to compensate for the atmospheric aberrations of the observed wavefront, an Adaptive Optics (AO) system has been introduced. The AO can be arranged into two systems: closedloop and open-loop systems. The aim of this paper is to model and compare the performance of both AO loop systems by using one of the most recent Adaptive Optics simulation tools, the Objected-Oriented Matlab Adaptive Optics (OOMAO). Then assess the performance of closed and open loop systems by their capabilities to compensate for wavefront aberrations and improve image quality, also their effect by the observed optical bands (near-infrared band

ملخص البحث: تناول البحث الحالي:(أنظمة التكوين الجمالي للكتابات والنصوص في النحت العربي المعاصر)، عددا من الأفكار الأساسية التي تمثل المادة النظرية لتفسير ظاهرة الكتابة في الفن بشكل عام وفي فن النحت بشكل خاص، ولكشف أنظمة التكوين الجمالي وأنواعها المتعددة من خلال فحص بعض النصوص البصرية للفنانين العرب المعاصرين، فقد جاء هذا البحث في أربعة فصول. خصص الفصل الأول: الإطار المنهجي للبحث لبيان مشكلة البحث، وأهم

In this article, a continuous terminal sliding mode control algorithm is proposed for servo motor systems. A novel full-order terminal sliding mode surface is proposed based on the bilimit homogeneous property, such that the sliding motion is finite-time stable independent of the system’s initial condition. A new continuous terminal sliding mode control algorithm is proposed to guarantee that the system states reach the sliding surface in finitetime. Not only the robustness is guaranteed by the proposed controller but also the continuity makes the control algorithm more suitable for the servo mechanical systems. Finally, a numerical example is presented to depict the advantages of the proposed control algorithm. An application in the rota

In recent years, the world witnessed a rapid growth in attacks on the internet which resulted in deficiencies in networks performances. The growth was in both quantity and versatility of the attacks. To cope with this, new detection techniques are required especially the ones that use Artificial Intelligence techniques such as machine learning based intrusion detection and prevention systems. Many machine learning models are used to deal with intrusion detection and each has its own pros and cons and this is where this paper falls in, performance analysis of different Machine Learning Models for Intrusion Detection Systems based on supervised machine learning algorithms. Using Python Scikit-Learn library KNN, Support Ve