This study discussed the effects of doping with silver (Ag) on the optical and structural properties of
CdO nanoparticles at different concentrations 0, 1, 2, 3, 4, 5 wt% prepared by the precipitation method. The
materials were annealed at 550˚C for 1 h. The structural, topographical, and optical properties were
diagnosed by X-ray diffraction analysis, atomic force instrument, and visible and ultraviolet spectrometers.
The results show that the average diameter of the grains depends on the percentage of added silver to the
material, as the diameter decreased from 88.8 to 59.7 nm, and it was found that the roughness increased from
5.56 to 26.5. When studying the optical properties, it was noted that th

In this paper, two types of iron oxide nanomaterial (Fe3O4) and nanocomposite (T-Fe3O4) were created from the bio-waste mass of tangerine peel. These two materials were utilized for adsorption tests to remove cefixime (CFX) from an aqueous solution. Before the adsorption application, both adsorbents have been characterized by various characterizations such as XRD, FTIR, VSM, TEM, and FESEM. The mesoporous nano-crystalline structure of Fe3O4 and T-Fe3O4 nanocomposite with less than 100-nm diameter is confirmed. The adsorption of the obtained adsorbents was evaluated for CFX removal by adjusting several operation parameters to optimize the removal. The optimal conditions for CFX removal were found to be an initial concentration of 40 and 50 m

The convergence speed is the most important feature of Back-Propagation (BP) algorithm. A lot of improvements were proposed to this algorithm since its presentation, in order to speed up the convergence phase. In this paper, a new modified BP algorithm called Speeding up Back-Propagation Learning (SUBPL) algorithm is proposed and compared to the standard BP. Different data sets were implemented and experimented to verify the improvement in SUBPL.

THE ACTUAL SOCIETY MODEL

Our society model has been showing signs of exhaustion since the end of the XX century. The recent appearance of the Covid-19 virus is another consequence of humanity's distance from Nature and the environment it inhabits. Also, climate change, environmental deterioration, loss of biodiversity, overexploitation of natural resources, and social inequality are some of the consequences derived from this separation between society and Nature. We must change our operating habits. Inevitably from a society based on the market, hyper consumption, fossil energy and individual enrichment at all costs, we will have to change to another

The birth of Multiple Intelligence (MI) theory has revolutionized traditional education since the twentieth century. Howard Gardner, the pioneer of this theory, believed that each learner has a unique way of acquiring information. This paper investigates how the New Headway Plus for Upper-Intermediate-Third Edition, a standard EFL textbook in Iraqi universities, can cater to the diverse MI profiles of Iraqi EFL learners. It evaluates the success or failure of senior undergraduates in actively engaging with different learning styles and activities in the classroom. The study maps the distribution of nine distinct modes of MI theory across all 12 Units_ Logic/Mathematical, Verbal/Linguistic, Musical, Bodily/Kinesthetic, Visual/Spatial, Natura

We propose a new method for detecting the abnormality in cerebral tissues present within Magnetic Resonance Images (MRI). Present classifier is comprised of cerebral tissue extraction, image division into angular and distance span vectors, acquirement of four features for each portion and classification to ascertain the abnormality location. The threshold value and region of interest are discerned using operator input and Otsu algorithm. Novel brain slices image division is introduced via angular and distance span vectors of sizes 24˚ with 15 pixels. Rotation invariance of the angular span vector is determined. An automatic image categorization into normal and abnormal brain tissues is performed using Support Vector Machine (SVM). St

     Many authors investigated the problem of the early visibility of the new crescent moon after the conjunction and proposed many criteria addressing this issue in the literature. This article presented a proposed criterion for early crescent moon sighting based on a deep-learned pattern recognizer artificial neural network (ANN) performance. Moon sight datasets were collected from various sources and used to learn the ANN. The new criterion relied on the crescent width and the arc of vision from the edge of the crescent bright limb. The result of that criterion was a control value indicating the moon's visibility condition, which separated the datasets into four regions: invisible, telescope only, probably visible, and certai