In this work, magnesium aluminate spinel (MA) (MgO 28 wt%, Al2O3 72 wt%) stoichiometric compound , were synthesized via solid state reaction (SSR) Single firing stage, and the impact of sintering on the physical properties and thermal properties as well as the fine structure and morphology of the ceramic product were examined. The Spinel samples were pressed at of (14 MPa) and sintering soaking time (2h). The effect of adding oxide titania (TiO2) was studied. The obtained powders were calcined at a temperature range of 1200 and 1400 °C. The calcined samples spinel were characterized by XRD, it showed the presence of developed spinel phase end also showed that the best catalyst is titania. The SEM image showed the high sintering temperat

The disruptions in supply chains have put small‐ and medium‐sized enterprises (SMEs) in dire need of resilient supply chains through which they can improve their performance. Based on the resource dependence theory, this study proposes a mediation model to improve the environmental performance (EP) of SMEs. The purpose of this study is to investigate the effect of supply chain resilience (SCR) on EP mediated by ambidextrous green innovation (AMGI). We proved a structural equation model based on questionnaire data from 261 companies in Iraq to test our hypotheses. The results show that SCR has a positive effect on AMGI for proactive and exploitative green innovation dimensions and positive impact on SMEs’ EP. AMGI plays a media

Abstract

            Itraconazole is a triazole antifungal given orally for the treatment of oropharyngeal and vulvovaginal candidiasis, for systemic infections including aspergillosis, candidiasis,  and for the prophylaxis of fungal infections in immunocompromised patients.

           The study aimed to formulate a practical water-insoluble Itraconazole, with insufficient bioavailability as nanosuspension to increase aqueous solubility and improve its dissolution and oral bioavailability.

          Itraconazole nanosuspension was produced by a

The accumulation of toxic elements in vegetables and melons grown in agriculture, Brassica rapa - turnip, Solanum lycopersicum - tomato, Citrullus lanatus - watermelon, Capsicum annuum - bell pepper, Daucus carota - carrots, Cucurbita pepo - pumpkin, Cucumis melo - melon, and also Prunus armeniaca - apricot from fruit trees were analyzed. The excess of maximum allowable concentrations in agricultural crops of the element As by 1.65-1.75, Cd - 1.6-2.3, Cr -1.2-2.35, Cu -1.6-3.3, Ni - 1.16-3.53, Pb - 1.54-3.08, Al - 1.36-3.5, Sb - 2.0-33, Se - 1.1-3.3 times was established. The maximum allowable concentration of mercury in vegetables and melons was equal to 0.02 mg/kg,

Global concerns are rising due to complications associated with the use of chemical agents and antibiotic resistance. Consequently, research focus has shifted towards the quest for effective agents of biological origin. The aim of the present study was to assess the antioxidant and antimicrobial potentials of aqueous and organic extracts derived from various parts of Alcea kurdica. Different parts of A. kurdica were obtained and prepared into leaf, flower and root powders. The powders were extracted with aqueous and organic solvents. The antimicrobial activity of these extracts was assessed against bacterial pathogens using the agar well-diffusion assay. Additionally, the antioxidant effects of the extracts were evaluated using the

     This paper develops the work of Mary Florence et.al. on centralizer of semiprime semirings and presents reverse centralizer of semirings with several propositions and lemmas. Also introduces the notion of dependent element and free actions on semirings with some results of free action of centralizer and reverse centralizer on semiprime semirings and some another mappings.

     Deep learning (DL) plays a significant role in several tasks, especially classification and prediction. Classification tasks can be efficiently achieved via convolutional neural networks (CNN) with a huge dataset, while recurrent neural networks (RNN) can perform prediction tasks due to their ability to remember time series data. In this paper, three models have been proposed to certify the evaluation track for classification and prediction tasks associated with four datasets (two for each task). These models are CNN and RNN, which include two models (Long Short Term Memory (LSTM)) and GRU (Gated Recurrent Unit). Each model is employed to work consequently over the two mentioned tasks to draw a road map of deep learning mod

The current research aims to identify: 1. The level of mathematical construct among the Department of Mathematics students in the colleges of education and basic education. 2. The level of effective mathematical operations in both sides of the brain at the Department of Mathematics students in the colleges of education and basic education. 3. The strength and direction of the correlation between the mathematical construct and effective mathematical operations on both sides of the brain at the Department of Mathematics students in the colleges of Education and Basic Education. To investigate the research objectives, the researcher formulated zero-main hypothesis for each aim and from the same hypothesis, three sub-zero hypotheses are deri