Synthesis, characterization and microstructural evaluation of ZnO nanoparticles by William-Hall and size-strain plot methods

ABSTRACT. At various calcination temperatures 450, 550 and 650 °C, zinc oxide nanoparticles were produced. Calcinated ZnO has high surface area as the BET was 119.12 m2g–1 and the average particle radius was calculated to be 1.16 nm. The dimension of crystallites and straining in ZnO nanoparticles' diffraction peaks remained measured. The Williamson–Hall (W–H) technique besides the size–strain approach stayed used. For each of XRD reflection peaks, physical characteristics like strain and stress were computed. Towards regulate the magnitude of crystallites, the Williamson–Hall (W–H) approach besides the size–strain technique are used that is good agreement with the size that determine from SEM as it was 22.6, 26.6 and 32.6 nm for ZnO calcinated at 450, 550 and 650 oC, individually. Using the W–H plot to modify the subversion shape, assuming an unvarying distortion model (UDM), unvarying stress deformation model (USDM), unvarying deformation energy density model (UDEDM), and The size–strain plan (SSP) approach was used to determine this. The SEM and Scherrer methods match well with the crystal size of ZnO NPs determined using W–H plots and the SSP technique.     KEY WORDS: Zinc oxide nanosphere, Calcination, Physical characterization, W–H investigation, SSP technique Bull. Chem. Soc. Ethiop. 2022, 36(4), 815-829.                                                           DOI: https://dx.doi.org/10.4314/bcse.v36i4.8                                                       &nbsp

Variables Affecting the Mothers Access to Quality Care during Childbirth using the Neural Networks and Logistic Regression Models

Quality pregnancy and birth care is crucial in reducing maternal and child mortality in Egypt, requiring both supply and demand interventions. Using data from the Egypt Demographic Health Survey 2014, a neural networks and logistic regression models were built to determine demographic, social, and economic determinants affecting mothers access to care during childbirth. The study found that mothers working status had a significant impact on access to care, with an inverse relationship. Logistic regression outperformed neural networks in analyzing the relationship between explanatory variables and mothers access to care during childbirth

Hydrophobic Polymers Flooding

Crude oil and other petroleum products are crucial to the global economy today due to increasing energy demand approximately (~1.5%) per year and significant oil remaining after primary and secondary oil recovery (~45-55% of original oil in place, OOIP), which accelerates the development of enhanced oil recovery (EOR) technologies. Polymer flooding through hydrophobically associated polyacrylamides (HAPAM) is a widely implemented EOR-technique, so they attracted much attention on both academic and industrial scales. Hydrophobically associating polyacrylamide (HAPAM) prepared by free radical emulsion polymerization of acrylamide (AM) monomer, divinyl sulfone as hydrophobic crosslinked moiety and surfmers, to chemically anchor a surfmer and hydrophobic crosslinker moiety onto the back bone of acrylamide chain. After that, polymeric nanocomposite was prepared through copolymerization of prepared HAPAM with different molar ratios of silica nanoparticles through one shot synthesis. Rheological properties for the prepared composites were evaluated. Wettability evaluation carried through quantitative and qualitative techniques where the results indicate novel polymers ability to alter rock wettability from oil-wet to water- wet

PROTECTIVE EFFECTS OF ZINGIBER OFFICINALE AGAINST CARBON TETRACHLORIDE INDUCED LIVER FIBROSIS

Objective: Liver plays a pivotal role in regulating various physiological processes in the body such as metabolism, secretion, and storage. It has a great capacity to detoxify toxic substances and synthesize useful principles. The current study was designed to investigate the possible protective effects of Zingiber officinale (ginger) extract on liver fibrosis induced by carbon tetrachloride (CCl4) in rats.Methods: The animals were divided into four groups with eight rats in each. To induce liver fibrosis, Wistar albino rats received CCl4 (2 ml/kg diluted in corn oil) twice weekly for eight weeks. Rats were concurrently treated with Z. officinale extract at two different doses (300 and 600 mg/kg/day).Results: CCl4 ­­induced liver injury characterized by fibrotic changes, degenerated hepatocytes and focal accumulation of inflammatory cells. In addition, CCl4 administration produced a significant increase in serum aminotransferases, lipids, liver lipid peroxidation and nitric oxide. The hepatoprotective effects of Z. officinale extract were evidenced by the significant decrease in serum aminotransferases and liver lipid peroxidation. Further, concurrent treatment with either dose of Z. officinale enhanced liver glutathione and enzymatic antioxidant defenses.Conclusion: Z. officinale showed a marked hepatoprotective effect against CCl4–induced liver fibrosis and injury through the abolishment of oxidative stress and potentiation of the antioxidant defense system.Keywords: Antioxidant, Ginger, Fibrosis, Oxidative stres

Contribution to the understanding of the Ionian Basin sedimentary evolution along the eastern edge of Apulia during the Late Cretaceous in Albania

Integrated in the peri-Adriatic domain, the Ionian Basin extended along a NW-SE direction during the Late Cretaceous, limited on its sides by the Apulian and the Kruja platforms. The basinal/slope succession was studied in seven outcrops exposed in the Albanian fold-and-thrust belt. Sedimentological investigations, supported by bio- and chronostratigraphy were performed on calcareous Upper Cretaceous hemipelagites, gravity-flow deposits and slumps. The western part of the basin was studied, revealing a strong influence of the Apulian margin, alternatively shedding sediment basinward, by means of a tectonically controlled edge. The Late Albian to Cenomanian period is characterized by the settling of muddy debrites along the margin. A deep basinal environment characterizes this period which prolongs until the Santonian, with no significant influx of the platform basinward. This sedimentary setting abruptly changed at the end of the Santonian, with an important influx derived from both platforms. Coarsening and thickening upward sequences show a progressive increase in sediment shedding during the Campanian. The Late Campanian-Early Maastrichtian period points out a major change on the resedimentation processes with the settling of several slumped units reworking thick sediment packages. The latter can be traced along the Apulian margin, testifying of instabilities along the edge of Apulia

Notice of Retraction A New Profile Learning Model for Recommendation System based on Machine Learning Technique

Notice of Retraction-----------------------------------------------------------------------After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IAES's Publication Principles.We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.The presenting author of this paper has the option to appeal this decision by contacting ijeei.iaes@gmail.com.-----------------------------------------------------------------------Recommender systems (RSs) have been used to successfully address the information overload problem by providing personalized and targeted recommendations to the end users. RSs are software tools and techniques providing suggestions for items to be of use to a user, hence, they typically apply techniques and methodologies from Data Mining. The main contribution of this paper is to introduce a new user profile learning model to promote the recommendation accuracy of vertical recommendation systems. The proposed profile learning model employs the vertical classifier that has been used in multi classification module of the Intelligent Adaptive Vertical Recommendation (IAVR) system to discover the user’s area of interest, and then build the user’s profile accordingly. Experimental results have proven the effectiveness of the proposed profile learning model, which accordingly will promote the recommendation accuracy