Motivated by the vital role played by transition metal nitride (TMN) composites in various industrial applications, the current study reports electronic properties, thermodynamic stability phase diagram, and vacancy formation energies of the plausible surfaces of NiAs and WC-type structures of δ3-MoN and δ-WN hexagonal phases, respectively. Low miller indices of various surface terminations of δ3-MoN and δ-WN namely, (100), (110), (111), and (001) have been considered. Initial cleaving of δ3-MoN bulk unit cell offers separate Mo and N terminations signified as δ3-MoN (100): Mo, δ3-MoN(100):N, δ3-MoN(111):Mo, δ3-MoN(111):Mo, and δ3-MoN(001):Mo. However, the (110) plane reveals mix-truncated with both molybdenum and nitrogen atoms i

This search study the effect of particle size of graphite on the mechanical and thermal properties of epoxy composites, where graphite adopted with particle sizes (45,53,75) ?m, respectively, and the percentages by weight (0,1,3,5,7,9)% for each size of this three particle sizes.Mechanical properties represented by the bending (three-point bending) and through which the conclusion is bending stress and modulus of elasticity, thermal properties were either through thermal conductivity tests.The results showed that the ratio(1%) is the maximum value of bending stress at the three particle size and the (45 ?m) is the maximum.Thermal conductivity result show is the maximum value at ratio (1%) of particle size(53 ?m)

The adsorption behavior of Bismarck brown (BB) dye from aqueous solutions onto graphene oxide GO and graphene oxide-g-poly (n-butyl methacrylate-co-methacrylic acid) GO-g-pBCM as adsorbents was investigated. The prepared GO and GO-g-pBCM were characterized by Fourier transform infrared spectroscopy FTIR, which confirmed the compositions of the prepared adsorbents. Adsorption of BB dye onto GO and GO-g-pBCM was explored in a series of batch experiments under various conditions. The data were examined utilizing Langmuir and Freundlich isotherms. The Langmuir isotherm was seen as increasingly reasonable from the experimental information of dye on formulating adsorbents. Kinetic investigations showed that the experimental data were fitted ve

characteristic tissues and cells, exerting their pharmacological aspects and alleviating a lot of diseased processes. Accordingly, this research is about introducing some isatins to be nucleophilically attacked at C3 forming products of azomethine ylide functionality. These iminium compounds were made by allowing certain isatins to be reacted with the secondary amino acid, proline, at acetic acid and methanol medium and then collected after purification to be identified with total Leukocyte count (TLC) and melting point. The structural characterization was performed by fourier-transform infrared spectroscopy (FTIR), proton nuclear magnetic resonance (1H-NMR), and community health nursing (CHN) analysis. The microbiological evaluatio

Dyspepsia is a significant public health issue that affects the entire world population. In this work, we formulate and analyze a deterministic model for the population dynamics of Gut bacteria in the presence of antibiotics and Probiotic supplements. All the possible equilibria and their local stability are obtained. The global stability around the positive equilibrium point is established. Numerical simulations back up our analytical findings and show the temporal dynamics of gut microorganisms.

The research is concerned with studying the characteristics of Sustainable Architecture and Green Architecture, as a general research methodology related to the specific field of architecture, based on the differentiation between two generic concepts, Sustainability and Greening, to form the framework of the research specific methodology, where both concepts seem to be extremely overlapping for research centers, individuals, and relevant organizations. In this regard, the research tend towards searching their characteristics and to clearly differentiates between the two terms, particularly in architecture, where the research seeks understanding sustainable and green architectures, how they are so close or so far, and the

In this paper, we derive and prove the stability bounds of the momentum coefficient µ and the learning rate ? of the back propagation updating rule in Artificial Neural Networks .The theoretical upper bound of learning rate ? is derived and its practical approximation is obtained

The present study deals with the application of an a bundant low cost biosorbent sunflower shell for metal ions removal. Lead, Cadmium and Zinc were chosen as model sorbates. The influences of initial pH, sorbent dosage, contact time, temperature and initial metal ions concentration on the removal efficiency were examined. The single ion equilibrium sorption data were fitted to the non-competitive Langmuir and Freundlich isotherm models. The Freundlich model represents the equilibrium data better than the Langmuir model. In single, binary and ternary component systems,Pb⁺² ions was the most favorable component rather than Cd⁺² and Zn⁺² ions. The biosorption kinetics for the three metal ions followed the p

Copper (Cu) Zinc (Zn) and Magnesium (Mg) in serum, RBC, urine and dialyzate fluids were
studied in 39 patients, who have been undergoing chronic haemodialysis treatment. They were
divided in to polyuric , oliguric and anuric depending on their urinary output. Elevated serum and
RBC Mg was observed before dialysis, while decreased serum and RBC level was noticed except
serum Mg of polyuric patients. Before dialysis elevated serum and RBC Zn were observed. While
after dialysis these parameters were increased. Normal RBC Cu value before dialysis was observed.
While low serum Cu was noticed. After dialysis serum Cu showed raised value, while RBC level
decreased in oliguric and increased in polyuric patients. Zn / Cu rati

     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