TiO2 thin films have been deposited at different concentration of
CdO of (x= 0.0, 0.05, 0.1, 0.15 and 0.2) Wt. % onto glass substrates
by pulsed laser deposition technique (PLD) using Nd-YAG laser
with λ=1064nm, energy=800mJ and number of shots=500. The
thickness of the film was 200nm. The films were annealed to
different annealing (423 and 523) k. The effect of annealing
temperatures and concentration of CdO on the structural and
photoluminescence (PL) properties were investigated. X-ray
diffraction (XRD) results reveals that the deposited TiO2(1-x)CdOx
thin films were polycrystalline with tetragonal structure and many
peaks were appeared at (110), (101), (111) and (211) planes with
preferred orientation along 2Ɵ around 27.30. The results of
photoluminescence (PL) emission show that there are two peaks
positioned are around 320 nm and 400 nm for predominated peak
and 620 nm and 680 nm for the small peaks.
In this paper, a fixed point theorem of nonexpansive mapping is established to study the existence and sufficient conditions for the controllability of nonlinear fractional control systems in reflexive Banach spaces. The result so obtained have been modified and developed in arbitrary space having Opial’s condition by using fixed point theorem deals with nonexpansive mapping defined on a set has normal structure. An application is provided to show the effectiveness of the obtained result.
1,3,4-oxadizole and pyrazole derivatives are very important scaffolds for medicinal chemistry. A literature survey revealed that they possess a wide spectrum of biological activities including anti-inflammatory and antitumor effects.
To describe the synthesis and evaluation of two classes of new niflumic acid (NF) derivatives, the 1,3,4-oxadizole derivatives (compounds 3 and (4A-E) and pyrazole derivatives (compounds 5 and 6), as EGFR tyrosine kinase inhibitors in silico and in vitro.
The designed compounds were synthesized using convent
Data scarcity is a major challenge when training deep learning (DL) models. DL demands a large amount of data to achieve exceptional performance. Unfortunately, many applications have small or inadequate data to train DL frameworks. Usually, manual labeling is needed to provide labeled data, which typically involves human annotators with a vast background of knowledge. This annotation process is costly, time-consuming, and error-prone. Usually, every DL framework is fed by a significant amount of labeled data to automatically learn representations. Ultimately, a larger amount of data would generate a better DL model and its performance is also application dependent. This issue is the main barrier for
This work includes the synthesis of new ester compounds containing two 1,3,4-oxadiazole rings, 15a-c and 16a-c. This was done over seven steps, starting with p-acetamido-phenol 1 and 2-mercaptobenzoimidazole 2. The structure of the products was determined using FT-IR, 1H NMR, and mass spectroscopy. The evaluation of the antimicrobial activities of some prepared compounds was achieved against four types of bacteria (two types of gram-positive bacteria; Staphylococcus aureus and Bacillus subtilis, and two types of gram-negative bacteria, Pseudomonas aeruginosa and E. Coli), as well as against one types of fungus (C. albino). The results show moderate activit against the study bacteria, and the theoretical analysis of the toxi
... Show MoreAn improved Metal Solar Wall (MSW) with integrated thermal energy storage is presented in this research. The proposed MSW makes use of two, combined, enhanced heat transfer methods. One of the methods is characterized by filling the tested ducts with a commercially available copper Wired Inserts (WI), while the other one uses dimpled or sinusoidal shaped duct walls instead of plane walls. Ducts having square or semi-circular cross sectional areas are tested in this work.
A developed numerical model for simulating the transported thermal energy in MSW is solved by finite difference method. The model is described by system of three governing energy equations. An experimental test rig has been built and six new duct configurations have b
Feature selection (FS) constitutes a series of processes used to decide which relevant features/attributes to include and which irrelevant features to exclude for predictive modeling. It is a crucial task that aids machine learning classifiers in reducing error rates, computation time, overfitting, and improving classification accuracy. It has demonstrated its efficacy in myriads of domains, ranging from its use for text classification (TC), text mining, and image recognition. While there are many traditional FS methods, recent research efforts have been devoted to applying metaheuristic algorithms as FS techniques for the TC task. However, there are few literature reviews concerning TC. Therefore, a comprehensive overview was systematicall
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