Background Microorganisms and fungal growth especially Candida albicans, on soft denture lining material are the most common problem which can lead to chronic mucosal inflammation. The aim of this study was to evaluate the effect of zirconium nanoparticles into acrylic-based heat cured soft denture lining material against Candida albicans, and the amount of zirconium ion release of soft liner/ZrNPs composite. Furthermore, evaluate shear bond strength after ZrNPs addition to soft liner. Materials and methods: Zirconium nanoparticles were added into acrylic-based soft denture liner in various percentages (1%, and 1.5% by weight). Two hundred and fifty specimens were arranged and isolated into four groups as per the test to be done The antifu

Zinc oxide (ZnO) nanostructures were synthesized through the hydrothermal method at various conditions growth times (6,7 and 8 hrs.) and a growth temperature (70, 90, and 100 ºC). The prepared ZnO nanostructure samples were described using scanning electron microscopy (SEM) and X-ray diffractometer to distinguish their surface morphologies and crystal structures. The ZnO samples were confirmed to have the same crystal type, with different densities and dimensions (diameter and length). The obtained ZnO nanostructures were used to manufacture gas sensors for NO2 gas detection. Sensing characteristics for the fabricated sensor to NO2 gas were examined at different operating temperatures (180, 200, 220, and 240) ºC with a low gas concentrati

In this work, some of new 2-benzylidenehydrazinecarbothioamide derivatives have been prepared by condensation of thiosemicarbazide and different substituted aromatic benzaldehydes in presence of glacial acetic acid to give compounds (1-6), these compounds have characterized by its physical properties and spectroscopic methods. This work also included theoretical study to prove the ability of these compounds as corrosion inhibitors; The program package of Gaussian 09W with its graphical user interface GaussView 5.0 had used for this purpose; the methods of Density Functional Theory (DFT) with basis set of 6-311G (d,p) / hybrid function of B3LYP and semiempirical method of PM3 have been used, the study included theoretical simulation

 Abstract

Basra province is known for its logistic location for trading activity and oil industry. By geological point of view, Basra areas are believed to consist mainly of alternation of (clay, silty clay, clayey silt, silt and sand) type of soil. Any development of industry in this area should be affected by the occurrence of the clay soil. That is why the investigation to the soil is more than necessary. In this case, a vast testing program was carried out by the author to evaluate the various formations constituting the of some Basra soils. An attempt to characterize and discuss the nature, minerals, engineering behavior and field properties of soil samples extracted from more than one thousand and one

This paper focuses on developing a self-starting numerical approach that can be used for direct integration of higher-order initial value problems of Ordinary Differential Equations. The method is derived from power series approximation with the resulting equations discretized at the selected grid and off-grid points. The method is applied in a block-by-block approach as a numerical integrator of higher-order initial value problems. The basic properties of the block method are investigated to authenticate its performance and then implemented with some tested experiments to validate the accuracy and convergence of the method.

This study employs evolutionary optimization and Artificial Intelligence algorithms to determine an individual’s age using a single-faced image as the basis for the identification process. Additionally, we used the WIKI dataset, widely considered the most comprehensive collection of facial images to date, including descriptions of age and gender attributes. However, estimating age from facial images is a recent topic of study, even though much research has been undertaken on establishing chronological age from facial photographs. Retrained artificial neural networks are used for classification after applying reprocessing and optimization techniques to achieve this goal. It is possible that the difficulty of determining age could be reduce

The blade pitch angle (BPA) controller is key factor to improve the power generation of wind turbine (WT). Due to the aerodynamic structural behavior of the rotor blades, wind turbine system performance is influenced by pitch angle and environmental conditions such as wind speed, which fluctuate throughout the day. Therefore, to overcome the pitch angle control (PAC) problem, high wind speed conditions, and due to type-1 and type-2 fuzzy logic limitations for handling high levels of uncertainty, the newly proposed optimal hybrid type-3 fuzzy logic controller has been applied and compared since type-3 fuzzy controllers utilize three-dimensional membership functions, unlike type-2 and type-1 fuzzy logic controllers. In this paper six differen

A new and hybrid deep learning-based approach for diagnosing faults in electric vehicle (EV) drive motors is proposed in this article. This article presents a new and hybrid deep learning-based method of diagnosing faults in the drive motors of electric vehicles (EV). In contrast to standard CNNLSTM approaches that depend on SoftMax classification, the introduced framework combines a Random Forest (RF) classifier to enhance the generalization, interpretability, and robustness of fault prediction. Furthermore meant for use on edge computing equipment with IoT integration, the design allows for real-time monitoring in resource-limited settings. The introduced algorithm utilizes a Random Forest (RF) classifier for accurate fault classification

Background Due to the intermittent, nonlinear, and uncertain behavior of renewable energy sources (res) such as solar and wind, grid stability and reliability require very high forecasting and optimization skills as widely reported in the literature. Traditional optimization methods work very well in small or static systems but are suffer difficulty on large-scale, dynamic and stochastic renewable environment due to their NP-hard nature. Methods The framework introduces the concept of a Machine Learning-Assisted Hybrid Cuckoo Search (ML-HCS) that combines CS with a hybrid metaheuristic and integrates Long Short-Term Memory (LSTM) networks for forecasting based on both regression models of LSTMs and hybrid optimization algorithm