The objective of this study was to develop neural network algorithm, (Multilayer Perceptron), based correlations for the prediction overall volumetric mass-transfer coefficient (k_La), in slurry bubble column for gas-liquid-solid systems. The Multilayer Perceptron is a novel technique based on the feature generation approach using back propagation neural network. Measurements of overall volumetric mass transfer coefficient were made with the air - Water, air - Glycerin and air - Alcohol systems as the liquid phase in bubble column of 0.15 m diameter. For operation with gas velocity in the range 0-20 cm/sec, the overall volumetric mass transfer coefficient was found to decrease w

A Tonido cloud server provides a private cloud storage solution and synchronizes customers and employees with the required cloud services over the enterprise. Generally, access to any cloud services by users is via the Internet connection, which can face some problems, and then users may encounter in accessing these services due to a weak Internet connection or heavy load sometimes especially with live video streaming applications overcloud. In this work, flexible and inexpensive proposed accessing methods are submitted and implemented concerning real-time applications that enable users to access cloud services locally and regionally. Practically, to simulate our network connection, we proposed to use the Raspberry-pi3 m

Objective: To assess of Science Teachers' Awareness towards Communicable Diseases Control in Baghdad City
Primary Schools
Methodology: A descriptive study was conducted, included (100) primary school, (50) in Al-Rassafa sector, and
(50) in Al-Karkh sector, from March 5th 2012 to March 15th 2013, to assess of science teachers' awareness
towards communicable diseases control. A cluster sample of (100) Science teachers (males and females) were
selected, as one teacher from each school. A questionnaire format was used for data collection. The validity of
questionnaire was estimated through a penal of experts related to the field of study, and its reliability was
estimated through a pilot study conducted in (20) schools (

Hydrogels are hydrophilic biocompatible polymers that can be used as a drug delivery material in different medical branches, including vital pulp therapy. The aim of this study is to characterize the physical and biological properties of the newly developed formula as a candidate direct pulp-capping material. The hydrogel composite was prepared from natural and synthetic origins (polyvinyl alcohol (PVA), hyaluronic acid (HA), and sodium alginate (SA)) with the incorporation of bioactive Moringa. Different formulas of hydrogel containing different concentrations were evaluated for physicochemical (FTIR, XRD, SEM, degradation, and swelling), mechanical (viscosity, folding endurance, film thickness), and biological (antioxidant, antibacterial,

To promote sustainable steel-concrete composite structures, it is essential to develop special shear connectors that facilitate accelerated construction and deconstruction. A lockbolt demountable shear connector (LBDSC) was recently proposed. While the LBDSC has been evaluated using horizontal and vertical (standard) push-out tests, it is essential to further assess the disassembly mechanism and the positive flexural performance of prefabricated demountable composite beams (PDCBs) under both serviceability and ultimate limit states. Two full-scale test specimens of PDCBs with LBDSC were designed with partial shear connections and assessed using a three or four-point load beam setup under both cyclic and static monotonic loading conditions.

Metasurface polarizers are essential optical components in modern integrated optics and play a vital role in many optical applications including Quantum Key Distribution systems in quantum cryptography. However, inverse design of metasurface polarizers with high efficiency depends on the proper prediction of structural dimensions based on required optical response. Deep learning neural networks can efficiently help in the inverse design process, minimizing both time and simulation resources requirements, while better results can be achieved compared to traditional optimization methods. Hereby, utilizing the COMSOL Multiphysics Surrogate model and deep neural networks to design a metasurface grating structure with high extinction rat

Background: pregnancy as a systemic condition causes changes in the functioning of human body as a whole and specifically in the oral cavity and it also is considered as a stressful condition. These changes may favor the increase of oxidative stress. Aim: The aim of this study was to estimate the level of marker of oxidative stress (malondialdehyde) and antioxidant (uric acid) in saliva of pregnant compared to non-pregnant women and to assess the gingival health condition in both groups. Additionally, unstimulated salivary flow rate was determined in both groups. Subjects, materials and methods: The study group consisted of sixty pregnant women, they were divided into three equal groups according to trimester (20 pregnant women for each

Transmission lines are generally subjected to faults, so it is advantageous to determine these faults as quickly as possible. This study uses an Artificial Neural Network technique to locate a fault as soon as it happens on the Doukan-Erbil of 132kv double Transmission lines network. CYME 7.1-Programming/Simulink utilized simulation to model the suggested network. A multilayer perceptron feed-forward artificial neural network with a back propagation learning algorithm is used for the intelligence locator's training, testing, assessment, and validation. Voltages and currents were applied as inputs during the neural network's training. The pre-fault and post-fault values determined the scaled values. The neural network's p