The paper discusses the structural and optical properties of In2O3 and In2O3-SnO2 gas sensor thin films were deposited on glass and silicon substrates and grown by irradiation of assistant microwave on seeded layer nucleated using spin coating technique. The X-ray diffraction revealed a polycrystalline nature of the cubic structure. Atomic Force Microscopy (AFM) used for morphology analysis that shown the grain size of the prepared thin film is less than 100 nm, surface roughness and root mean square for In2O3 where increased after loading SnO2, this addition is a challenge in gas sensing application. Sensitivity of In2O3 thin film against NO2 toxic gas is 35% at 300oC. Sensing properties were improved after adding Tin Oxide (SnO2) to be mo

Liquid-liquid membrane extraction technique, pertraction, using three types of solvents (methyl isobutyl ketone, n-butyl acetate, and n-amyl acetate) was used for recovery of penicillin V from simulated fermentation broth under various operating conditions of pH value (4-6) for feed and (6-8) for receiver phase, time (0-40 min), and agitation speed (300-500 rpm) in a batch laboratory unit system. The optimum conditions for extraction were at pH of 4 for feed, and 8 for receiver phase, rotation speed of 500 rpm, time of 40 min, and solvent of MIBK as membrane, where more than 98% of penicillin was extracted. 

In this work, strains and dynamic crack growth were studied and analyzed in thin flat plate with a surface crack at the center, subjected to cycling low velocity impact loading for two types of aluminum plates (2024, 6061). Experimental and numerical methods were implemented to achieve this research. Numerical analysis using program (ANSYS11-APDL) based on finite element method used to analysis the strains with respect to time at crack tip and then find the velocity of the crack growth under cycling impact loading. In the experimental work, a rig was designed and manufactured to applying the cycling impact loading on the cracked specimens. The grid points was screened in front of the crack tip to measure the elastic-plas

The pure ZnS and ZnS-Gr nanocomposite have been prepared
successfully by a novel method using chemical co-precipitation. Also
conductive polymer PPy nanotubes and ZnS-PPy nanocomposite
have been synthesized successfully by chemical route. The effect of
graphene on the characterization of ZnS has been investigated. X-ray
diffraction (XRD) study confirmed the formation of cubic and
hexagonal structure of ZnS-Gr. Dc-conductivity proves that ZnS and
ZnS-Gr have semiconductor behavior. The SEM proved that
formation of PPy nanotubes and the Gr nanosheet. The sensing
properties of ZnS-PPy/ZnS-Gr for NO2 gas was investigated as a
function of operating temperature and time under optimal condition.
The sensitivity,

The catalytic activity of faujasite type NaY catalysts prepared from local clay (kaolin) with different Si/Al ratio was studied using cumene cracking as a model for catalytic cracking process in the temperature range of 450-525° C, weight hourly space velocity (WHSV) of 5-20 h1, particle size ≤75μm and atmospheric pressure. The catalytic activity was investigated using experimental laboratory plant scale of fluidized bed reactor.
It was found that the cumene conversion increases with increasing temperature and decreasing WHSV. At 525° C and WHSV 5 h-1, the conversion was 42.36 and 35.43 mol% for catalyst with 3.54 Si/Al ratio and Catalyst with 5.75 Si/Al ratio, respectively, while at 450° C and at the same WHSV, the conversion w

Developments are carried out to enhance the performance of vertical axis wind turbines (VAWT). This paper studies the performance of the ducted wind turbine with convergent duct (DAWT). Basically, the duct technique is utilized to provide the desired wind velocity facing the turbine. Methodology was developed to estimate the decisive performance parameter and to present the effect of the convergent duct with different inlet angles. The ducted wind turbine was analyzed and simulated using MATLAB software and numerically using ANSYS-Fluent 17.2. Result of both approaches were presented and showed good closeness for the two cases of covering angles 12  and 20 respectively. Results also showed that the convergent duct with an inlet angl

Understanding, promoting, and teaching media literacy is an important societal challenge. STEM educators are increasingly looking to incorporate 21st century skills such as media literacy into core subject education. In this paper we investigate how undergraduate Computer Science (CS) students can learn media literacy as a by-product of collaborative video tutorial production. The paper presents a study of 34 third-year CS undergraduates who, as part of their learning, were each asked to produce three video tutorials on Raspberry Pi programming, using a collaborative video production tool for mobile phones (Bootlegger). We provide results of both quantitative and qualitative analysis of the production process and resulting video tutorials,

In this work, an inventive photovoltaic evaporative cooling (PV/EC) hybrid system was constructed and experimentally investigated. The PV/EC hybrid system has the prosperous advantage of producing electrical energy and cooling the PV panel besides providing cooled-humid air. Two cooling techniques were utilized: backside evaporative cooling (case #1) and combined backside evaporative cooling with a front-side water spray technique (case #2). The water spraying on the front side of the PV panel is intermittent to minimize water and power consumption depending on the PV panel temperature. In addition, two pad thicknesses of 5 cm and 10 cm were investigated at three different water flow rates of 1, 2, and 3 lpm. In Case #1,

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class