Convolutional Neural Networks (CNN) have high performance in the fields of object recognition and classification. The strength of CNNs comes from the fact that they are able to extract information from raw-pixel content and learn features automatically. Feature extraction and classification algorithms can be either hand-crafted or Deep Learning (DL) based. DL detection approaches can be either two stages (region proposal approaches) detector or a single stage (non-region proposal approach) detector. Region proposal-based techniques include R-CNN, Fast RCNN, and Faster RCNN. Non-region proposal-based techniques include Single Shot Detector (SSD) and You Only Look Once (YOLO). We are going to compare the speed and accuracy of Faster RCNN,

Copper Telluride Thin films of thickness 700nm and 900nm, prepared thin films using thermal evaporation on cleaned Si substrates kept at 300K under the vacuum about (4x10-5 ) mbar. The XRD analysis and (AFM) measurements use to study structure properties. The sensitivity (S) of the fabricated sensors to NO2 and H2 was measured at room temperature. The experimental relationship between S and thickness of the sensitive film was investigated, and higher S values were recorded for thicker sensors. Results showed that the best sensitivity was attributed to the Cu2Te film of 900 nm thickness at the H2 gas.

Background: Polymethyl methacrylate (PMMA) is used in denture fabrication and considered as the most reliable material for the construction of removable prosthodontic appliances. The material is far from ideal in fulfilling the mechanical requirements and the effect of autoclave processing has not been fully determined. The purpose of this study was to evaluate the effect of addition of salinized (ZrO2) Nano fillers in percentages 3%, 5% and 7% by weight on some properties of heat cured acrylic processed the by autoclave and compare it with 0% (control) group . Materials and methods: The silanized(ZrO2) Nano-particles was added to PMMA powder by weight in three different percentages 3%, 5% and 7%, mixed by probe ultra-sonication machine.

Slag of aluminum is a residue which results during the melting process of primary and secondary aluminum production. Salt slag of aluminum is hazardous solid waste according to the European Catalogue for Hazardous Wastes. Hence, recovery of aluminum not only saves the environment, but also has advantages of financial and economic returns. In this research, aluminum was recovered and purified from the industrial wastes generated as waste from both of State Company for Electrical and Electronic Industries (Baghdad/AlWaziriya) and General Company for Mechanical Industries (Babylon/-Al-Escandria). It was found that these wastes contain tiny proportions of other elements such as iron, copper, nickel, titanium, lead, and potassium. Wastes were

Aluminum alloy 5052 had been anodized by sulfuric acid as an electrolyte under constant voltage and the anodic oxide film produce will be testing by potentiostatic anodic polarization .Two variables, which were considered as important variables, were studied. These variables are anodizing time 15,30 min. and sealing time 10,20 min., and the test by potentiostatic anodic polarization through electro chemical polarization measurements in solutions of 1N na2so4 ( PH= 1 ). The results are discussed in light of the rate of ionic current flow through the coating during anodic polarization measurements.

Herein, date palm (Phoenix dactylifera) bunch (DPB) waste was transformed into activated carbon (DPAC) adsorbent by using microwaveinduced ZnCl2 activation for 15 min at a power of 600 W. Several analytical methods were used to explain the physicochemical parameters of DPBAC including XRD, pHpzc, BET, SEM–EDX, and FTIR. Afterwards, the adsorptive performance of DPBAC was thoroughly investigated for the removal of two structurally different organic dyes namely methyl violet (MV) and fuchsin basic (FB). The key adsorption parameters, including the dose of DPBAC (A: 0.02–0.06 g), the solution pH (B: 4–10), and the contact time (C: 2–20 min) were statistically optimized using the Box-Behnken design with response surface methodology (RSM