In the present study, the effect of Zinc nanoparticles on levels of (T3 , T4 and TSH) hormones was investigated. Zinc nanoparticles were synthesized by Laser induced plasma.The Nd: YAG Nd: YAG laser with a wavelength of 1064 nm was used to generate nanomaterials of the elements (zinc) upon collision with target atoms. Plasma generated by different laser intensity is generated. After confirming the preparation of zinc nanoparticles, XRD, AFM was examined, and the effect of these substances on the thyroid gland (T3, T4, TSH) was observed for two doses of each component (1 ml / kg, 4 ml / kg) after conducting a cytotoxicity examination of the lymphocytes of the rats extracted from Rat spleen was 1.8% less toxic to zinc, and as noted The

The nanocompsite of alumina (Al₂O₃) produced a number of beneficial effects in alloys. There is increasing in resistance of materials to surface related failures , such as the mechanical properties , fatigue and stress corrosion cracking .The experimental results observed that the adding of reinforced  nanomaterials  type Al₂O₃  enhanced the   HB hardness, UTS, 0.2 YS and ductility of 2014 Al/Al₂O₃ nano composites . the analysis of experiments, indicated that The maximum enhancement was observed at 0.4 wt.% Al₂O₃. The ultimate improvement percentage were 15.78% HB hardness, 18.1% (UTS), 12.86% (

Exploding wire Technique is a way for production metal and its compound nanoparticle that is capable of production of bulk amount at low cost semiconductor. In this work a copper iodine nanoparticles were fabricate by exploding copper wires with different currents in iodine solution. The produced samples were examined by XRD, FTIR, SEM and TEM to characterize their properties. The XRD proved the Nano-size for producer. The crystalline size increases with increasing current. FTIR measurements show a peaks located at 638.92 for Cu-I stretch bond indicate on formation of copper iodide compound and the peaks intensities increase with increasing current. The SEM and TEM measurements show that the thin films have nanostructures.

CdO:NiO/Si solar cell film was fabricated via deposition of CdO:NiO in different concentrations 1%, 3%, and 5% for NiO thin films in R.T and 723K, on n-type silicon substrate with approximately 200 nm thickness using pulse laser deposition. CdO:NiO/n-Si solar cell photovoltaic properties were examined under 60 mW/cm2 intensity illumination. The highest efficiency of the solar cell is 2.4% when the NiO concentration is 0.05 at 723K.

Kamil, S.H. 2024. Effects of Aqueous and Alcoholic Extracts of Lemongrass, Cymbopogon citratus on Some Biological Aspects of the Fig Moth, Ephestia cautella. Arab Journal of Plant Protection, 42(3): 377-381. https://doi.org/10.22268/AJPP-001245 This study evaluated the effects of aqueous and ethanolic extracts of lemongrass on the third instar larvae of the date moth Ephestia cautella Walker (Lepidoptera: Pyralidae). The results obtained showed that there were toxic effects of aqueous extract, which produced the highest mortality rate of 43.35% at 5% concentration72 hours after treatment, whereas the lowest mortality rate of 17% was obtained at 0.5% concentration, 72 hours after treatment. The LC50 was 0.082%. The results indicated

Digital image manipulation has become increasingly prevalent due to the widespread availability of sophisticated image editing tools. In copy-move forgery, a portion of an image is copied and pasted into another area within the same image. The proposed methodology begins with extracting the image's Local Binary Pattern (LBP) algorithm features. Two main statistical functions, Stander Deviation (STD) and Angler Second Moment (ASM), are computed for each LBP feature, capturing additional statistical information about the local textures. Next, a multi-level LBP feature selection is applied to select the most relevant features. This process involves performing LBP computation at multiple scales or levels, capturing textures at different

Wildfire risk has globally increased during the past few years due to several factors. An efficient and fast response to wildfires is extremely important to reduce the damaging effect on humans and wildlife. This work introduces a methodology for designing an efficient machine learning system to detect wildfires using satellite imagery. A convolutional neural network (CNN) model is optimized to reduce the required computational resources. Due to the limitations of images containing fire and seasonal variations, an image augmentation process is used to develop adequate training samples for the change in the forest’s visual features and the seasonal wind direction at the study area during the fire season. The selected CNN model (Mob