Ag nanoparticles were prepared using Nd:YAG laser from Ag matel in distilled water using different energies laser (100 and 600) mJ using 200 pulses, and study the effect of the preparation conditions on the structural characteristics of and then study the effect of nanoparticles on the rate of killing the two types of bacteria particles (Staph and E.coli). The goal is to prepare the nanoparticle effectively used to kill bacteria.

In this work we study the influence of the laser pulse energy and ablation time on the aluminum nanoparticles productivity during nanosecond laser ablation of bulk aluminum immersed in liquid.
Aluminum nanoparticles were synthesized by pulsed laser ablation of Al targets in ethanol for 3-8 minutes using the 1064 nm wavelength of a Nd:YAG laser with energies of 300-500 mJ per pulse.The laser energy was varied between 300 and 500 mJ/pulse, whereas the ablation time was set to 5 minutes. UV-Visible absorption spectra was used for the characterization and comparison of products.

Background: The PMMA polymer denture base materials are low mechanical properties, adaptation of the denture base to underlying tissue is important for retention and stability of denture. The aim of the study was toevaluate the effect of mixtureZrO2-Al2O3 nanoparticles on impact strength, transverse strength, hardness, roughness, denture base adaptation of heat cured acrylic resin denture base material. Materials and methods: One hundred (100) specimens were prepared, the specimens were divided into five groups (20 specimens to each) according to the test type, each group was subdivided in to two subgroups (control and experimental) each subgroup consist of 10 specimens, the experimental group included mixture of 2% (ZrO2-Al2O3ratio2:1) b

This study aimed to determine histological and functional effects of Galangin (Gal) conjugated with Gold nanoparticles (AuNPs) on the Kidneys male albino mice treated with CCL₄. Gold nanoparticles were prepared chemically by Turkevich Method. Characterizing of the prepared AuNPs and AuNPs+Gal was carried out using UV Spectrophotometry, X-Ray Diffraction (XRD) and particle size,. For the in vivo study, 42 adult male albino mice were used and randomly distributed into seven groups and experiment extended for 14 days, first group (G1) was control group without any treatment, (G2) group injected intra-peritoneal (i.p) with CCl₄ once a week to the end of experiments, (G3)  injected with  AuNPs, (G4 and

Thirty one  samples of gum swabs were collected from patients with tooth caries (5-30 years old)  from the College of Science (Biology department )- University of Baghdad- Iraq for the period from October 2018 to December 2018. , The samples were transported, after inoculation in a transport media (nutrient broth), to the laboratory of the College of Science and then cultured on mannitol salt agar and  blood agar). The isolates belonging to Staphylococcus spp. were identified by biochemical tests and Vitek 2 compact system, while the more antibiotic resistant isolates were identified by using Polymerase Chain Reaction(ï´¾PCR)  and sequencing of 16SrRNA . The results showed sharp UV absorption peaks at 330 - 340nm and AFM at 5

Sludge from stone-cutting (SSC) factories and stone mines cannot be used as decorative stones, stone powder, etc. These substances are left in the environment and cause environmental problems. This study aim is to produce artificial stone composite (ASC) using sludge from stone cutting factories, cement, unsaturated resin, water, silicon carbide nanoparticles (SiC-NPs), and nano-graphene oxide (NGO) as fillers. Nano graphene oxide has a hydrophobic plate structure that water is not absorbed due to the lack of surface tension on these plates. NGO has a significant effect on the properties of artificial stone due to its high specific surface area and low density in the composite. Its uniform distribution in ASC is very low due to its hydropho

Activity recognition (AR) is a new interesting and challenging research area with many applications (e.g. healthcare, security, and event detection). Basically, activity recognition (e.g. identifying user’s physical activity) is more likely to be considered as a classification problem. In this paper, a combination of 7 classification methods is employed and experimented on accelerometer data collected via smartphones, and compared for best performance. The dataset is collected from 59 individuals who performed 6 different activities (i.e. walk, jog, sit, stand, upstairs, and downstairs). The total number of dataset instances is 5418 with 46 labeled features. The results show that the proposed method of ensemble boost-based classif