In this work, the dyes Rhodamine B and Coumarin 102 containing titanium dioxide nanoparticles were used as scattering centers to fabricate a random gain medium. The laser dye was dissolved in hexanol and methanol solvent respectively. The titanium dioxide nanoparticles were synthesized by DC reaction magnetron spraying technique. The random-gain medium was made by adding 2.5 mg of titanium dioxide nanoparticles to Rhodamine and coumarin 102 dyes by coating the glass cell with two-sided titanium dioxide with high spectral efficiency and low production cost. A narrow line optical emission was detected at 565 nm for Rhodamine B and 534 nm for coumarin 102, where it was found that rhodamine B dye has FWHM 8 nm and coumarin dye 102 has FWHM 9 nm

The dynamic development of computer and software technology in recent years was accompanied by the expansion and widespread implementation of artificial intelligence (AI) based methods in many aspects of human life. A prominent field where rapid progress was observed are high‐throughput methods in biology that generate big amounts of data that need to be processed and analyzed. Therefore, AI methods are more and more applied in the biomedical field, among others for RNA‐protein binding sites prediction, DNA sequence function prediction, protein‐protein interaction prediction, or biomedical image classification. Stem cells are widely used in biomedical research, e.g., leukemia or other disease studies. Our proposed approach of

S Khalifa E, N Adil A, AS Mazin M…, 2008

Introduction: Streptococcus mutans and lactobacilli are most important bacteria in the pathogenesis of dental caries. Cariogenic microflora has been associated to the primary caregiver transmission and sugary diets.

Aim: This study aimed to relate S. mutans and lactobacilli in the plaque to demographic factors in Iraqi children.

Materials and methods: The study included 135 children aged 3-10 years. Samples of their dental plaque were taken from the upper second primary molars on the buccal surfaces using a clean toothpick. Each sample was stored in 1 ml of n

Automated detection of Dubas palm infestation by image processing techniques has practical significance as it can improve agricultural efficiency, increase crop yield and quality, protect the environment, and provide data-driven insights. It also reduces the human effort required for pest control and enhances sustainability. In this study, we aimed to automate the detection of Dubas bug infestation in palm trees using deep learning with transfer learning residual neural networks. Based on four models: InceptionResNetV2, ResNet18, ResNet50, and ResNet101, the data used in this study were obtained by drone photography, many images were taken, and then the infected area was extracted. Using two types of data, 185 infected images and 185 health