Synthesis and Characterization of ZnO-Nanostructured Particles Produced by Solar Ablation.

Nowadays, nanotechnology offers opportunities to create new features and functions of emerging materials. Correlation studies of nanostructured materials' development processes with morphology, structure, and properties represent one of the most important topics today due to potential applications in all fields: chemistry, mechanics, electronics, optics, medicine, food, or defense. Our research was motivated by the fact that in the nanometric domain, the crystalline structure and morphology are determined by the elaboration mechanism. The objective of this paper is to provide an introduction to the fundamentals of nanotechnology and nanopowder production using the sun's energy. Solar energy, as part of renewable energy sources, is one of the sources that remain to be exploited in the future. The basic principle involved in the production of nanopowders consists of the use of a solar energy reactor concentrated on sintered targets made of commercial micropowders. As part of our study, for the first time, we report the solar ablation synthesis and characterization of Ni-doped ZnO performed in the CNRS-PROMES laboratory, UPR 8521, a member of the CNRS (French National Centre for Scientific Research). Also, we study the effect of the elaboration method on structural and morphological characteristics of pure and doped ZnO nanoparticles determined by XRD, SEM, and UV-Vis.

Early Melanoma Detection Based on a Hybrid YOLOv5 and ResNet Technique.

Skin cancer, specifically melanoma, is a serious health issue that arises from the melanocytes, the cells that produce melanin, the pigment responsible for skin color. With skin cancer on the rise, the timely identification of skin lesions is crucial for effective treatment. However, the similarity between some skin lesions can result in misclassification, which is a significant problem. It is important to note that benign skin lesions are more prevalent than malignant ones, which can lead to overly cautious algorithms and incorrect results. As a solution, researchers are developing computer-assisted diagnostic tools to detect malignant tumors early. First, a new model based on the combination of "you only look once" (YOLOv5) and "ResNet50" is proposed for melanoma detection with its degree using humans against a machine with 10,000 training images (HAM10000). Second, feature maps integrate gradient change, which allows rapid inference, boosts precision, and reduces the number of hyperparameters in the model, making it smaller. Finally, the current YOLOv5 model is changed to obtain the desired outcomes by adding new classes for dermatoscopic images of typical lesions with pigmented skin. The proposed approach improves melanoma detection with a real-time speed of 0.4 MS of non-maximum suppression (NMS) per image. The performance metrics average is 99.0%, 98.6%, 98.8%, 99.5, 98.3%, and 98.7% for the precision, recall, dice similarity coefficient (DSC), accuracy, mean average precision (MAP) from 0.0 to 0.5, and MAP from 0.5 to 0.95, respectively. Compared to current melanoma detection approaches, the provided approach is more efficient in using deep features.