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The purpose of the study is to classify the radiographic images into three categories such as fillings, cavity and implant to identify dental diseases because dental disease is a very common dental health problem for all people. The detection of dental issues and the selection of the most suitable method of treatment are both determined by the results of a radiological examination. Dental x-rays provide important information about the insides of teeth and their surrounding cells, which helps dentists detect dental issues that are not immediately visible. The analysis of dental x-rays, which is typically done by dentists, is a time-consuming process that can become an error-prone technique due to the wide variations in the structure of teeth and the dentist's lack of expertise. The workload of a dental professional and the chance of misinterpretation can be decreased by the availability of such a system, which can interpret the result of an x-ray automatically.

This study uses deep learning (DL) models to identify dental diseases in order to tackle this issue. Four different DL models, such as ResNet-101, Xception, DenseNet-201 and EfficientNet-B0, were evaluated in order to determine which one would be the most useful for the detection of dental diseases (such as fillings, cavity and implant).

Loss and accuracy curves have been used to analyze the model. However, the EfficientNet-B0 model performed better compared to Xception, DenseNet-201 and ResNet-101. The accuracy, recall, F1-score and AUC values for this model were 98.91, 98.91, 98.74 and 99.98%, respectively. The accuracy rates for the Xception, ResNet-101 and DenseNet-201 are 96.74, 93.48 and 95.65%, respectively.

The present study can benefit dentists from using the DL model to more accurately diagnose dental problems.

This study is conducted to evaluate dental diseases using Convolutional neural network (CNN) techniques to assist dentists in selecting the most effective technique for a particular clinical condition.

Paper aims to preparation of new acid disperse dyes based on thiadiazol derivatives and evaluation of their use as antimicrobial colorants in digital transfer-printing ink formulations for printing onto polyester fabric substrates.

New disperse dyes based on 1,3,4 - thiadiazol derivative (dyes 1–3) were prepared and evaluated by different analysis then formulated as colored materials in the ink formulations. The viscosity, dynamic surface tension and particle size distribution of the prepared inks were measured. The printed polyester fabric substrates were tested using a variety of tests, including light fastness, washing, alkali perspiration and Crock fastness, as well as depth of penetration. Density-functional theory (DFT) calculations were carried out at the Becke3-Lee-Yang-parr (B3LYP) level using the 6–311** basis set, and the biological activity of the prepared disperse dyes was investigated.

The obtained results of the physical of the prepared ink revealed that thiadiazol disperse ink is a promising ink formulation for polyester printing and agrees with the quality of the printed polyester fabric. The optimization geometry for molecular structures agreed with the analysis of these compounds. The HOMO/LUMO and energy gap of the studied system were discussed. The molecular docking analysis showed strong interaction with DNA Gyrase and demonstrated to us the high ability of these inks to act as antimicrobial agents.

The prepared inks containing the prepared thiadiazol disperse dye were high-performance and suitable for this type of printing technique, according to the results. The prepared inks resist the growth of microorganisms and thus increase the ink's storage stability.

The prepared disperse dyes based on 1,3,4 - thiadiazol derivative (dyes 1–3) can be a promising colorant in different applications, like some types of paint formulations and as a colorant in printing of different fabric substrates.