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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.

Mobile payment (or m-payment), a relatively new digital banking system targeting Pakistani customers, is rapidly expanding. This study aims to explore the elements that impact customer behaviour and encourage the adoption of m-payment in Pakistan.

This study used a quantitative research design, surveying 315 m-payment users residing in three Pakistani cities. A conceptual framework was developed by extending the meta-unified theory of acceptance and use technology (meta-UTAUT) model to incorporate institutional privacy concerns (IPC) and institutional source reliability (ISR). Data analysis was conducted using partial least squares structural equation modelling via Smart PLS 4.0 software.

This study’s results indicate that behavioural intention (BI) is the primary driver ofm-payment use behaviour. The findings also reveal that attitude (AT), performance expectancy (PE), facilitating conditions (FC), social influence (SI), effort expectancy (EE), IPC and ISR significantly influence BI. Notably, PE and FC are positively associated with AT, while EE and SI have no significant impact on AT.

This study has two key limitations. First, its focus on only Pakistani m-payment users limits the broader applicability of the results. Second, the cross-sectional design overlooks potential longitudinal changes in users’ attitude. Future research should aim to recruit diverse country samples and conduct comparative studies, thereby enhancing the understanding of m-payment adoption.

This study provides insights for service providers and marketers, identifying key factors that influence m-payment adoption. Convenience emerges as a critical consideration, suggesting it may drive customer behaviour.

This research significantly advances the field of m-payment studies by investigating the key factors influencing Pakistani consumers’ adoption of m-payment, extending the meta-UTAUT model to include IPC and ISR. By applying this extended framework to the context of Pakistani consumers’ acceptance and use of m-payment, this study provides new insights into the complex factors driving m-payment adoption in developing Asian countries like Pakistan, addressing a significant research gap and paving the way for future studies.

The emergence of smart cities, metropolises that integrate physical infrastructure, digital technology, and data analytics, and that focus on urban sustainability, have profoundly changed urban development. In the modern digital era, robust infrastructure has become an indispensable catalyst for urban advancement. Kuwait is dedicated to the integration of diverse renewable energy technologies in the development of smart cities that enhance energy security, promote innovation, and contribute to global climate change mitigation efforts. Focusing on smart cities within Gulf Cooperation Council (GCC) countries, a review is presented of how successfully they have effectively combined technology, infrastructure, and sustainability to serve as models for new global and regional developments. Insights into what makes a city smart are provided in different settings.

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.