Antibiotic resistance is an imminent threat to human health, requiring the development of effective alternate antibacterial agents. One such alternative includes nanoparticle (photo)­catalysts that are good at producing reactive oxygen species (ROS). Herein, we report the design and preparation of nitrogen-doped carbon dots functionalized with atomically dispersed copper centers by Cu–N coordination (Cu/NCD) that exhibit apparent antibacterial activity toward Gram-negative Escherichia coli (E. coli) under photoirradiation. The growth of E. coli cells is found to be markedly inhibited by Cu/NCD under 365 nm photoirradiation, whereas no apparent inhibition is observed in the dark or with the copper-free carbon dots alone. This is ascribed to the prolonged photoluminescence lifetime of Cu/NCD that facilitates the separation of photogenerated electron–hole pairs and ROS formation. The addition of tert-butyl alcohol is found to completely diminish the antimicrobial activity, suggesting that hydroxyl radicals are responsible for microbial death. Consistent results are obtained from fluorescence microscopic studies using CellROX green as the probe. Similar bactericidal behaviors are observed with Gram-positive Staphylococcus epidermidis (S. epidermidis). The copper content within the carbon material is optimized at a low loading of 1.09 wt %, reducing the possibility of toxic copper-ion leaching. Results from this study highlight the significance of carbon-based nanocomposites with isolated metal species as potent antimicrobial reagents.

Sustainable hydrogen gas production is critical for future fuel infrastructure. Here, a series of phosphorous-doped carbon nitride materials were synthesized by thermal annealing of urea and ammonium hexafluorophosphate, and platinum...

The use of data analytics in virology is a rapidly growing means to provide accurate and reliable information to healthcare providers. Its mechanisms allow for deeper comprehension and characterization of pathogens (i.e., virus transmission rate and behavior). Artificial intelligence and machine learning technology have shown the potential to forecast and subdue the spread of coronaviruses. When applied to extended periods, however, the ability of these prediction models to anticipate disease spread is not promising. The development of superior algorithms is essential to improving COVID-19 forecasting accuracy. This drawback has motivated us to conduct this study with the objective of developing a COVID-19 forecasting algorithm that functions over an extended period of time. This paper highlights the mechanism of the coronavirus forecast: the Deep Learning (DL) approach. The combined utilization of online data sets and the DL approach was employed in the investigation of the life cycle and spread of COVID-19 in the Kingdom of Saudi Arabia and the Kingdom of Bahrain.

The mechanisms of data analytics and machine learning can allow for a profound conceptualization of viruses (such as pathogen transmission rate and behavior). Consequently, such models have been widely employed to provide rapid and accurate viral spread forecasts to public health officials. Nevertheless, the capability of these algorithms to predict outbreaks is not capable of long-term predictions. Thus, the development of superior models is crucial to strengthen disease prevention strategies and long-term COVID-19 forecasting accuracy. This paper provides a comparative analysis of COVID-19 forecasting models, including the Deep Learning (DL) approach and its examination of the circulation and transmission of COVID-19 in the Kingdom of Saudi Arabia (KSA), Kuwait, Bahrain, and the UAE.

Due to the advancement in information technology and the boom of micro-blogging platforms, a growing number of online reviews are posted daily on product distributed platforms in the form of spontaneous and insightful user feedback, and these can be used as a significant data source to understand user experience (UX) and satisfaction. However, despite the vast amount of online reviews, the existing literature focuses on online ratings and ignores the real textual context in reviews. We proposed a three-step UX quantification model from online reviews to understand customer satisfaction using the effect-based Kano model. First, the relevant online reviews are selected using various filter mechanisms. Second, UX dimensions (UXDs) are extracted using a proposed method called UX word embedding Latent Dirichlet allocation (UXWE-LDA) and sentiment orientation using a transformer-based pipeline. Then, the casual relationships are identified for the extracted UXDs. Third, the UXDs are mapped on the customer satisfaction model (effect-based Kano) to understand the user perspective about the system, product, or services. Finally, the different parts of the proposed quantification model are evaluated to examine the performance of this method. We present different results of the proposed method in terms of accuracy, topic coherence (TC), Topic-wise performance, and expert-based evaluation for the proposed framework validation. For review quality filters, we achieved 98.49% accuracy for the spam detection classifier and 95% accuracy for the relatedness detection classifier. The results show that the proposed method for the topic extractor module always gives a higher TC value than other models such as WE-LDA and LDA. Regarding topic-wise performance measures, UXWE-LDA achieves a 3% improvement on average compared to LDA due to the incorporation of semantic domain knowledge. We also compute the Jaccard coefficient similarity between the extracted dimensions using UXWE-LDA and UX experts-based analysis for checking the mutual agreement, which is 0.3, 0.5, and 0.4, respectively. Based on the Kano model, the presented study has potential implications concerning issues and knowing the product’s strengths and weaknesses in product design.