Accurate air quality forecasting is important in pollution prevention and risk reduction. Effective short-term and long-term forecasting models are needed. This study investigated the need for a new model to forecast air pollution concentrations in three cities with distinct characteristics: a city with high industrial activity, a city with a high population density and urbanization, and an agricultural city. The air pollution data were collected using the Sentinel-5P satellite and Google Earth Engine to apply descriptive analysis and comparison of two years, 2022 and 2023. The studied cities were Al Riyadh (high population), Al Jubail (industrial), and Najran (agricultural) in Saudi Arabia. The selected pollutants were SO₂, NO₂, CO, O₃, and HCHO. In addition, this study investigated the variations observed in all the pollutants during the months of the year, the correlations between the contaminants, and the correlation between NO₂ and the meteorological data. Based on our findings, Al Jubail had the highest level of all the pollutants during the two years, except for NO₂, for which the highest level was observed in Al Riyadh, which has witnessed notable urbanization and development recently. Moreover, this study developed a forecasting model for the concentration of NO₂ based on weather data and the previous values of NO₂ using Long Short-Term Memory (LSTM) and Time2Vec. The modeling proved that any model that is trained on data collected from a specific city is not suitable for predicting the pollution level in another city and the level of another pollutant, as the three cities have different correlations with the pollutants and the weather data. The proposed model demonstrated a superior accuracy in predicting NO₂ concentrations compared to traditional LSTM models, effectively capturing temporal patterns and achieving minimal prediction errors, which contributes to ongoing efforts to understand the dynamics of air pollution based on cities’ characteristics and the period of the year.

In energy‐constrained networks, designing an energy‐efficient MAC protocol is crucial since the MAC protocol controls the access of the nodes to the shared wireless medium. Indeed, an efficient MAC protocol should reduce or even mitigate the consumption of the main sources of energy wastage, namely, overhearing, collision, and idle listening, and hence the network lifetime will be improved. One of the most efficient solutions is designing a MAC protocol that utilizes the quorum concept to increase energy efficiency by reducing idle listening and overhearing. Researchers have proposed several quorum‐based MAC protocols to conserve energy. This paper presents a detailed overview and classification of several existing quorum‐based sleep/wakeup scheduling MAC protocols. We classify these quorum‐based MAC protocols into homogeneous and heterogeneous based on their quorum system characteristics. Moreover, we further classify these protocols according to the guaranteed intersection between any two quorums into fixed and limited. Furthermore, we compare these protocols in terms of their quorum system characteristics, limitations, and performance. Finally, a guide for future research directions is presented.

The condensation of water vapor plays a crucial role in various applications, including combating water scarcity. In this study, by employing molecular dynamics simulations, we delved into the impact of graphene coatings on water vapor condensation on copper surfaces. Unique to this work was the exploration of various levels of graphene coverage and distribution, a facet largely unexplored in prior investigations. The findings demonstrated a notable increase in the rate of water vapor condensation and heat transfer performance as the graphene coverage was reduced. Using graphene coverages of 84%, 68%, and 52%, the numbers of condensed water molecules were 664, 735, and 880 molecules/ns, respectively. One of the most important findings was that when using the same graphene coverage of 68%, the rate of water vapor condensation and heat transfer performance increased as the graphene coating became more distributed. The overall performance of the water condensation correlated well with the energy and vibrational interaction between the graphene and the copper. This phenomenon suggests how a hybrid surface can enhance the nucleation and growth of a droplet, which might be beneficial for tailoring graphene-coated copper surfaces for applications demanding efficient water vapor condensation.

Rapid data growth in large systems has introduced significant challenges in real-time monitoring and analysis. One of these challenges is detecting anomalies in time series data with high-dimensional inputs that contain complex inter-correlations between them. In addition, the lack of labeled data leads to the use of unsupervised learning that relies on daily system data to train models, which can contain noise that affects feature extraction. To address these challenges, we propose PCA-AAE, a novel anomaly detection model for time series data using an Adversarial Autoencoder integrated with Principal Component Analysis (PCA). PCA contributes to analyzing the latent space by transforming it into uncorrelated components to extract important features and reduce noise within the latent space. We tested the integration of PCA into the model’s phases and studied its efficiency in each phase. The tests show that the best practice is to apply PCA to the latent code during the adversarial training phase of the AAE model. We used two public datasets, the SWaT and SMAP datasets, to compare our model with state-of-the-art models. The results indicate that our model achieves an average F1 score of 0.90, which is competitive with state-of-the-art models, and an average of 58.5% faster detection speed compared to similar state-of-the-art models. This makes PCA-AAE a candidate solution to enhance real-time anomaly detection in high-dimensional datasets.