The global market share of electric vehicles (EVs) is on the rise, resulting in a rapid increase in their charging demand in both spatial and temporal domains. A remedy to shift the extra charging loads at peak hours to off-peak hours, caused by charging EVs at public charging stations, is an online pricing strategy. This paper presents a novel combinatorial online pricing strategy that has been established upon a reward-based model to prevent network instability and power outages. In the proposed solution, the utility provides incentives to the charging stations for their contributions in the EVs charging load shifting. Then, a constraint optimization problem is developed to minimize the total charging demand of the EVs during peak hours. To control the EVs charging demands in supporting utility’s stability and increasing the total revenue of the charging stations, treated as a multi-agent framework, an online reinforcement learning model is developed which is based on the combination of an adaptive heuristic critic and recursive least square algorithm. The effective performance of the proposed model is validated through extensive simulation studies such as qualitative, numerical, and robustness performance assessment tests. The simulation results indicate significant improvement in the robustness and effectiveness of the proposed solution in terms of utility’s power saving and charging stations’ profit.

We present a summary of the latest research on digital emotion regulation (DER) within social media applications and an overview of recent research on ER interventions for social media. We discuss how social media platforms are utilized as instruments to shape others’ emotional states.

Recent studies have shown how motion-based biometrics can be used as a form of user authentication and identification without requiring any human cooperation. This category of behavioural biometrics deals with the features we learn in our life as a result of our interaction with the environment and nature. This modality is related to changes in human behaviour over time. The developments in these methods aim to amplify continuous authentication such as biometrics to protect their privacy on user devices. Various Continuous Authentication (CA) systems have been proposed in the literature. They represent a new generation of security mechanisms that continuously monitor user behaviour and use this as the basis to re-authenticate them periodically throughout a login session. However, these methods usually constitute a single classification model which is used to identify or verify a user. This work proposes an algorithm to blend behavioural biometrics with multi-factor authentication (MFA) by introducing a two-step user verification algorithm that verifies the user’s identity using motion-based biometrics and complements the multi-factor authentication, thus making it more secure and flexible. This two-step user verification algorithm is also immune to adversarial attacks, based on our experimental results that show how the rate of misclassification drops while using this model with adversarial data.

Wildfires pose significant challenges globally due to their increasing frequency and intensity. This paper proposes a comprehensive multi-agent system (MAS) for wildfire management, leveraging cutting-edge technologies to enhance various stages of wildfire response. The system integrates diverse data sources, including historical fire incidents, real-time weather data, topographical information, land cover data, and spatial data, to improve the accuracy of wildfire prediction. Data pre-processing and feature engineering are employed to optimize datasets for subsequent modelling. A predictive model incorporating methodologies such as Artificial Neural Networks (ANNs), Long Short-Term Memory (LSTM) networks, and agent-based models refines integrated data for precise forecasts. The wildfire detection module deploys UAVs with thermal infrared cameras, IoT sensors, and deep learning-based fire detection models for real-time monitoring and early detection. The wildfire monitoring and tracking agent utilizes UAVs, wireless sensor networks, and deep reinforcement learning algorithms to provide real-time insights into fire progression. The wildfire area estimation agent employs machine learning and clustering algorithms to predict fire spread and estimate burning areas accurately. For evacuation planning, the system integrates agent-based models, traffic simulations, and decision support systems to optimize evacuation routes and strategies. The situational awareness and decision-making support agent consolidate data from various sources, offering geospatial visualizations and strategic recommendations. Finally, the firefighting strategy planner uses predictive models, optimization algorithms, and UAV swarm technology to allocate resources effectively. Despite its conceptual nature, the proposed MAS for wildfire management is validated through extensive referencing of existing research and technological advancements, ensuring a solid theoretical foundation, and demonstrating its potential effectiveness. This integrated approach aims to enhance the efficiency and effectiveness of wildfire management, reducing the ecological and societal impact of wildfires.