In the last decade, ground-level ozone exposure has led to a significant increase in environmental and health risks. Thus, it is essential to measure and monitor atmospheric ozone concentration levels. Specifically, recent improvements in machine learning (ML) processes, based on statistical modeling, have provided a better approach to solving these risks. In this study, we compare Naive Bayes, K-Nearest Neighbors, Decision Tree, Stochastic Gradient Descent, and Extreme Gradient Boosting (XGBoost) algorithms and their ensemble technique to classify ground-level ozone concentration in the El Paso-Juarez area. As El Paso-Juarez is a non-attainment city, the concentrations of several air pollutants and meteorological parameters were analyzed. We found that the ensemble (soft voting classifier) of algorithms used in this paper provide high classification accuracy (94.55%) for the ozone dataset. Furthermore, variables that are highly responsible for the high ozone concentration such as Nitrogen Oxide (NOx), Wind Speed and Gust, and Solar radiation have been discovered.

In recent years, integrating distributed energy resources has emerged as a pervasive trend in competitive energy markets. The idea of virtual power plants (VPPs) has gained traction among researchers and startups, offering a solution to address diverse social, economic, and environmental requirements. A VPP comprises interconnected distributed energy resources collaborating to optimize operations and participate in energy markets. However, existing VPPs confront numerous challenges, including the unpredictability of renewable energy sources, the intricacies and fluctuations of energy markets, and concerns related to insecure communication and data transmission. This article comprehensively reviews the concept, historical development, evolution, and components of VPPs. It delves into the various issues and challenges encountered by current VPPs. Furthermore, the article explores the potential of artificial intelligence (AI) in mitigating these challenges, investigating how AI can enhance the performance, efficiency, and sustainability of future smart VPPs.

In recent years, computing has been moving rapidly from the centralized cloud to various edges. For instance, electric vehicles (EVs), one of the next-generation computing platforms, have grown in popularity as a sustainable alternative to conventional vehicles. Compared with traditional ones, EVs have many unique advantages, such as less environmental pollution, high energy utilization efficiency, simple structure, and convenient maintenance etc. Meanwhile, it is also currently facing lots of challenges, including short cruising range, long charging time, inadequate supporting facilities, cyber security risks, etc. Nevertheless, electric vehicles are still developing as a future industry, and the number of users keeps growing, with governments and companies around the world continuously investing in promoting EV-related supply chains. As an emerging and important computing platform, we comprehensively study electric vehicular systems and state-of-the-art EV-related technologies. Specifically, this paper outlines electric vehicles' history, major architecture and components in hardware and software, current state-of-the-art technologies, and anticipated future developments to reduce drawbacks and difficulties.

In recent years, electric vehicles (EVs) have emerged as a sustainable alternative to conventional automobiles. Distinguished by their environmental friendliness, superior performance, reduced noise, and low maintenance requirements, EVs offer numerous advantages over traditional vehicles. The integration of electric vehicles with cloud computing has heralded a transformative shift in the automotive industry. However, as EVs become increasingly interconnected with the internet, various devices, and infrastructure, they become susceptible to cyberattacks. These attacks pose a significant risk to the safety, privacy, and functionality of both the vehicles and the broader transportation infrastructure. In this paper, we delve into the topic of electric vehicles and their connectivity to the cloud. We scrutinize the potential attack vectors that EVs are vulnerable to and the consequential impact on vehicle operations. Moreover, we outline both general and specific strategies aimed at thwarting these cyberattacks. Additionally, we anticipate future developments aimed at enhancing EV performance and reducing security risks.