The Corona Virus Disease 2019 (COVID-19) epidemic has deferred global progress in energy efficiency to a decade-long low, posing a threat to the achievement of international climate goals, and also profoundly affected the development of economics. To gain insight into the research frontiers and hotspots in energy efficiency and COVID-19, a systematic literature review and bibliometric analysis on economic effects are performed with the help of the bibliometric tools VOSviewer and Bibliometrix. This paper selects all the publications retrieved based on the subject terms in the Web of Science core collection. Firstly, this article performs a performance analysis of related publications to present the development and distribution of energy efficiency and COVID-19 from research areas, relevant sources, and influential articles. Afterward, a visual analysis of the literature called science mapping analysis is implemented to display the structural and dynamic organization of knowledge in energy efficiency and COVID-19 research. In the end, detailed discussions of two research hotspots and some theoretical and practical implications are concluded in the systematic literature review and bibliometric analysis findings, which may contribute to further development for researchers in the field of energy efficiency and eventually propel the progress of society and economy in an all-round way.

With the rapid growth of global air traffic, 4D-flight trajectory prediction (4D-FTP) using deep learning (DL) methods has become essential for applications such as flight delay prediction, fuel consumption analysis, and traffic management. However, the adversarial attacks pose significant security threats to DL-based 4D-FTP systems reliant on automatic dependent surveillance-broadcast (ADS-B) sensors. Furthermore, existing vulnerabilities in time series prediction (TSP) models for 4D-FTP remain underexplored due to the lack of effective and stealthy attack methods. To address this gap, we propose the Dynamic Quasi-Hyperbolic Momentum Iterative Attack with Small Perturbation (DQM-Attack). This method leverages quasi-hyperbolic momentum (QHM) and dynamic step sizes to optimize gradient utilization in 4D-FTP models. In addition, attack stealth is enhanced through the Manifold Smooth Module (MSM) and Sparse Smooth Reinforcement Learning (SS-RL). Experimental results demonstrate that DQM-Attack effectively disrupts predictions with minimal perturbations across four state-of-the-art TSP models. This study appears to be the first to address stealthy adversarial attacks on 4D-FTP systems, revealing a critical security vulnerability in air traffic management (ATM).

Benefiting from the robust feature representation capability of convolutional neural networks (CNNs), the object detection technology of intelligent high-altitude UAV remote sensing has been developed rapidly. In this field, the adversarial examples (AEs) pose serious security risks and vulnerabilities to deep learning-based systems. Due to the limitation of object size, image degradation, and scene brightness, adding adversarial disturbances to small and dense objects is extremely challenging. To study the threat of AE for UAV object detection, a dynamic bi-level integrated attack (DBI-Attack) is proposed for intensive multi-scale UAV object detection. Firstly, we use the dynamic iterative attack (DIA) method to generate perturbation on the classification level by improving the momentum iterative fast gradient sign method (MIM). Secondly, the bi-level adversarial attack method (BAAM) is constructed to add global perturbation on the decision level for completing the white-box attack. Finally, the integrated black-box attack method (IBAM) is combined to realize the black-box mislabeling and fabrication attacks. We experiment on the real drone traffic vehicle detection datasets to better evaluate the attack effectiveness. The experimental results show that the proposed method can achieve mislabeling and fabrication attacks on the UAV object detectors in black-box conditions. Furthermore, the adversarial training is applied to improve the model robustness. This work aims to call more attention to the adversarial and defensive aspects of UAV target detection models.