Purpose of Review The coronavirus disease-2019 (COVID-19) is a global pandemic which has not been seen in recent history, leaving behind deep socioeconomic damages and huge human losses with the disturbance in the healthcare sector. Despite the tremendous international effort and the launch of various clinical trials for the containment of this pandemic, no effective therapy has been proven yet. Recent Findings This review has highlighted the different traditional therapeutic techniques, along with the potential contribution of nanomedicine against the severe acute respiratory syndrome corovirus-2 (SARS-CoV-2). Repositioning of the drugs, such as remdesivir and chloroquine, is a rapid process for the reach of safe therapeutics, and the related clinical trials have determined effects against COVID-19. Various protein-based SARS-CoV-2 vaccine candidates have successfully entered clinical phases, determining positive results. The self-assembled and metallic nanovaccines mostly based on the antigenic properties of spike (S) protein are also approachable, feasible, and promising techniques for lowering the viral burden. Summary There are number of NP-based diagnostic systems have been reported for coronaviruses (CoVs) and specifically for SARS-CoV-2. However, extensive studies are still necessary and required for the nanoparticle (NP)-based therapy.

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Smart mobility is an imperative facet of smart cities, and the transition of conventional automotive systems to connected and automated vehicles (CAVs) is envisioned as one of the emerging technologies on urban roads. The existing AV mobility environment is perhaps centered around road users and infrastructure, but it does not support future CAV implementation due to its proximity with distinct modules nested in the cyber layer. Therefore, this paper conceptualizes a more sustainable CAVenabled mobility framework that accommodates all cyber-based entities. Further, the key to a thriving autonomous system relies on accurate decision making in real-time, but cyberattacks on these entities can disrupt decision-making capabilities, leading to complicated CAV accidents. Due to the incompetence of the existing accident investigation frameworks to comprehend and handle these accidents, this paper proposes a 5Ws and 1H-based investigation approach to deal with cyberattack-related accidents. Further, this paper develops STRIDE threat modeling to analyze potential threats endured by the cyber-physical system (CPS) of a CAV ecosystem. Also, a stochastic anomaly detection system is proposed to identify the anomalies, abnormal activities, and unusual operations of the automated driving system (ADS) functions during a crash analysis.INDEX TERMS CAV-enabled transport mobility environment, cybersecurity, STRIDE threat modeling, accident investigation.

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Autonomous driving (AD) has developed tremendously in parallel with the ongoing development and improvement of deep learning (DL) technology. However, the uptake of artificial intelligence (AI) in AD as the core enabling technology raises serious cybersecurity issues. An enhanced attack surface has been spurred on by the rising digitization of vehicles and the integration of AI features. The performance of the autonomous vehicle (AV)-based applications is constrained by the DL models' susceptibility to adversarial attacks despite their great potential. Hence, AI-enabled AVs face numerous security threats, which prevent the large-scale adoption of AVs. Therefore, it becomes crucial to evolve existing cybersecurity practices to deal with risks associated with the increased uptake of AI. Furthermore, putting defense models into practice against adversarial attacks has grown in importance as a field of study amongst researchers. Therefore, this study seeks to provide an overview of the most recent adversarial defensive and attack models developed in the domain of AD.

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