Multi-Tier 3D Trajectory Planning for Cellular-Connected UAVs in Complex Urban Environments

Cellular-connected unmanned aerial vehicles (UAVs) present a viable solution to address communication and navigation limitations by leveraging base stations for air–ground communication. However, in complex urban scenarios with stringent communication requirements, achieving asymmetrical control becomes crucial to strike a balance between communication reliability and flight safety. Moreover, existing cellular-connected UAV trajectory planning algorithms often struggle to handle real scenes with sudden and intricate obstacles. To address the aforementioned challenges, this paper presents the multi-tier trajectory planning method (MTTP), which takes into account air–ground communication service assurance and collision avoidance in intricate urban environments. The proposed approach establishes a flight risk model that accounts for both the outage probability of UAV-ground base station (GBS) communication and the complexity of flight environments, and transforms the inherently complex three-dimensional (3D) trajectory optimization problem into a risk distance minimization model. To optimize the flight trajectory, a hierarchical progressive solution approach is proposed, which combines the strengths of the heuristic search algorithm (HSA) and deep reinforcement learning (DRL) algorithm. This innovative fusion of techniques empowers MTTP to efficiently navigate complex scenarios with sudden obstacles and communication challenges. Simulations show that the proposed MTTP method achieves a more superior performance of trajectory planning than the conventional communication-based solution, which yields a substantial reduction in flight distance of at least 8.49% and an impressive 10% increase in the mission success rate. Furthermore, a real-world scenario is chosen from the Yuhang District, Hangzhou (a southern Chinese city), to validate the practical applicability of the MTTP method in highly complex operating scenarios

Synthesis of bioactive (1→6)-β-glucose branched poly-amido-saccharides that stimulate and induce M1 polarization in macrophages

β-Glucans are of significant interest due to their potent antitumor and immunomodulatory activities. Nevertheless, the difficulty in purification, structural heterogenicity, and limited solubility impede the development of structure-property relationships and translation to therapeutic applications. Here, we report the synthesis of a new class of (1→6)-β-glucose-branched poly-amido-saccharides (PASs) as β-glucan mimetics by ring-opening polymerization of a gentiobiose-based disaccharide β-lactam and its copolymerization with a glucose-based β-lactam, followed by post-polymerization deprotection. The molecular weight (Mn) and frequency of branching (FB) of PASs is readily tuned by adjusting monomer-to-initiator ratio and mole fraction of gentiobiose-lactam in copolymerization. Branched PASs stimulate mouse macrophages, and enhance production of pro-inflammatory cytokines in a FB-, dose-, and Mn-dependent manner. The stimulation proceeds via the activation of NF-κB/AP-1 pathway in a Dectin-1-dependent manner, similar to natural β-glucans. The lead PAS significantly polarizes primary human macrophages towards M1 phenotype compared to other β-glucans such as lentinan, laminarin, and curdlan.Published versionThis work was supported in part by Boston University and Boston University Nanotechnology Innovation Center (BUnano), and the National Institutes of Health (NIH R01EB017722 (M.W.G.), T32GM130546 (E.M.B.), and F30 CA257566 (E.M.B.))

A Comparative Study of Location-sharing Privacy Preferences in the U.S. and China (CMU-CyLab-12-003)

While prior studies have provided us with an initial understanding of people's location-sharing privacy preferences, they have been limited to Western countries and have not investigated the impact of the granularity of location disclosures on people’s privacy preferences. We report findings of a three-week comparative study collecting location traces and location-sharing preferences from two comparable groups in the U.S. and China. Results of the study shed further light on the complexity of people’s location-sharing privacy preferences and key attributes influencing willingness to disclose locations to others and to advertisers. While our findings reveal many similarities between U.S. and Chinese participants, they also show interesting differences, such as differences in willingness to share location at 'home' and at 'work' and differences in the granularity of disclosures people feel comfortable with. We conclude with a discussion of implications for the design of location-sharing applications and location-based advertising.</p