The multi-drone-truck collaborative delivery, where unmanned trucks serve as mobile supply stations for drones, effectively combines the strengths of both vehicles and presents wide application prospects. But the majority of existing literature restricts drone launch and retrieve operations (LARO) to stationary trucks, and potential drone route collisions are mostly ignored. This leads to inability to fully exploit the capability of drones. We address these gaps and introduce a new variant of multi-drone-truck collaborative delivery. However, the scheduling for drones and truck faces high-dimensional solution space and complex constraints, making it almost impossible for centralized solving. To this end, we develop a hierarchical solution framework that decomposes the complete problem into two levels of subproblem. The upper solver centrally allocates tasks and schedules when drones to launch, while the lower solver, based on multi-agent reinforcement learning (MARL), plans paths for each drone agent in a decentralized but cooperative manner. In addition, we validate the effectiveness of our method by benchmarking it against three state-of-the-art approaches, demonstrating its superiority in terms of both efficiency and collision avoidance.

The design of a scalable Quantum Control Architecture (QCA) faces two primary challenges.First, the continuous growth in qubit counts has rendered distributed QCA inevitable, yet the nondeterministic latencies inherent in feedback loops demand cycleaccurate synchronization across multiple controllers.Existing synchronization strategies -whether lock-step or demand-drivenintroduce significant performance penalties.Second, existing quantum instruction set architectures are polarized, being either too abstract or too granular.This lack of a unifying design necessitates recurrent hardware customization for each new control requirement, which limits the system's reconfigurability and impedes the path toward a scalable and unified digital microarchitecture.Addressing these challenges, we propose Distributed-HISQ, featuring: (i) HISQ, A universal instruction set that redefines quantum control with a hardware-agnostic design.By decoupling from quantum operation semantics, HISQ provides a unified language for control sequences, enabling a single microarchitecture to support various control methods and enhancing system reconfigurability.(ii) BISP, a booking-based synchronization protocol that can potentially achieve zero-cycle synchronization overhead.The feasibility and adaptability of Distributed-HISQ are validated through its implementation on a commercial quantum control system targeting superconducting qubits.We performed a comprehensive evaluation using a customized quantum software stack.Our results show that BISP effectively synchronizes multiple control boards, leading to a 22.8% reduction in average program execution time and a ∼ 5× reduction in infidelity when compared to an existing lock-step synchronization scheme.

Blockchain rewriting has become widely explored for addressing data deletion requirements, such as error data deletion, space-saving, and compliance with the “right-to-be-forgotten” rule. However, existing approaches are inadequate for handling cross-chain redaction issues, issues, in facing with the increasing need for inter-chain communication. In particular, transaction rewriting on a blockchain might have relevant effects on the states of other blockchains. The cross-chain interoperability results in inter-chain transactions with more complex dependency relations. The issues pose new challenges to achieve rewriting consistency, for example, ensuring the rewriting of related transactions when a transaction is being modified, and achieve atomic rewriting, whereby two cross-chain transactions must either all, or neither, be processed. This paper introduces a cross-chain solution IvyRedaction, with an emphasis on customizing a decentralized intermediary for generating and maintaining global cross-chain redaction states and transaction dependencies. The paper proposes a novel cross-chain state mapping method with rollback rules, as well as customized block structures and verification algorithms, to address the aforementioned issues. Proof-of-concept experiments are conducted to demonstrate the feasibility of the proposed framework.

Sugarcane is a water-intensive crop that plants in a large region of Southern China. It is important to estimate sugarcane yield under various soil water conditions during sugar production. A field experiment of 39 plots under five irrigation levels was implemented. Extensive unmanned aerial vehicle (UAV) measurements (including RGB and multispectral images) and ground observations (including soil water content (SWC), leaf area index (LAI), and meteorological data) have been collected. The objective of this study was to improve sugarcane yield estimation by assimilating UAV-derived LAI and ground measured SWC data into SWAP-WOFOST-Sugarcane crop model via different data assimilation strategies. Results showed that ensemble Kalman filter (EnKF) method produces the most satisfactory estimation than forcing and calibration method. Ground SWC measurement alone is not sufficient to guarantee the estimation accuracy, while joint utilization of UAV-derived LAI measurement is necessary. This study demonstrated the benefit of assimilating UAV-derived data and provided methodological reference of fusing various data into soil-water-atmosphere-plant system.