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低空新型基础设施是低空经济高质量发展的核心支撑，其价值闭环构建对促进产业可持续发展具有重要意义。本文分析了EPC+O、投建营一体化、政企合作、特许经营和LATOD等商业模式创新的价值闭环途径，探讨空域管理改革、数据要素市场化配置和产业基金等政策支持体系如何降低运营成本、提升价值创造效率，研究通信导航监视反制、“一塔一城”和设施/人员复用等技术路径如何支撑价值闭环实现。研究表明，低空新型基础设施的价值闭环构建需要商业模式创新、政策支持机制和技术应用路径的协同演进，通过“公益底座+市场服务”的组合模式，构建“建设-运营-价值创造-再投资”的良性循环。

A prominent phenomenon in contemporary condensed matter physics is Anderson localization -- suppression of wave propagation in disordered systems as a result of interference effects. Despite being observed with various types of waves over the years, all prior attempts to reach Anderson localization of light in three-dimensional systems have been hampered by experimental artifacts. Here, we report an unambiguous experimental proof of three-dimensional Anderson localization of microwaves in disordered metal aggregates. By studying samples with different metal volume fractions, we show a clear difference between diffusive and localized behaviors, and the latter is confirmed by a scaling analysis of transmitted beam width in excellent agreement with theoretical and numerical results. Our demonstration opens avenues for both fundamental studies and practical applications of this extraordinary phenomenon.

We investigate posterior sampling strategies for cosmological parameter inference using fully differentiable neural-network likelihood emulators, which provide both rapid likelihood evaluations and automatic differentiation. We compare Metropolis--Hastings (MH), the Metropolis-Adjusted Langevin Algorithm (MALA), Hamiltonian Monte Carlo (HMC), the No U-Turn Sampler (NUTS), and Affine Invariant Ensemble Sampling (AIES) using likelihood emulators constructed with the CLiENT framework. The methods are tested on emulators of both the $Λ$CDM model and a sterile-neutrino extension. While NUTS generally converges in the fewest samples, its higher computational cost reduces this advantage when performance is measured by wall time. As a result, MALA and even standard MH remain highly competitive. We further find that whitening and covariance adaptation substantially improve sampling efficiency. The TensorFlow implementations developed for this work are released as the BEST (Batched Emulator Sampling with TensorFlow) package, providing a general framework for sampling arbitrary TensorFlow likelihood functions. The package is available through PyPI as 'best-inference' and on GitHub (at https://github.com/AndreasNygaard/best-inference.git).

This study investigates the combined effects of anisotropy and auxetic mesh geometry on the performance of skin graft expansion. Finite element models of auxetic slit-based geometries were developed and subjected to 25 percent tensile strain. Skin was modelled using an anisotropic constitutive formulation. Langer's line orientations were varied relative to the load direction. Results showed anisotropy strongly influenced expansion behaviour. The effect was observed to be complex and highly dependent on mesh type. Anisotropy was observed to enhance or inhibit the auxetic expansion behaviour. In all mesh types studied, the expansion performance is lowest when Langer's lines align with the transverse direction. Greatest expansion was typically observed when Langer's lines were close to the loading direction. Isotropic models overpredicted stress relative to the anisotropic models. These findings support the use of auxetic structures for skin mesh expansion applications and show that anisotropy is an important factor in both deformation and stress prediction.

Confidential blockchains leveraging Trusted Execution Environments (TEEs) have garnered extensive attention for transaction confidentiality. In this paper, we first taxonomize two classes of attacks against confidential blockchains, i.e., execution-inference and execution-replay attacks, which exploit TEEs' long-lasting side-channel and state-continuity issues to compromise the confidentiality of existing consortium blockchains. Then, we present ODYSSEY, a confidential blockchain that efficiently mitigates these attacks. The core innovations of ODYSSEY are the following: (1) Its delegation model: clients delegate transaction execution to their designated trustees, while other participants synchronize only the execution results, which significantly reduces the attack surface while preserving confidentiality and system performance. (2) Two novel techniques to improve ODYSSEY's efficiency and security: location-aware concurrent execution and delegation failure handler. Finally, we develop a prototype of ODYSSEY on FISCO BCOS, an enterprise-grade consortium blockchain platform. We have conducted various experiments, and our evaluation results show that in a WAN environment with 3 nodes, ODYSSEY can achieve about 4k throughput while keeping latency as low as 0.4-0.5s.