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Metallic charge transport and porosity appear almost mutually exclusive. Whereas metals demand large numbers of free carriers and must have minimal impurities and lattice vibrations to avoid charge scattering, the voids in porous materials limit the carrier concentration, provide ample space for impurities, and create more charge-scattering vibrations due to the size and flexibility of the lattice. No microporous material has been conclusively shown to behave as a metal. Here, we demonstrate that single crystals of the porous metal-organic framework Ln1.5(2,3,6,7,10,11-hexaoxytriphenylene) (Ln = La, Nd) are metallic. The materials display the highest room-temperature conductivities of all porous materials, reaching values above 1,000 S/cm. Single crystals of the compounds additionally show clear temperature-deactivated charge transport, a hallmark of a metallic material. Lastly, a structural transition consistent with charge density wave ordering, present only in metals and rare in any materials, provides additional conclusive proof of the metallic nature of the materials. Our results provide an example of a metal with porosity intrinsic to its structure. We anticipate that the combination of porosity and chemical tunability that these materials possess will provide a unique handle toward controlling the unconventional states that lie within them, such as charge density waves that we observed, or perhaps superconductivity.

With the development of quantum computers, the classical public key encryption system is not capable enough to guarantee the communication security of internet of things (IoT).Because the mathematical puzzles which post-quantum encryption algorithms are based on cannot yet be broken by quantum computers, these new algorithms have good anti-quantum computing security.In particular, the lattice-based cryptography is expected to become the main technology of the next generation public key cryptosystem.However, post-quantum encryption algorithms have the disadvantages of large amount of computation and high storage space.The communication efficiency of IoT will be affected if post-quantum encryption algorithms are directly applied to the lightweight device under IoT environment.In order to better guarantee the communication security and improve the commutation efficiency of IoT, Sym-BRLWE (symmetrical binary RLWE) encryption scheme was proposed.Sym-BRLWE was improved from the existing post-quantum encryption scheme based on BRLWE (binary ringlearning with errors) problem.Specifically, Sym-BRLWE encryption algorithm met the efficiency requirements of IoT via improving the random number selection on the discrete uniform distribution and the calculation of the polynomial multiplication.Sym-BRLWE encryption algorithm achieved high efficiency and high security via adding encryption security precautions, then it is more suitable for IoT lightweight devices.From the security analysis, the proposed Sym-BRLWE encryption scheme had high security.It could theoretically resist lattice attacks, timing attacks, simple power analysis (SPA) and differential power analysis (DPA).From simulation experiments, which were carried out in a binary computing environment simulating an 8-bit micro-device, the proposed Sym-BRLWE encryption scheme has high efficiency and small key size in encryption and decryption.It could reduce the total encryption time by 30% to 40% when compared with other BRLWE-based encryption schemes with the parameter selection of the 140 bit quantum security level.

AbstractAll-pair shortest path routing within stochastic road networks is often more complicated and computationally challenging than routing in deterministic networks because uncertainties in travel time are introduced. We develop and test a GPU-enabled approach to resolve this computational challenge. This approach is based on a simulation framework that includes four steps: simulation of road networks impedances, computation of candidate paths using an all-pair shortest path algorithm, travel time estimation across candidate paths, and all-pair optimal path calculations. We developed GPU algorithms to accelerate the second and third steps, which are computationally demanding for large road networks. We used the road network of Wuhan, China as a case study. Our experiments indicate that the GPU approach leads to thousands of times in improvement of acceleration, which makes all-pair shortest path routing within stochastic road networks computationally feasible. [ABSTRACT FROM AUTHOR]