Privacy Preserving distributed smart grid system based on Hyperledger Fabric and Wireguard

Smart grid has drawn a lot of attention and investment in recent years, which not only helps the modern generation and distribution of traditional power but also highly widens the application of renewable energy sources. However, the main challenges in the application of smart grid are 1. the privacy preservation of users' information and 2. the trustful transmission channel among peers. In order to solve these problems, VPN and blockchain can be considered since they have some features perfectly suitable for these situations. In this paper, we propose a smart grid system based on WireGuard and Hyperledger Fabric to solve the problems mentioned above. And we also implement the whole system and give a view by web application. What's more, all the functionalities are displayed and tested, including building a smart device simulator, deploying data visualization and making some performance evaluations about transactions and WireGuard communication. Experiment results show that the introduction of WireGuard into network infrastructure does not cause too much loss of bandwidth and delay, but it ensures a certain degree of communication security. And Fabric provides the consistency and traceability of transactions in smart grid system.     

Femtosecond Laser 4D Printing of Light-Driven Intelligent Micromachines

Intelligent micromachines that respond to external light stimuli have a broad range of potential applications, such as microbots, biomedicine, and adaptive optics. However, artificial light-driven intelligent micromachines with a low actuation threshold, rapid responsiveness, and designable and precise 3D transformation capability remain unachievable to date. Here, a single-material and one-step 4D printing strategy are proposed to enable the nanomanufacturing of agile and low-threshold light-driven 3D micromachines with programmable shape-morphing characteristics. The as-developed carbon nanotube-doped composite hydrogel simultaneously enhanced the light absorption, thermal conductivity, and mechanical modulus of the crosslinked network, thus significantly increasing the light sensitivity and response speed of micromachines. Moreover, the structural design and assembly of asymmetric microscale mechanical metamaterial unit cells enable the highly efficient additive nanomanufacturing of 3D shape-morphable micromachines with large dynamic modulation and spatiotemporal controllability. Using this strategy, the world's smallest artificial beating heart with programmable light-stimulus responsiveness for the cardiac cycle is successfully printed. This 4D printing method paves the way for the construction of multifunctional intelligent micromachines for bionics, drug delivery, integrated microsystems, and other fields.     

Relaxation-Induced Significant Room-Temperature Dielectric Pulsing Effects

Thermo-responsive dielectric materials are in urgent demand owing to the rapid development of smart electronic/electrical systems. Although different types and structures of thermally responsive dielectric materials have been continuously reported, their dielectric response behaviors all originate from thermodynamic phase transitions. Herein, it is demonstrated that structural relaxation in poly(vinylidene fluoride) (PVDF), a non-thermodynamic phase transition, can induce a significant thermal dielectric pulse at room temperature. The dielectric pulse strength of up to 6.3 × 10⁵ at 20 Hz, with a dielectric pulsing temperature of 24 °C, is achieved from polyethylene glycol (PEG)-PVDF coaxial nanofibrous films (PVDF@PEG), fabricated via a continuous blow spinning method. Moreover, the films exhibit excellent flexibility, adjustable strength and toughness, switchable hydrophilicity/hydrophobicity, and effective thermal management capability. The relaxation-induced dielectric pulsing effect, outstanding multifunctionality, and simple preparation combine to promote further scalability and prospects of PVDF@PEG. In particular, the work contributes to the discovery of the relaxation-induced dielectric response mechanism, which provides a new strategy for the generation of thermo-responsive dielectric materials.     

Dispersion-Enabled Symmetry Switching of Photonic Angular-Momentum Coupling

Photonic spin-orbit interactions describe the interactions between spin angular momentum and orbital angular momentum of photons, which play essential roles in subwavelength optics. However, the influence of frequency dispersion on photonic angular-momentum coupling is rarely studied. Here, by elaborately designing the contribution of the geometric phase and waveguide propagation phase, the dispersion-enabled symmetry switching of photonic angular-momentum coupling is experimentally demonstrated. This notion may induce many exotic phenomena and be found in enormous applications, such as the spin-Hall effect, optical calculation, and wavelength division multiplexing systems. As a proof-of-concept demonstration, two metadevices, a multi-channel vectorial vortex beam generator and a phase-only hologram, are applied to experimentally display optical double convolution, which may offer additional degrees of freedom to accelerate computing and a miniaturization configuration for optical convolution without collimation operation. These results may provide a new opportunity for complex vector optical field manipulation and calculation, optical information coding, light-matter interaction manipulation, and optical communication.     

A Boronate Ester Driven Rechargeable Antibacterial Membrane for Fast Molecular Sieving

Membrane decorated with biocides is an effective way to suppress biofilm growth. However, their immediate biocidal effect usually suffers from a significant decline due to the irreversible consumption of the biocides. Here, a smart nanofiltration membrane is reported with rechargeable antibacterial capability that is fabricated by a facile interfacial polymerization via 3-aminophenylboronic acid and trimesoyl chloride on a polysulfone substrate. Biocides bearing diol groups can be grafted onto the membrane surface under neutral/alkaline condition and then released from the surface under acidic environment, due to the pH-responsive feature of boronate ester complexes. The resultant membrane exhibits integrated properties of fast bacterial inactivating efficiency, rechargeable antibacterial capability, and impressive stability. In addition, the achieved membrane shows remarkable separation efficiency to dye/monovalent salt system. The successful fabrication of the membrane with rechargeable anti-bacterial property provides new insights into the development of pH-responsive and sustainable antibacterial membranes.     

Unveiling the Synergy of Interfacial Contact and Defects in α-Fe2O3 for Enhanced Photo-Electrochemical Water Splitting

Photo-electrochemical (PEC) water splitting is a promising method for converting solar energy into clean energy, but the mechanism of improving PEC efficiency through the interfacial contact and defect strategy remains highly controversial. Herein, reduced graphene oxide (rGO) and oxygen vacancies are introduced into α-Fe₂O₃ nanorod (NR) arrays using a simple spin-coating method and acid treatment. The resultant oxygen vacancy–α-Fe₂O₃/rGO-integrated system exhibits a higher photocurrent, four times than the pristine α-Fe₂O₃. It is well evidenced that the electronic interface interaction between α-Fe₂O₃ and rGO is boosted with the oxygen vacancies, facilitating electron transfer from α-Fe₂O₃ to rGO. Moreover, the oxygen vacancies not only create interband states in α-Fe₂O₃ that can trap photogenerated holes and thus facilitate charge separation but significantly also strengthen the adsorption of oxidative intermediates and reduce the energy barrier of rate-determining step during oxygen evolution reaction (OER). This study demonstrates an rGO–oxygen vacancy synergistic interfacial contact and defect modification approach to design semiconducting photocatalysts for high-efficiency solar energy capture and conversion. The generated principle is expected to be extendable to another material system.     

Functional Materials for Memristor-Based Reservoir Computing: Dynamics and Applications

The booming development of artificial intelligence (AI) requires faster physical processing units as well as more efficient algorithms. Recently, reservoir computing (RC) has emerged as an alternative brain-inspired framework for fast learning with low training cost, since only the weights associated with the output layers should be trained. Physical RC becomes one of the leading paradigms for computation using high-dimensional, nonlinear, dynamic substrates. Among them, memristor appears to be a simple, adaptable, and efficient framework for constructing physical RC since they exhibit nonlinear features and memory behavior, while memristor-implemented artificial neural networks display increasing popularity towards neuromorphic computing. In this review, the memristor-implemented RC systems from the following aspects: architectures, materials, and applications are summarized. It starts with an introduction to the RC structures that can be simulated with memristor blocks. Specific interest then focuses on the dynamic memory behaviors of memristors based on various material systems, optimizing the understanding of the relationship between the relaxation behaviors and materials, which provides guidance and references for building RC systems coped with on-demand application scenarios. Furthermore, recent advances in the application of memristor-based physical RC systems are surveyed. In the end, the further prospects of memristor-implemented RC system in a material view are envisaged.     

Face anti-spoofing based on multi-modal and multi-scale features fusion

Face anti-spoofing is used to assist face recognition system to judge whether the detected face is real face or fake face. In the traditional face anti-spoofing methods, features extracted by hand are used to describe the difference between living face and fraudulent face. But these handmade features do not apply to different variations in an unconstrained environment. The convolutional neural network (CNN) for face deceptions achieves considerable results. However, most existing neural network-based methods simply use neural networks to extract single-scale features from single-modal data, while ignoring multi-scale and multi-modal information. To address this problem, a novel face anti-spoofing method based on multi-modal and multi-scale features fusion ( MMFF) is proposed. Specifically, first residual network ( Resnet )-34 is adopted to extract features of different scales from each modality, then these features of different scales are fused by feature pyramid network (FPN), finally squeeze-and-excitation fusion ( SEF) module and self-attention network ( SAN) are combined to fuse features from different modalities for classification. Experiments on the CASIA-SURF dataset show that the new method based on MMFF achieves better performance compared with most existing methods.     

The integrable hierarchy constructed from a pair of KdV-type hierarchies and its associatedW algebra

For any two arbitrary positive integers n and m, using them ^th KdV hierarchy and the (n+m)^th KdV hierarchy as building blocks, we are able to construct another integrable hierarchy (referred to as the (n, m)^th KdV hierarchy). TheW-algebra associated to the second Hamiltonian structure of the (n, m)^th KdV hierarchy (calledW(n, m) algebra) is isomorphic via a Miura map to the direct sum of aW _m -algebra, aW _n+m -algebra and an additionalU(1) current algebra. In turn, from the latter, we can always construct a representation of aW -algebra.