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.     

Three Potential Elements of Developing Nerve Guidance Conduit for Peripheral Nerve Regeneration

Autograft replaced by a nerve guidance conduit (NGC) is challenging in peripheral nerve injury because current NGC is still limited by precise conductivity and excellent biocompatibility in vivo, which influences the peripheral nerve repair even for a long lesion gap repair. Several particular elements have the potential function for nerve conductivity acceleration based on the traditional three factors of neural tissue engineering. The review aims to address three questions: 1) What is the superior factor for nerve conduction in the application? 2) How can a more conductive regenerative scaffold be constructed in vivo? 3) What is the next step in nerve regeneration for NGC? The bibliometrics analysis of NGC-related references is adopted to acquire that the conductive material, manufacturing technology of neural scaffold, and electrical stimulation (ES) play essential roles in the acceleration of nerve conduction. This review visually analyses the research status and summarizes the main types of conductive materials, the manufacturing technologies of neural scaffolds, and the characteristics of ES. The viewpoints and outlook of developing NGC are also discussed in this review. The proposed three elements are expected to improve the nerve conduction of NGC in vivo and even address the dilemma of long-distance peripheral nerve injury.     

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.     

A Low-Complexity Precoding Scheme for Downlink Massive MU-MIMO Systems with Low-Resolution DACs

In this paper, we investigate the problem of downlink precoding for the narrowband massive multi-user multiple-input multiple-output (MU-MIMO) system with low-resolution digital-to-analog converters (DACs). We introduce a low-complexity precoding scheme based on the alternating direction method of multipliers (ADMM) framework in this work. An efficient gradient descent (GD) algorithm with adaptive step-size determination mechanism (ASGD) is proposed to alleviate the computational complexity bottleneck of the inherent matrix inversion. Numerical results demonstrate that the ASGD precoder achieves an attractive trade-off between the performance and computational complexity compared with other counterparts.

     

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.     

Adaptive Clustered Federated Learning for Heterogeneous Data in Edge Computing

Mobile Networks and Applications - Although federated learning has been widely used in collaborative training of machine learning models, its practical uses are still challenged by heterogeneous...     

Low-rank tensor completion via combined Tucker and Tensor Train for color image recovery

Applied Intelligence - In recent years, low-rank tensor completion has been widely used in color image recovery. Tensor Train (TT), as a balanced tensor rank minimization method, has achieved good...     

Transformation of monolayer MoS<Subscript>2</Subscript> into multiphasic MoTe<Subscript>2</Subscript>: Chalcogen atom-exchange synthesis route

Molybdenum ditelluride (MoTe2),which is an important transition-metal dichalcogenide,has attracted considerable interest owing to its unique properties,such as its small bandgap and large Seebeck coefficient.However,the batch production of monolayer MoTe2 has been rarely reported.In this study,we demonstrate the synthesis of large-domain (edge length exceeding 30 μm),monolayer MoTe2 from chemical vapor deposition-grown monolayer MoS2 using a chalcogen atom-exchange synthesis route.An in-depth investigation of the tellurization process reveals that the substitution of S atoms by Te is prevalently initiated at the edges and grain boundaries of the monolayer MoS2,which differs from the homogeneous selenization of MoS2 flakes with the formation of alloyed Mo-S-Se hybrids.Moreover,we detect a large compressive strain (approximately-10％) in the transformed MoTe2 lattice,which possibly drives the phase transition from 2H to 1T'at the reaction temperature of 500 ℃.This phase change is substantiated by experimental facts and first-principles calculations.This work introduces a novel route for the templated synthesis of two-dimensional layered materials through atom substitutional chemistry and provides a new pathway for engineering the strain and thus the intriguing physics and chemistry.