Recent Development of Jumping Motions Based on Soft Actuators

Drawing inspiration from the jumping motions of living creatures in nature, jumping robots have emerged as a promising research field over the past few decades due to great application potential in interstellar exploration, military reconnaissance, and life rescue missions. Early reviews mainly focused on jumping robots made of lightweight and rigid materials with mechanical components, concentrating on jumping control and stability. Herein, attention is paid to the jumping mechanisms of soft actuators assembled from various soft smarting materials and powered by different stimulus sources. The challenges and prospects of soft jumping actuators are also discussed. It is hoped that this review will contribute to the further development of soft jumping actuators and broaden their practical applications.     

Ferroptosis-Mediated Synergistic Therapy of Hypertrophic Scarring Based on Metal–Organic Framework Microneedle Patch

Hypertrophic scarring, an abnormal fibroproliferative wound-healing disease, has brought tremendous burden for global healthcare systems. To date, no satisfactory treatment of hypertrophic scarring is available yet. Ferroptosis, an iron-dependent form of cell death, has attracted much attention recently for the therapy of diseases featuring iron addiction. Intriguingly, myofibroblasts derived from hypertrophic scarring are found to exhibit a high iron state which appears to be sensitive to trigger ferroptosis for scarring treatment. Accordingly, in this study, a pH responsive self-assembly nanoplatform is designed by encapsulating silver nanoclusters (AgNCs) and Chinese herbal medicine trigonelline (TRG) into zeolitic imidazolate framework-8 (ZIF-8) for synergistic ferroptosis therapy against hypertrophic scarring. The fabricated AgNC/TRG/ZIF-8 composites exhibit good biocompatibility and pH responsive-degradation inside myofibroblasts. The ZIF-8 precursors can increase the generation of lipid reactive oxygen species and deplete intracellular glutathione (GSH). Also, AgNCs have the capability to consume GSH, while TRG can inhibit the activity of glutathione peroxidase. Consequently, the synergistic ferroptosis anti-scarring therapy can be effectively achieved. Furthermore, AgNC/TRG/ZIF-8-loaded microneedle patches made of gelatin methacrylate show remarkable therapeutic effect against hypertrophic scarring on a rabbit ear model. This study suggests the great potential of ferroptosis-mediated strategy for treating fibrotic skin diseases in future clinical application.     

Recent Progress in Advanced Tactile Sensing Technologies for Soft Grippers

Tactile sensing technology is crucial for soft grippers. Soft grippers equipped with intelligent tactile sensing systems based on various sensors can interact safely with the unstructured environments and obtain precise properties of objects (e.g., size and shape). It is essential to develop state-of-the-art sensing technologies for soft grippers to handle different grasping tasks. In this review, the development of tactile sensing techniques for robotic hands is first introduced. Then, the principles and structures of different types of sensors normally adopted in soft grippers, including capacitive tactile sensors, piezoresistive tactile sensors, piezoelectric tactile sensors, fiber Bragg grating (FBG) sensors, vision-based tactile sensors, triboelectric tactile sensors, and other advanced sensors developed recently are briefly presented. Furthermore, sensing modalities and methodologies for soft grippers are also described in aspects of force measurement, perception of object properties, slip detection, and fusion of perception. The application scenarios of soft grippers are also summarized based on these advanced sensing technologies. Finally, the challenges of tactile sensing technologies for soft grippers that need to be tackled are discussed and perspectives in addressing these challenges are pointed out.     

URLM: Utilising reinforcement learning to schedule subflows in MPTCP

Parallel transmission in multiple access networks using MultiPath TCP (MPTCP) greatly enhances the throughput. However, critical packet disorder is commonly observed due to traffic fluctuation and path diversity. Although several predictive scheduling algorithms have been proposed to solve this problem, they cannot accommodate prediction accuracy and real-time adaptation simultaneously in a dynamic network environment. The time overhead in modifying scheduling parameters to adapt to network changes leads to performance degradation in throughput and packet disorder. In this study, we propose a scheduling algorithm called U tilising R einforcement L earning to Schedule Subflows in M PTCP (URLM). We apply reinforcement learning to select an optimal scheduling parameter in real time, which brings significant time benefits for modifying the parameters. The simulation comparison experiments show that URLM reduces the average number of out-of-order packets and the time overhead in adapting to network changes while improving global throughput.     

Surface Growth of Boron Nitride Nanotubes through Boron Source Design

Boron nitride nanotubes (BNNTs) are promising materials due to their unique physical and chemical properties. Fabrication technologies based on gas-phase reactions reduce the control and collection efficiency of BNNTs due to reactant and product dispersion within the reaction vessel. A surface growth method that allows for controllable growth of BNNTs in certain regions using a preburied boron source is introduced. This work leverages the high solubility of boron in metals to create a boronized layer on the surface which serves as the boron source to confine the growth of BNNTs. Dense and uniform BNNTs are obtained after loading catalysts onto the boronized substrate and annealing under ammonia. Confirmatory experiments demonstrate that the boride layer provides boron for BNNTs growth. Furthermore, the patterned growth of BNNTs is realized by patterning the boronizing region, demonstrating the controllability of this method. In addition, the Ni substrate with BNNTs growth exhibits better performance in corrosion resistance and thermal conductivity than pure Ni. This study introduces an alternative strategy for the surface growth of BNNTs based on boron source design, which offers new possibilities for the controllable preparation of BNNTs for various applications.     

A Solenoid-Type Inductor Fabricated by MEMS Technique

A solenoid-type inductor for high frequency application is realized using a micro-electro-mechanical systems (MEMS) technique.In order to achieve a high inductance value and Q factor,UV-LIGA,dry etching technique,fine polishing and electroplating technique are adopted.The dimensions of the inductor are 1500μm×900μm×70μm,having 41 turns with a coil width of 20μm separated by 20μm spaces and a high aspect ratio of 3.5∶1.The maximum measured inductance of the inductor is 6.17nH with a Q factor of about 6.     

能量回收电容耦合逻辑及其综合方法

提出了一种新型能量回收电路ERCCL（能量回收电容耦合逻辑）,该电路的能耗低于传统CMOS电路及其他能量回收电路．ERCCL利用电容耦合进行逻辑求值,因此可以在一个门中低能耗地实现高扇入、高复杂度的逻辑．同时ERCCL是一种阈值逻辑．所以一个基于ERCCL的系统可以大大减少逻辑门数,从而降低系统能耗．针对ERCCL提出了一种阈值逻辑综合方法．用基准电路集MCNC做了相应的实验．与SIS的综合结果相比,该方法大约减少80％的逻辑门．     

InP/InGaAs SHBT器件自对准结构设计和工艺实现

从InP湿法腐蚀各向异性特性实验出发,利用传统的基极-发射极自对准工艺和改进的基极-发射极工艺制作了两种InP/InGaAs SHBT自对准结构,比较了两种自对准工艺对减小基极与发射极台面间距的效果,为制作高频率特性InP/InGaAs SHBT提供了工艺途径.     

三相电压型PWM变流器的线性化状态反馈解耦控制

三相电压型PWM变流器在实现能量变换的同时可以消除谐波、实现单位功率因数运行,是一个多输入多输出的非线性系统。文章建立了基于开关函数的变流器数学模型,采用非线性变换以及引入变流器交直流侧的动态功率平衡两种方案相结合,得到了线性化的变流器数学模型。在线性化数学模型的基础上,提出了电压型PWM变流器的线性化状态反馈解耦控制。仿真结果表明该控制策略能较好地实现三相电压型PWM变流器有功功率和无功功率的解耦控制,具有较好的动态特性,而且网侧电流谐波含量很低。     

Complexity reduction of signal detection by exploiting correlation characteristics of spreading sequences

In this paper, we propose an approach to reduce the multi‐user detection (MUD) complexity based on user grouping and signal replica classification by exploiting the correlation characteristics of spreading sequences in multipath fading channels. The spreading sequences are constructed from inter‐group complementary codes with a sparse and regular correlation matrix and inherit its attractive auto/cross‐correlation properties. Users are first partitioned into independent user groups according to whether or not there is interference among them, and then the replicas of user signals from the same user group are further classified into independent replica classes. The MUD is carried out within each low‐dimensional user group or replica class, respectively, reducing the MUD complexity substantially. This approach can be applied to most of the existing MUD algorithms for complexity reduction, and in this paper optimal MUD and multi‐stage MUD are exemplified. The analytical and simulated results demonstrate that this approach can reduce the MUD complexity significantly under any load conditions without performance loss. Copyright © 2009 John Wiley & Sons, Ltd.