Multi-objective routing algorithms for low-earth orbit satellite network

The low-earth orbit (LEO) satellite network, composed of a large number of satellite nodes, is a hot research topic at present. Due to the characteristics of the large-scale LEO satellite network, such as many satellite nodes, short orbit period, large dynamic change of topology, and unstable link-state, its communication quality of service (QoS) requirements are difficult to meet. Aiming at this problem, various factors that may affect data transmission are first analyzed. The network link selection problem is modeled as a multi-constraint optimization decision problem, a routing mathematical model based on linear programming (LP) is designed, and its solution is solved. Aiming at the problem of limited onboard computing resources, a multi-object optimization Dijkstra algorithm (MOODA) is designed. The MOODA finds the optimal path according to the comprehensive performance of the link. It solves the problems of poor comprehensive QoS performance and the low degree of load balancing of the paths found by the Dijkstra algorithm. The simulation results show that the paths found by the two algorithms have good QoS, robustness, and load balancing performance.     

2D MoN1.2-rGO Stacked Heterostructures Enabled Water State Modification for Highly Efficient Interfacial Solar Evaporation

Improving interfacial solar evaporation performance is crucial for the practical application of this technology in solar-driven seawater desalination. Lowering evaporation enthalpy is one of the most promising and effective strategies to significantly improve solar evaporation rate. In this study, a new pathway to lower vaporization enthalpy by introducing heterogeneous interactions between hydrophilic hybrid materials and water molecules is developed. 2D MoN_1.2 nanosheets are synthesized and integrated with rGO nanosheets to form stacked MoN_1.2-rGO heterostructures with massive junction interfaces for interfacial solar evaporation. Molecular dynamics simulation confirms that atomic thick 2D MoN_1.2 and rGO in the MoN_1.2-rGO heterostructures simultaneously interact with water molecules, while the interactions are remarkably different. These heterogeneous interactions cause an imbalanced water state, which easily breaks the hydrogen bonds between water molecules, leading to dramatically lowered vaporization enthalpy and improved solar evaporation rate (2.6 kg m⁻² h⁻¹). This study provides a promising strategy for designing 2D-2D heterostructures to regulate evaporation enthalpy to improve solar evaporate rate for clean water production.     

Nano-Engineered Carbon Fibre-Based Piezoelectric Smart Composites for Energy Harvesting and Self-Powered Sensing

The integration of piezoelectric materials onto carbon fiber (CF) can add energy harvesting and self-power sensing capabilities enabling great potential for “Internet of Things” (IoT) applications in motion tracking, environmental sensing, and personal portable electronics. Herein, a CF-based smart composite is developed by integrating piezoelectric poly(3,4-ethylenedioxythiophene) (PEDOT)/CuSCN-coated ZnO nanorods onto the CF surfaces with no detrimental effect on the mechanical properties of the composite, forming composites using two different polymer matrices: highly flexible polydimethylsiloxane (PDMS) and more rigid epoxy. The PDMS-coated piezoelectric smart composite can serve as an energy harvester and a self-powered sensor for detecting variations in impact acceleration with increasing output voltage from 1.4 to 7.6 V under impact acceleration from 0.1 to 0.4 m s⁻². Using epoxy as the matrix for a CF-reinforced plastic (CFRP) device with sensing and detection functions produces a voltage varying from 0.27 to 3.53 V when impacted at acceleration from 0.1 to 0.4 m s⁻², with a lower output compared to the PDMS-coated device attributed to the greater stiffness of the matrix. Finally, spatially sensitive detection is demonstrated by positioning two piezoelectric structures at different locations, which can identify the location as well as the level of the impacting force from the fabricated device.     

Creating Atomic Ordering in Electrocatalysis

Catalysis always proceeds in a chaotic fashion. Therefore, identifying the working principles of heterogeneous catalysts is a challenging task. Creating atomic order in heterogeneous catalysts simplifies this task and also offers new opportunities for rationally designing active sites to manipulate catalytic performance. The recent rapid advances in heterogeneous electrocatalysis have led to exciting progress in the construction of atomically ordered materials. Here, the latest progress in electrocatalysts with the periodic atomic arrangement, including intermetallic compounds with long-range order and metal atom-array catalysts with short-range order is summarized. The synthesis principles and the intriguing physical and chemical properties of these electrocatalysts are discussed. Furthermore, the compelling prospects of atomically ordered catalysts in the frontier of catalyst research are outlined.     

Tough Transient Ionic Junctions Printed with Ionic Microgels

Emerging soft ionotronics better match the human body mechanically and electrically compared to conventional rigid electronics. They hold great potential for human-machine interfaces, wearable and implantable devices, and soft machines. Among various ionotronic devices, ionic junctions play critical roles in rectifying currents as electrical p–n junctions. Existing ionic junctions, however, are limited in electrical and mechanical performance, and are difficult to fabricate and degrade. Herein, the design, fabrication, and characterization of tough transient ionic junctions fabricated via 3D ionic microgel printing is reported. The 3D printing method demonstrates excellent printability and allows one to fabricate ionic junctions of various configurations with high fidelity. By combining ionic microgels, degradable networks, and highly charged biopolymers, the ionic junctions feature high stretchability (stretch limit 27), high fracture energy (>1000 Jm⁻²), excellent electrical performance (current rectification ratio >100), and transient stability (degrade in 1 week). A variety of ionotronic devices, including ionic diodes, ionic bipolar junction transistors, ionic full-wave rectifiers, and ionic touchpads are further demonstrated. This study merges ionotronics, 3D printing, and degradable hydrogels, and will motivate the future development of high-performance transient ionotronics.     

Surface Oxygen Coordination of Hydrogen Bond Chemistry for Aqueous Ammonium Ion Hybrid Supercapacitor

Aqueous ammonium ion hybrid supercapacitor (A-HSC) combines the charge storage mechanisms of surface adsorption and bulk intercalation, making it a low-cost, safe, and sustainable energy storage candidate. However, its development is hindered by the low capacity and unclear charge storage fundamentals. Here, the strategy of phosphate ion-assisted surface functionalization is used to increase the ammonium ion storage capacity of an α-MoO₃ electrode. Moreover, the understanding of charge storage mechanisms via structural characterization, electrochemical analysis, and theoretical calculation is advanced. It is shown that NH₄⁺ intercalation into layered α-MoO₃ is not dominant in the A-HSC system; rather, the charge storage mainly depends on the adsorption energy of surface “O” to NH₄⁺. It is further revealed that the hydrogen bond chemistry of the coordination between “O” of surface phosphate ion and NH₄⁺ is the reason for the capacity increase of MoO₃. This study not only advances the basic understanding of rechargeable aqueous A-HSC but also demonstrates the promising future of surface engineering strategies for energy storage devices.     

Highly Conductive MXene/PEDOT:PSS-Integrated Poly(N-Isopropylacrylamide) Hydrogels for Bioinspired Somatosensory Soft Actuators

Sophisticated sensing and actuation capabilities of many living organisms in nature have inspired scientists to develop biomimetic somatosensory soft robots. Herein, the design and fabrication of homogeneous and highly conductive hydrogels for bioinspired somatosensory soft actuators are reported. The conductive hydrogels are synthesized by in situ copolymerization of conductive surface-functionalized MXene/Poly(3,4-ethylenedioxythiophene)/poly(styrenesulfonate) ink with thermoresponsive poly(N-isopropylacrylamide) hydrogels. The resulting hydrogels are found to exhibit high conductivity (11.76 S m⁻¹), strain sensitivity (GF of 9.93), broad working strain range (≈560% strain), and high stability after over 300 loading–unloading cycles at 100% strain. Importantly, shape-programmable somatosensory hydrogel actuators with rapid response, light-driven remote control, and self-sensing capability are developed by chemically integrating the conductive hydrogels with a structurally colored polymer. As the proof-of-concept illustration, structurally colored hydrogel actuators are applied for devising light-driven programmable shape-morphing of an artificial octopus, an artificial fish, and a soft gripper that can simultaneously monitor their own motions via real-time resistance variation. This work is expected to offer new insights into the design of advanced somatosensory materials with self-sensing and actuation capabilities, and pave an avenue for the development of soft-matter-based self-regulatory intelligence via built-in feedback control that is of paramount significance for intelligent soft robotics and automated machines.     

基于差和共阵的新型高自由度互质阵

针对均匀线列阵自由度(DOF)受限于阵元数的问题,该文提出一种基于差和共阵的新型互质阵,称为放置互质阵(DCA),其借助由接收信号的时域和空域信息组合成的共轭增广矩阵得到等价的差和共阵来进行波达方向(DOA)估计.DCA将广义互质阵放置在与原点处单阵元相隔一定距离的位置,实现了和共阵与差共阵的阵元位置互补,从而最大限度上利用和共阵带来的自由度增幅.该文给出了DCA阵元位置和放置距离的闭式表达,随后分别对DCA的差共阵及和共阵的连续阵元及孔洞位置进行了理论分析,同时给出了两者间的关系,说明了DCA的高自由度特性.多个仿真实验验证了所提阵型DOA估计的有效性.     

Response map evaluation for RGBT tracking

Neural Computing and Applications - In recent years, RGB and thermal sensors are widely used. There is complementary information from these two types of sensors. A fundamental task which arises in...     

基于因果干预与不变性的卷积预训练模型优化研究

基于卷积神经网络（CNN）的深度模型在图像识别与分类领域应用广泛,但在全局特征控制、概念层次特征不变性提取和变量之间的因果关系确定方面仍存在不足,使得深度模型缺乏灵活性、适应性及泛化性。基于因果干预和不变性,提出一种基于CNN深度模型的定向修剪和网络结构优化方法。通过对模型输入进行基于不变性的干预调制,根据生成的调制图片序列分析预训练网络卷积子结构的输出分布,筛选和定向修剪噪声敏感子结构。构建基于类间区分度的目标函数,借助经济学领域中的资本资产定价模型构建网络的层间连接,生成在单分类任务下能增大类间区分度的网络拓扑结构,逐层优化构建概念层次的稳定特征。在ImageNet-2012数据集上的实验结果表明,优化后的深度模型相比于ResNet50基线预训练模型的分类准确率约提升了5个百分点,并大幅降低了训练集规模。