Side-by-side bicomponent fibers have a spring-like three-dimensional spiral crimp structure and are widely used in elastic fabric. The difference in thermal shrinkage between different polymers can produce an unbalanced stress during the cooling process, and this unbalanced stress can be exploited to prepare naturally crimped fibers by spinning design. In this work, different types of polyamides(PAs) were selected for fabrication of the PA-based side-by-side bicomponent elastic fibers using melt spinning, and the structure development and performance of such bicomponent elastic fibers were studied. Meanwhile, thermoplastic PA elastomer(TPAE) with intrinsic elasticity was also used as one of the comparative materials. The block structure of the PA segment and the polyether segment in the TPAE molecule is the key to providing thermal shrinkage differences and forming a good interface structure. As a result, the crimp ratio of PA6/TPAE bicomponent elastic fiber is 7.23%, which is better than that of the currently commercialized T400 fiber(6.72%). The excellent crimp performance of PA6/TPAE bicomponent elastic fibers comes from the asymmetric distribution of the stress along the radial direction of the fibers during the cooling process, which is caused by the difference in thermal shrinkage between PA6 and TPAE. In addition, the crimp formability of the PA-based bicomponent elastic fibers could be improved by expanding the shrinkage stress through wet-heat treatment. The crimp ratio of PA6/TPAE bicomponent elastic fibers reaches the maximum(33.08%) after treatment at 100 ℃. At the same time, the fabric made of PA6/TPAE bicomponent elastic fibers has the excellent air and water vapor permeability, with an air permeability of 272.76 mm/s and a water vapor transmission rate of 406.71 g/(m²·h).

Nanofibers based on cellulose are highly desired due to their remarkable biocompatibility and attractive physical and biochemical characteristics. The current research describes a simple electrospinning process and the nano-materials therefrom, utilizing the classical cellulose-cuprammonium solution without the more exotic chemical solvent combinations. Furthermore, without the use of organic solvents, a binary polymer system with the addition of polyethylene oxide(PEO) is introduced to improve the robustness of the electrospinning and the properties of the final material. The impacts of the cellulose source, cellulose mass fraction and PEO formulation on spinnability, fiber morphology and mechanical properties are investigated. Nanofibers with diameters ranging from 130 nm to 382 nm are successfully fabricated. The presence of copper in the fabricated material is confirmed by using the X-ray photoelectron spectroscopy(XPS) analysis. The cuprammonium process significantly changes the original crystalline structure of cellulose Ⅰ into cellulose Ⅲ within the nanofiber morphology. The nanofibrous membranes also demonstrate notable antibacterial characteristics for Staphylococcus aureus(S. aureus) and Escherichia coli(E. coli).

A glass fiber(GF)/polydicyclopentadiene(PDCPD) composite impact simulation model was established based on LS-DYNA(the finite element analysis software peroduced by Livermore Software Technology Corporation) simulation. An optimal ply thickness of the composite GF/PDCPD was determined as 3.0 mm, and thus the final intrusion depth was controlled within 8.8 mm, meeting the performance standards for battery electric vehicle protection materials. A comparative analysis of failure modes during impacts was conducted for composites GF/PDCPD, GF/polypropylene(PP) and GF/polyamide(PA). The results indicated that GF/PDCPD exhibited compressive failure modes and ductile fractures, resulting in smaller damage areas. In contrast, GF/PP and GF/PA showed fiber fracture failures, leading to larger damage areas. The molding process and impact resistance of GF/PDCPD were investigated. By comparing the impact performance of GF/PDCPD with that of GF/PP and GF/PA, it was concluded that GF/PDCPD demonstrated superior performance and better alignment with the performance standards of battery electric vehicle protective materials. The predictability and accuracy of LS-DYNA simulation was verified, providing a theoretical foundation for further in-depth research.

Dyeing wastewater has the problems of complex composition, deep color and difficulty in degradation, which seriously threaten the ecological environment. This study investigated the Ni²⁺/peroxymonosulfate(PMS)/MXene system for efficient degradation of the dyeing wastewater with lower metal consumption. The reactive red 24(RR24) simulated dyeing wastewater was used as the research object. The influences of mass concentrations of PMS, Ni²⁺, MXene and RR24, and initial pH values on RR24 degradation were explored. The contribution of free radicals in the degradation of dyes was investigated by free radical quench experiments. The results showed that the degradation percentage of RR24 was as high as 96.62% using a mixture of 7.5 g/L PMS, 100 mg/L Ni²⁺ and 210 mg/L MXene at 25℃ for 60 min. Under neutral conditions, compared with the system without Ti_3C₂ MXene, the degradation percentage of RR24 increased by 2.04 times. In this system, the ·OH radical played a dominant role. When the dyeing wastewater was treated by using the Ni²⁺/PMS/MXene system, the inorganic salts significantly altered the degradation rate of the dyeing wastewater, but only slightly affected the final degradation percentage.

Antifouling textiles have been a hot research field in the last ten years. But lately, the European Union(EU) is expected to ban the production of C6 fluorinated water repellent, oil repellent and antifouling agents from 2025, achieving complete fluoride-free antifouling measures. In the context of the current policy regulations on fluorine-free treatment in the waterproof, oil repellent and antifouling finishing field, this paper conducts a literature survey and comprehensive understanding of the current research status and trend in fluorine-free antifouling finishing. CiteSpace and Carrot2 are used to conduct a literature review of the latest papers in two databases, Web of Science(WoS) and China National Knowledge Infrastructure(CNKI). Firstly, the theoretical evolution and technical characteristics of textiles antifouling finishing technology are systematically discussed, and the technological differences of antifouling finishing textiles for different demands are compared. Secondly, the three main stages of the development of textile antifouling finishing technology and its corresponding finishing methods are summarized. Finally, three paths for the future development of fluorine-free antifouling finishing are prospected: construction of low surface energy, construction of uniform rough surface and low surface energy, and construction of multistage rough surface with low surface energy. To facilitate the transition of the industrial sector to fluorine-free, regulatory restrictions could be strengthened by enhancing the sampling inspection of apparel fabrics, detecting fluorine in wastewater discharges and restricting the production of fluorine-containing finishing agents by manufacturers.

With the advancement of technology, the collaboration of multiple unmanned aerial vehicles(multi-UAVs) is a general trend, both in military and civilian domains. Path planning is a crucial step for multi-UAV mission execution, it is a nonlinear problem with constraints. Traditional optimization algorithms have difficulty in finding the optimal solution that minimizes the cost function under various constraints. At the same time, robustness should be taken into account to ensure the reliable and safe operation of the UAVs. In this paper, a self-adaptive sparrow search algorithm(SSA), denoted as DRSSA, is presented. During optimization, a dynamic population strategy is used to allocate the searching effort between exploration and exploitation; a t-distribution perturbation coefficient is proposed to adaptively adjust the exploration range; a random learning strategy is used to help the algorithm from falling into the vicinity of the origin and local optimums. The convergence of DRSSA is tested by 29 test functions from the Institute of Electrical and Electronics Engineers(IEEE) Congress on Evolutionary Computation(CEC) 2017 benchmark suite. Furthermore, a stochastic optimization strategy is introduced to enhance safety in the path by accounting for potential perturbations. Two sets of simulation experiments on multi-UAV path planning in three-dimensional environments demonstrate that the algorithm exhibits strong optimization capabilities and robustness in dealing with uncertain situations.

With the growth of maritime activities,the number of computationally complex applications is growing exponentially.Mobile edge computing(MEC) is widely recognized as a viable option to address the substantial need for wireless communications and compute-intensive operations in maritime environments.To reduce the processing load and meet the demands of mobile terminals for high bandwidth,low latency and multiple access,MEC systems with unmanned aerial vehicles(UAVs) have been proposed and extensively explored.In this paper,a maritime MEC network that employs a top-UAV(T-UAV) for task offloading supported by digital twin(DT) is considered.To explore the task offloading strategy employed by the edge server,the flight trajectory and resource allocation strategy of the T-UAV is studied in detail.The objective of this study is to minimize latency costs while ensuring that the energy of the T-UAV is sufficient to fulfill services.In order to accomplish this objective,the joint optimization problem is described as a Markov decision process(MDP).To overcome this problem,the priority-based reinforcement learning(RL) algorithm for computation offloading and trajectory planning(PRL-COTP) is developed.The simulation results demonstrate that the proposed approach can significantly reduce the overall cost of the system in comparison to other benchmarks.

Extracting building contours from aerial images is a fundamental task in remote sensing. Current building extraction methods cannot accurately extract building contour information and have errors in extracting small-scale buildings. This paper introduces a novel dense feature iterative(DFI) fusion network, denoted as DFINet, for extracting building contours. The network uses a DFI decoder to fuse semantic information at different scales and learns the building contour knowledge, producing the last features through iterative fusion. The dense feature fusion(DFF) module combines features at multiple scales. We employ the contour reconstruction(CR) module to access the final predictions. Extensive experiments validate the effectiveness of the DFINet on two different remote sensing datasets, INRIA aerial image dataset and Wuhan University(WHU) building dataset. On the INRIA aerial image dataset, our method achieves the highest intersection over union(IoU), overall accuracy(OA) and F1 scores compared to other state-of-the-art methods.

The time-varying difference-in-difference model is used to identify the impact of payment technology on residents' consumption, and the moderation effect analysis method is used to identify its mechanism. It is found that payment technology promotes consumption capacity expansion and quality improvement(CEQI) through three pathways of alleviating liquidity constraints, reducing transaction costs and weakening the payment of pain. The parallel and serial mechanisms of the three are further explored. The effect of payment technology on the CEQI of residents' consumption shows obvious heterogeneity due to differences in urban and rural household registration and financial literacy. Based on the empirical research results and the national conditions of China, targeted policy recommendations are proposed from the demand side, the supply side and the technological side.

Circulating currents in a microgrid increase the power loss of the microgrid, reduce the operational efficiency, as well as affect the power quality of the microgrid. The existing literature is seldom concerned with methods to suppress the loop currents using fuzzy logic control. In this paper, a method based on fuzzy control of droop coefficients is proposed to suppress the circulating currents inside the microgrid. The method combines fuzzy control with droop control and can achieve the effect of suppressing the circulating currents by adaptively adjusting the droop coefficients to make the power distribution between each subgrid more balanced. To verify the proposed method, simulation is carried out in Matlab/Simulink environment, and the simulation results show that the proposed method is significantly better than the traditional proportional-integral control method. The circulating currents reduce from about 10 A to several nanoamperes, the bus voltage and frequency drops are significantly improved, and the total harmonic distortion rate of the output voltage reduces from 4.66% to 1.06%. In addition, the method used in this paper can be extended to be applied in multiple inverters connected in parallel, and the simulation results show that the method has a good effect on the suppression of circulating currents among multiple inverters.

Mobile edge computing(MEC) has a vital role in various delay-sensitive applications. With the increasing popularity of low-computing-capability Internet of Things(IoT) devices in industry 4.0 technology, MEC also facilitates wireless power transfer, enhancing efficiency and sustainability for these devices. The most related studies concerning the computation rate in MEC are based on the coordinate descent method, the alternating direction method of multipliers(ADMMs) and Lyapunov optimization. Nevertheless, these studies do not consider the buffer queue size. This research work concerns the computation rate maximization for wireless-powered and multiple-user MEC systems, specifically focusing on the computation rate of end devices and managing the task buffer queue before computation at the terminal devices. A deep reinforcement learning(RL)-based task offloading algorithm is proposed to maximize the computation rate of end devices and minimizes the buffer queue size at the terminal devices. The central idea of this work is to explore the best optimal mode selection for IoT devices connected to the MEC system. The proposed algorithm optimizes computation delay by maximizing the computation rate of end devices and minimizing the buffer queue size before computation at the terminal devices. Then, the current study presents a deep RL-based task offloading algorithm to solve such a mixed-integer and non-convex optimization problem, aiming to get a better trade-off between the buffer queue size and the computation rate. The extensive simulation results reveal that the presented algorithm is much more efficient than the existing work to maintain a small buffer queue for terminal devices while simultaneously achieving a high-level computation rate.