Search Results (108)

Liquid metals (LMs), with their unique combination of high electrical conductivity and mechanical deformability, have emerged as promising materials for stretchable electronics and biointerfaces. However, the practical application of bulk LMs in wearable sensors has been hindered by processing challenges and low stability. To overcome these limitations, liquid metal particles (LMPs) encapsulated by native oxide shells have gained attention as versatile and stable fillers for stretchable and conductive composites. Recent advances have focused on the development of LM-based hybrid composites that combine LMPs with metal, carbon, or polymeric fillers. These systems offer enhanced electrical and mechanical properties and can form conductive networks without the need for additional sintering processes. They also impart composites with multiple functions such as self-healing, electromagnetic interference shielding, and recyclability. Hence, the present review summarizes the fabrication methods and functional properties of LM-based composites, with a particular focus on their applications in wearable sensing. In addition, recent developments in the use of LM composites for physical motion monitoring (e.g., strain and pressure sensing) and electrophysiological signal recording (e.g., EMG and ECG) are presented, and the key challenges and opportunities for next-generation wearable platforms are discussed. Full article

The rapid growth in large-scale distributed video conferencing, remote education, and real-time broadcasting poses significant challenges to traditional centralized streaming systems, particularly regarding scalability, cost, and reliability under high concurrency. Centralized approaches often encounter bottlenecks, increased bandwidth expenses, and diminished fault tolerance. This paper proposes a novel decentralized real-time broadcasting system employing a peer-to-peer (P2P) chain topology based on IPv6 networking and the Secure Reliable Transport (SRT) protocol. By exploiting the global addressing capability of IPv6, our solution simplifies direct node interconnections, effectively eliminating complexities associated with Network Address Translation (NAT). Furthermore, we introduce an innovative chain-relay transmission method combined with distributed node management strategies, substantially reducing reliance on central servers and minimizing deployment complexity. Leveraging SRT’s low-latency UDP transmission, packet retransmission, congestion control, and AES-128/256 encryption, the proposed system ensures robust security and high video stream quality across wide-area networks. Additionally, a WebSocket-based real-time fault detection algorithm coupled with a rapid fallback self-healing mechanism is developed, enabling millisecond-level fault detection and swift restoration of disrupted links. Extensive performance evaluations using Video Multi-Resolution Fidelity (VMRF) metrics across geographically diverse and heterogeneous environments confirm significant performance gains. Specifically, our approach achieves substantial improvements in latency, video quality stability, and fault tolerance over existing P2P methods, along with over tenfold enhancements in frame rates compared with conventional RTMP-based solutions, thereby demonstrating its efficacy, scalability, and cost-effectiveness for real-time video streaming applications. Full article

Fault detection and remaining useful life (RUL) prediction are critical tasks in self-healing network (SHN) environments and industrial cyber–physical systems. These domains demand intelligent systems capable of handling dynamic, high-dimensional sensor data. However, existing optimization-based approaches often struggle with imbalanced datasets, noisy signals, and delayed convergence, limiting their effectiveness in real-time applications. This study utilizes two benchmark datasets—EFCD and SFDD—which represent electrical and sensor fault scenarios, respectively. These datasets pose challenges due to class imbalance and complex temporal dependencies. To address this, we propose a novel hybrid framework combining Attention-Augmented Convolutional Neural Networks (AACNN) with transformer encoders, enhanced through Enhanced Ensemble-SMOTE for balancing the minority class. The model captures spatial features and long-range temporal patterns and learns effectively from imbalanced data streams. The novelty lies in the integration of attention mechanisms and adaptive oversampling in a unified fault-prediction architecture. Model evaluation is based on multiple performance metrics, including accuracy, F1-score, MCC, RMSE, and score*. The results show that the proposed model outperforms state-of-the-art approaches, achieving up to 97.14% accuracy and a score* of 0.419, with faster convergence and improved generalization across both datasets. Full article

This paper addresses the scientific needs for investment decision-making in distribution networks against the backdrop of new power systems, constructing a three-tier decision-making system that includes investment scale decision-making, investment structure allocation, and investment project prioritization. Initially, it systematically analyzes the new requirements imposed by the new power systems on distribution networks and establishes an investment index system encompassing four dimensions: “capacity, self-healing, interaction, and efficiency”. Next, the Pearson correlation coefficient is employed to screen key influencing factors, and in conjunction with the grey MG(1,1) model and the support vector machine algorithm, precise forecasting of the investment scale is achieved. Furthermore, distribution network projects are categorized into ten classes, and an investment direction decision-making model is constructed to determine the investment scale for each attribute. Then, for the shortcomings of the traditional project comparison method, the investment project decision-making model is established with the attribute investment amount as the constraint and the maximisation of project benefits under the attribute as the goal. Finally, the effectiveness of the decision-making system is verified by taking the Lishui distribution network as a case study. The results show that the system keeps the investment scale prediction error within 5.9%, the city’s total investment deviation within 0.3%, and the projects are synergistically optimized to provide quantitative support for distribution network investment decision-making in the context of a new type of electric power system, and to achieve precise regulation. Full article

With the development of modern power systems, AC distribution networks face increasing demands for supply flexibility and reliability. Energy storage-based soft open points (E-SOPs), which integrate energy storage systems into the DC side of traditional SOP connecting AC distribution networks, not only maintain power flow control capabilities but also enhance system supply performance, providing a novel approach to AC distribution network fault recovery. To fully leverage the advantages of E-SOPs in handling faults in flexible interconnected AC distribution networks (FIDNs), this paper proposes an E-SOP-based FIDN islanding recovery method. First, the basic structure and control modes of SOPs for AC distribution networks are elaborated, and the E-SOP-based AC distribution network structure is analyzed. Second, with maximizing total load recovery as the objective function, the constraints of E-SOPs are comprehensively considered, and recovery priorities are established based on load importance classification. Then, a multi-dimensional improvement of the dung beetle optimizer (DBO) algorithm is implemented through Logistic chaotic mapping, adaptive parameter adjustment, elite learning mechanisms, and local search strategies, resulting in an efficient solution for AC distribution network power supply restoration. Finally, the proposed FIDN islanding partitioning and fault recovery methods are validated on a double-ended AC distribution network structure. Simulation results demonstrate that the improved DBO (IDBO) algorithm exhibits a superior optimization performance and the proposed method effectively enhances the load recovery capability of AC distribution networks, significantly improving the self-healing ability and operational reliability of AC distribution systems. Full article

To overcome the shortcomings of traditional polyurethane, such as poor weather resistance and susceptibility to hydrolysis, this study systematically explores the preparation techniques of organic silicon-modified polyurethane and its application in intelligent sports fields. By introducing siloxane into the polyurethane matrix through copolymerization, physical blending, and grafting techniques, the microphase separation structure and interfacial properties of the material are effectively optimized. In terms of synthesis processes, the one-step method achieves efficient preparation by controlling the isocyanate/hydroxyl molar ratio (1.05–1.15), while the prepolymer chain extension method optimizes the crosslinked network through dual reactions. The modified material exhibits significant performance improvements: tensile strength reaches 60 MPa, tear resistance reaches 80 kN/m, and the elastic recovery rate ranges from 85% to 92%, demonstrating outstanding weather resistance. In sports field applications, the 48% impact absorption rate meets the requirements for athletic tracks, wear resistance of <15 mg suits gym floors, and the impact resistance for skate parks reaches 55%–65%. Its environmental benefits are notable, with volatile organic compounds (VOC) <50 g/L and a recycling rate >85%, complying with green building material standards. However, its development is still constrained by multiple factors: insufficient material interface compatibility, a comprehensive cost of 435 RMB/m², and the lack of a quality evaluation system. Future research priorities include constructing dynamic covalent crosslinked networks (e.g., self-healing systems), adopting bio-based raw materials to reduce carbon footprint by 30%–50%, and integrating flexible sensing technologies for intelligent responsiveness. Through multidimensional innovation, this material is expected to evolve toward multifunctionality and environmental friendliness, providing core material support for the intelligent upgrading of sports fields. Full article

WSNs play a critical role in many applications that require network reliability, such as environmental monitoring, healthcare, and industrial automation. Thus, fault detection and self-healing are two effective mechanisms for addressing the challenges of node failure, communication disruption, a energy constraints faced by WSNs. This paper presents an intelligent framework based on Light Gradient Boosting Machine integration for fault detection and a Flying Fox Optimization Algorithm in dynamic self-healing. The LGBM model provides very accurate and scalable performance related to effective fault identification, whereas FFOA optimizes the recovery strategies to minimize downtown and maximize network resilience. Extensive performance evaluation of the developed system using a large dataset was presented and compared with the state-of-the-art heuristic-based traditional methods and machine learning models. The results showed that the proposed framework could achieve 94.6% fault detection accuracy, with a minimum of 120 milliseconds of recovery time and network resilience of 98.5%. These results hence attest to the efficiency of the proposed approach in ensuring robust and adaptive WSN operations toward the quest for enhanced reliability within dynamic and resource-constrained environments. Full article

To improve the consumption rate of distributed energy and enhance the self-healing performance of distribution networks, this paper proposes a distribution network optimization method considering carbon emissions and dynamic reconfiguration. Firstly, various measures such as dynamic reconfiguration and distributed energy scheduling are used in upper-level optimization to reduce the network loss and solar curtailment cost of the system and to realize the optimal economic operation of the distribution network. Secondly, based on carbon emission flow theory in lower-level optimization, a low-carbon demand response model with a dynamic carbon emission factor as the guiding signal is established to promote carbon emission reduction on the user side. Then, the second-order cone planning and improved dung beetle optimization algorithm are used to solve the model. Finally, it is verified on the test system that the method can effectively reduce the risk of voltage overruns and enhance the low-carbonization and economy of distribution network operation. Full article

Polyimine-based composites have emerged as a promising class of dynamic covalent thermosets, combining high mechanical strength, thermal stability, self-healing, recyclability, and reprocessability. This review systematically summarizes recent advances in polyimine synthesis, highlighting dynamic covalent chemistry (DCC) strategies such as imine exchange and reversible Schiff base reactions. Structural customization can be achieved by incorporating reinforcing phases such as carbon nanotubes, graphene, and bio-based fibers. Advanced fabrication methods—including solution casting, hot pressing, and interfacial polymerization—enable precise integration of these components while preserving structural integrity and adaptability. Mechanical performance analysis emphasizes the interplay between dynamic bonds, interfacial engineering, and multiscale design strategies. Polyimine composites exhibit outstanding performance characteristics, including a self-healing efficiency exceeding 90%, a tensile strength reaching 96.2 MPa, and remarkable chemical recyclability. Emerging engineering applications encompass sustainable green materials, flexible electronics, energy storage devices, and flame-retardant systems. Key challenges include balancing multifunctionality, enhancing large-scale processability, and developing low-energy recycling strategies. Future efforts should focus on interfacial optimization and network adaptivity to accelerate the industrial translation of polyimine composites, advancing next-generation sustainable materials. Full article

As Industry 5.0 progresses, the demand for zero-touch configuration in industrial automation and smart manufacturing is increasing. This paper proposes a dynamic configuration management framework for Time-Sensitive Networking (TSN), aiming to address the challenges of flexibility and adaptability in dynamic network environments. A zero-touch configuration model is presented for TSN by incorporating a Delay-Aware Shortest Path Search (DASPS) algorithm to improve scheduling success rates. Simulation results demonstrate the ability of the framework to reconfigure networks within 2.67 milliseconds. The DASPS algorithm achieves a scheduling success rate of 70.22% for 1000 TSN flows, in contrast to only 22.23% achieved by the Shortest Path Search (SPS) algorithm. The proposed model effectively adapts to dynamic network changes, guaranteeing real-time data transmission. To further evaluate system adaptability, path entropy is introduced as a metric to quantitatively assess the balance of scheduling outcomes under topological changes. In the event of link failures, path entropy experiences a sharp decline but rapidly recovers after reconfiguration, demonstrating the system’s strong self-healing capability. Full article

Urushiol, the principal bioactive component of natural lacquer, has emerged as a promising candidate for developing eco-friendly antimicrobial coatings due to its unique catechol structure and long alkyl chains. This review systematically elucidates the molecular mechanisms underpinning urushiol’s broad-spectrum antimicrobial activity, including membrane disruption via hydrophobic interactions, oxidative stress induction through redox-active phenolic groups, and enzyme inhibition via hydrogen bonding. Recent advances in urushiol-based composite systems—such as metal coordination networks, organic–inorganic hybrids, and stimuli-responsive platforms—are critically analyzed, highlighting their enhanced antibacterial performance, environmental durability, and self-healing capabilities. Case studies demonstrate that urushiol derivatives achieve >99% inhibition against both Gram-positive and Gram-negative pathogens, outperforming conventional agents like silver ions and quaternary ammonium salts. Despite progress, challenges persist in balancing antimicrobial efficacy, mechanical stability, and biosafety for real-world applications. Future research directions emphasize precision molecular engineering, synergistic multi-target strategies, and lifecycle toxicity assessments to advance urushiol coatings in medical devices, marine antifouling, and antiviral surfaces. This work provides a comprehensive framework for harnessing natural phenolic compounds in next-generation sustainable antimicrobial materials. Full article

Polyphenylene sulfide (PPS)-based separators have garnered significant attention as high-performance components for next-generation lithium-ion batteries (LIBs), driven by their exceptional thermal stability (>260 °C), chemical inertness, and mechanical durability. This review comprehensively examines advances in PPS separator design, focusing on two structurally distinct categories: porous separators engineered via wet-chemical methods (e.g., melt-blown spinning, electrospinning, thermally induced phase separation) and nonporous solid-state separators fabricated through solvent-free dry-film processes. Porous variants, typified by submicron pore architectures (<1 μm), enable electrolyte-mediated ion transport with ionic conductivities up to >1 mS·cm⁻¹ at >55% porosity, while their nonporous counterparts leverage crystalline sulfur-atom alignment and trace electrolyte infiltration to establish solid–liquid biphasic conduction pathways, achieving ion transference numbers >0.8 and homogenized lithium flux. Dry-processed solid-state PPS separators demonstrate unparalleled thermal dimensional stability (<2% shrinkage at 280 °C) and mitigate dendrite propagation through uniform electric field distribution, as evidenced by COMSOL simulations showing stable Li deposition under Cu particle contamination. Despite these advancements, challenges persist in reconciling thickness constraints (<25 μm) with mechanical robustness, scaling solvent-free manufacturing, and reducing costs. Innovations in ultra-thin formats (<20 μm) with self-healing polymer networks, coupled with compatibility extensions to sodium/zinc-ion systems, are identified as critical pathways for advancing PPS separators. By addressing these challenges, PPS-based architectures hold transformative potential for enabling high-energy-density (>500 Wh·kg⁻¹), intrinsically safe energy storage systems, particularly in applications demanding extreme operational reliability such as electric vehicles and grid-scale storage. Full article

This study presents a sustainable approach to transform waste cooking oil (WCO) into a multifunctional 3D-printable photocurable elastomer with integrated self-healing capabilities. A linear monomer, WCO-based methacrylate fatty acid ethyl ester (WMFAEE), was synthesized via a sequential strategy of transesterification, epoxidation, and ring-opening esterification. By copolymerizing WMFAEE with hydroxypropyl acrylate (HPA), a novel photocurable elastomer was developed, which could be amenable to molding using an LCD light-curing 3D printer. The resulting WMFAEE-HPA elastomer exhibits exceptional mechanical flexibility (elongation at break: 645.09%) and autonomous room-temperature self-healing properties, achieving 57.82% recovery of elongation after 24 h at 25 °C. Furthermore, the material demonstrates weldability (19.97% retained elongation after 12 h at 80 °C) and physical reprocessability (7.75% elongation retention after initial reprocessing). Additional functionalities include pressure-sensitive adhesion (interfacial toughness: 70.06 J/m² on glass), thermally triggered shape memory behavior (fixed at −25 °C with reversible deformation/recovery at ambient conditions), and notable biodegradability (13.25% mass loss after 45-day soil burial). Molecular simulations reveal that the unique structure of the WMFAEE monomer enables a dual mechanism of autonomous self-healing at room temperature without external stimuli: chain diffusion and entanglement-driven gap closure, followed by hydrogen bond-mediated network reorganization. Furthermore, the synergy between monomer chain diffusion/entanglement and dynamic hydrogen bond reorganization allows the WMFAEE-HPA system to achieve a balance of multifunctional integration. Moreover, the integration of these multifunctional attributes highlights the potential of this WCO-derived photocurable elastomer for various possible 3D printing applications, such as flexible electronics, adaptive robotics, environmentally benign adhesives, and so on. It also establishes a paradigm for converting low-cost biowastes into high-performance smart materials through precision molecular engineering. Full article

The subway power supply system, as a critical component of urban rail transit infrastructure, plays a pivotal role in ensuring operational efficiency and safety. However, current systems remain heavily dependent on manual interventions for fault diagnosis and recovery, limiting their ability to meet the growing demand for automation and efficiency in modern urban environments. While the concept of “self-healing” has been successfully implemented in power grids and distribution networks, adapting these technologies to subway power systems presents distinct challenges. This review introduces an innovative approach by integrating multi-agent systems (MASs) with advanced artificial intelligence (AI) algorithms, focusing on their potential to create fully autonomous self-healing control architectures for subway power networks. The novel contribution of this review lies in its hybrid model, which combines MASs with the IEC 61850 communication standard to develop fault diagnosis, isolation, and recovery mechanisms specifically tailored for subway systems. Unlike traditional methods, which rely on centralized control, the proposed approach leverages distributed decision-making capabilities within MASs, enhancing fault detection accuracy, speed, and system resilience. Through a thorough review of the state of the art in self-healing technologies, this work demonstrates the unique benefits of applying MASs and AI to address the specific challenges of subway power systems, offering significant advancement over existing methodologies in the field. Full article

Autonomous vessels are becoming paramount to ocean transportation, while they also face complex risks in dynamic marine environments. Machine learning plays a crucial role in enhancing maritime safety by leveraging its data analysis and predictive capabilities. However, there has been no review grounded in bibliometric analysis in this field. To explore the research evolution and knowledge frontier in the field of maritime safety for autonomous shipping, a bibliometric analysis was conducted using 719 publications from the Web of Science database, covering the period from 2000 up to May 2024. This study utilized VOSviewer, alongside traditional literature analysis methods, to construct a knowledge network map and perform cluster analysis, thereby identifying research hotspots, evolution trends, and emerging knowledge frontiers. The findings reveal a robust cooperative network among journals, researchers, research institutions, and countries or regions, underscoring the interdisciplinary nature of this research domain. Through the review, we found that maritime safety machine learning methods are evolving toward a systematic and comprehensive direction, and the integration with AI and human interaction may be the next bellwether. Future research will concentrate on three main areas: evolving safety objectives towards proactive management and autonomous coordination, developing advanced safety technologies, such as bio-inspired sensors, quantum machine learning, and self-healing systems, and enhancing decision-making with machine learning algorithms such as generative adversarial networks (GANs), hierarchical reinforcement learning (HRL), and federated learning. By visualizing collaborative networks, analyzing evolutionary trends, and identifying research hotspots, this study lays a groundwork for pioneering advancements and sets a visionary angle for the future of safety in autonomous shipping. Moreover, it also facilitates partnerships between industry and academia, making for concerted efforts in the domain of USVs. Full article