With the continuous expansion of air transportation, airport surface operations are facing increasing pressure in terms of safety and efficiency. During the taxiing phase, aircraft operate in a complex and densely trafficked environment, which is highly susceptible to structural constraints and information uncertainty, leading to risks such as runway incursions and surface conflicts. In this context, improving positioning accuracy and navigation support during the taxiing phase has become a critical means of ensuring safe airport surface operations. This paper presents a comprehensive review of research on aircraft taxiway positioning and navigation systems. From a technical perspective, it focuses on analyzing the characteristics and limitations of satellite-based positioning methods, ground surveillance and sensor-based positioning techniques, vision-based and intelligent perception approaches, and multi-source information fusion positioning technologies in airport surface scenarios. On this basis, recent advances in aircraft taxiway path planning and navigation methods are further reviewed, including multi-objective path optimization, taxi navigation under dynamic operating environments, and navigation decision-support techniques. This review provides a systematic understanding of the main research directions and development trends in aircraft taxiway positioning and navigation systems, and serves as a reference for future research and engineering applications.

Truss structures are widely used in actual bridge systems. During a long period of service life, the aging process of the bridges and the influence of the environment cause the measurements to be susceptible to noise during observations, thus reducing the ability of the data to effectively limit the posterior distribution of the model parameters. To overcome these issues, a numerical model based on OpenSees is adopted as the forward model to identify the parameter-response sensitivity matrix using the finite difference method. Under the assumption that the measurements follow a Gaussian distribution, a Fisher information matrix is introduced to characterize the information content of different candidate schemes for ranking and selecting loading and measurement schemes. Then, the Bayesian parameter updates based on the selected representative schemes are performed, while posterior degeneracy for the parameters is calculated using Monte Carlo sampling methods. The efficacy of the selected schemes is demonstrated by comparing the ability to reduce uncertainties and parameter correlations.

Traditional deep-sea equipment primarily relies on batteries or surface-laid cables for power, which imposes substantial constraints on mission economic viability and entails high maintenance costs. Inspired by the highly efficient filtering and capture mechanism of the deep-sea feather duster worm, this research developed a novel bionic composite material that integrates efficient particle capture with triboelectric nanogenerator (TENG) functionality. By analyzing the fluid-structure interaction mechanics of the multi-level structure of the feather duster worm's crown tentacles and combining it with the solid-liquid interface contact electrification mechanism, an innovative multi-scale fractal-structured bionic fiber network was designed. Flexible composite fibers consisting of polymer and conductive materials were fabricated using coaxial electrospinning micro-nano assembly technology. For functional validation, a simulated deep-sea environment featuring low temperature, high pressure, and low flow velocity was constructed. Experimental results indicate that under simulated deep-sea flow conditions of 3-5 cm/s, the bionic material achieved a stable capture efficiency of approximately 85% for standard 5 μm particles. The triboelectric power generation unit produced an open-circuit voltage of about 3.2 V and a short-circuit current of approximately 0.55 μA, delivering an estimated power density of 1.7 mW·m⁻², which is sufficient to power micro-sensors.

Electronic chips are becoming more and more integrated, so the heat they produce is getting much denser, and effectively dissipating heat has become a key technical problem that limits the performance and reliability of electronic devices; microchannel heat sinks are a revolutionary cooling technology, and because they have the advantages of compact structure and high heat transfer efficiency, they have become a research focus for cooling high-power electronic devices, this paper systematically explains the working principle of microchannel heat sinks, analyzes the key factors affecting their performance including the characteristics of working fluids, the geometric parameters of microchannels and material selection, on this basis, it details the methods to improve the performance of microchannel heat sinks through innovative structural design, working fluid optimization and integrated design, finally, it looks forward to the future research directions and development trends of microchannel heat sinks including multi-functional integration, the application of new materials and intelligent design, and this review aims to provide a theoretical reference for the further research and technological innovation of microchannel heat sinks.

To solve the problem of accuracy and reliability of damage identification of old bridge structures, this paper compares and analyzes three damage identification methods: Bayesian update, curvature modal analysis and BP neural network. The performance comparison is carried out from the four dimensions of local damage identification accuracy, data demand and utilization efficiency, anti-interference and uncertainty quantification, and then the reliability is analyzed from the essential difference between the deterministic and uncertain methods. The analysis shows that the Bayesian updating method has more advantages in uncertainty quantification and anti-interference. The physical mechanism of curvature modal analysis is clear, which is suitable for damage location under the condition of low noise and high-density measurement points; the BP neural network relies on large-scale samples, and its generalization ability is limited. Finally, I propose that the future should focus on the research direction of "physical mechanism+data-driven" hybrid model construction, lightweight real-time monitoring technology development, and deep integration of deep learning and uncertainty quantification.

The paper is a thorough review of high-gain antenna concepts of microstrip antennas to fifth-generation (5G) and next-generation (6G) wireless communication systems. The reason behind the extensive implementation of microstrip antennas includes their small shape, simple integration capabilities and reduced costs of fabrication. Nevertheless, the traditional designs have inherent drawbacks such as low gain, low bandwidth and high losses at high frequencies. These limitations do not enable them to amplify 5G/6G applications to longer ranges and data rates of extreme resolution. To solve these issues, general strategies are rigorously analyzed. These are metasurface loading to do wave front manipulation and to focus frequencies; frequency-selective surface (FSS) reflectors to create resonant cavity and a gain enhancement of forward radiations; optimization of dielectric substrates with low-loss ceramics and new fabrication; and array configurations that provide gain superposition. The review identifies the performance trade-offs in one technique and underlines the cooperative approach to the design. Balanced gains, bandwidth, profile, and compatibility are made at the cost of composite structures, multi-objective optimization algorithms, and system-level integration. The problems of processing complexity, material losses in millimeter-wave bands, and computing needs are examined nowadays. Intelligent surfaces should move towards reconfigurable surfaces, intelligent design, heterogeneous chip integration, and sensing-communication co-arrangement to support 6G requirements. The paper offers a lot of insights and references into the high-gain microstrip antenna technologies in the next-generation wireless networks.

To address the difficulty in accurately quantifying corrosion damage in Parker truss bridge members during their service life, this paper proposes a member durability identification method based on a combination of OpenSees finite element simulation and the Monte Carlo method. Taking the Parker truss as the research object, damage conditions are simulated by reducing the cross-sectional area of the members. A finite element model is established using OpenSees, and a stochastic simulation process is designed to invert the remaining cross-sectional area of the members from the nodal displacement response based on the rejection sampling algorithm in Bayesian updates. By changing the load application position, the histograms of damage identification results under different stress points are compared to find the optimal stress point for damage identification. Finally, structural mechanics analysis is used to explain the mechanism of the above phenomenon: the optimal stress point can maximize the sensitivity of the damaged member to the overall structural displacement, thereby selecting high-confidence parameter samples through the likelihood function in the stochastic simulation.

Driven by the goal of "double carbon," the construction of a new power system with new energy as the main body has become the core task of China's energy transformation. This paper adopts a literature review to make a systematic compendium of key technologies, development trends, and challenges of new power systems. The construction of a new type of power system needs to focus on a high proportion of new energy consumption, multi-energy complementary synergies, digitalization and intelligent upgrading, and other directions, and rely on the source-network-load-storage integrated planning to achieve system optimization. New energy volatility problems, insufficient grid regulation capacity, the lack of a cost transmission mechanism, etc., still restrict its development, and thus, proposed through technological innovation, sound market mechanisms, and policy synergies to promote a new type of power system high-quality progress and the realization of the "dual-carbon" goals to play a supporting role.

In modern society, virtual reality (VR) is gradually becoming an effective assistant for human–robot collaboration, teleoperation, and robot skill learning by enabling immersive and intuitive human-robot interaction. This research summarizes the latest progress of VR-based humanoid robot teleoperation and skill learning, with a particular focus on real-time control frameworks based on motion capture, mapping and closed-loop force and visual feedback. Key system architectures and representative approaches are reviewed to illustrate how VR technologies support stable, low-latency, and intuitive remote robot control in complex environments. Furthermore, this article reviews the robot skill learning methods based on demonstration and simulation-based reinforcement learning, introduces their fundamental principles and analyzes respective characteristics and analyze their advantages and limitations in complex tasks. The integration of VR technology has effectively promoted more immersive and efficient human-robot interaction, promoted its development in the direction of intelligence, and has a wide range of potential application scenarios in the future.

Because of the techniques' rapid fusion, mechatronics has already become an inevitable societal tendency in the family environment. This paper mainly studies the microcomputer controlling technique of the embedded system of household appliances, emphatically analyzing the coexistence working mechanism between sensors and actuators. The research purpose is to evaluate these systems' technique mechanism and societal value of the aspect of promoting health, industry innovation and sustainable possibility within the framework of the sustainable development purpose of the United Nations. This study adopted the methods of literature analysis and comparative analysis. The research subjects were the hardcore architectures in contemporary smart household appliances. The research data mainly originated from global academic databases and the technical specifications of related equipment. The study found that the high-precision feedback loop constructed by sensors and actuators in coordination is the foundation for maintaining a healthy indoor environment and optimizing energy consumption. This paper discovered that embedded intelligence is a technological advancement and also a key driving force for achieving sustainable development. Through extending the lifespan of devices and reducing ecological footprints, these integrated systems provide a solid hardware foundation for future sustainable communities.