In this paper, the research progress of stimulus-responsive application systems based on shape memory polymers (SMPs) is systematically reviewed. SMPs have shown great potential in the field of drug-controlled release due to their excellent biocompatibility, degradability and shape memory ability. With the continuous advancement of biomedical engineering and intelligent material science, SMP-based drug delivery systems have emerged as a promising strategy to achieve precise spatial and temporal control of drug release. These systems can respond to specific physiological or external stimuli, thereby improving therapeutic efficacy while minimizing side effects. This paper focuses on the design of SMP system under various stimuli-responsive mechanisms such as thermal response, enzyme response, magnetic response, light response and acoustic response, including multi-responsive fibers, electrospun membranes, balloons and other structural forms, and systematically evaluates its performance from the aspects of biocompatibility, drug release kinetics, on-demand release ability and physical properties. In addition, this paper also discusses the advantages and challenges of various response mechanisms, and puts forward prospects for the construction of multi-mode collaborative response systems, the promotion of animal and clinical experiments, and large-scale production in the future.

The development of Radio communication technology is an important milestone, and the TRF receiver, as a critical innovation in early wireless communication, is of great significance in promoting the development of this field. Although the TRF receiver is rarely used in modern wireless systems, understanding its principle, composition, advantages, and disadvantages helps understand wireless communication technology's evolution. Systematical analysis describes the TRF receiver's core principle, including signal acquisition, selection, RF amplification, information recovery, and audio amplification. The main components of the TRF receiver, such as the antenna, bandpass filter, RF amplifier, and detector, are described in detail. Then, the advantages and disadvantages of TRF receivers are compared with heterodyne receivers, and the problems of TRF receivers are pointed out, such as low sensitivity, poor selectivity, and only suitable for AM modulation. The results highlight the TRF receiver's advantages of simplicity, good noise performance, frequency stability, and low power, suggesting performance optimization through feedback and sampling window adjustment. It discusses the TRF receiver's prospects in wireless sensor networks, satellite communications, and specific applications. Finally, it concludes that the TRF receiver is a wireless communication milestone, laying the foundation despite limitations, with potential for further development through continuous innovation.

Remote sensing technology has become a core method in modern disaster emergency management because of its wide coverage, rapid response capability, and objective observation advantages. This paper systematically reviews the key role of remote sensing throughout the monitoring–assessment–decision support chain in disaster emergency response. In disaster monitoring, remote sensing has gradually shifted from single post-disaster observation to integrated space-air-ground collaborative observation, supporting multi-disaster, dynamic, and continuous early warning. In disaster assessment, artificial intelligence and image interpretation algorithms have promoted the transformation from traditional manual visual interpretation to automated and quantitative intelligent assessment, improving the efficiency and accuracy of disaster loss evaluation. In decision support, remote sensing products have evolved from basic situation maps into intelligent decision-making tools embedded in emergency command systems, providing spatial evidence for rescue deployment, risk zoning, and resource allocation. This paper further compares the characteristics, advantages, and limitations of optical, Synthetic Aperture Radar, and thermal infrared remote sensing. Finally, it discusses current challenges in multi-source data collaboration, model generalisation, real-time processing, and operational integration, and proposes future directions for more intelligent disaster emergency applications.

As 5G mobile communication systems expand into millimeter-wave bands, their enormous continuous bandwidth potential provides a crucial physical foundation for improving network capacity and peak data rates. However, the inherent high path loss, susceptibility to obstruction, and rapid time-varying characteristics of millimeter-wave signals pose a significant challenge to achieving stable and reliable wireless connections. Against this backdrop, efficient beam management technology has become a core enabler for overcoming these physical layer deficiencies and unlocking the potential of millimeter-wave communication. This paper focuses on the 5G New Radio(5G NR) standard and aims to systematically clarify how fundamental physical layer technologies directly determine and optimize the performance of higher-level beam management processes. First, it analyzes flexible waveform parameter sets, reference signal design, and advanced channel coding schemes to enhance control channel reliability for beam management. Then, it delves into how these physical layer mechanisms specifically empower key protocol processes such as initial beam scanning and access, connected-state beam tracking and refinement, and beam failure detection and recovery. This study concludes that the construction of an efficient and robust millimeter-wave beam management system relies on in-depth integration of physical layer signal processing technologies and upper-layer protocol state machines.

The iterative evolution of mobile communication technology serves as a core driver of the digital economy's growth. With 5G having achieved large-scale global commercialization, research into 6G—aimed at meeting the demands of the "Internet of Intelligent Things" and ubiquitous connectivity—is now fully underway worldwide. This paper employs a literature review approach to synthesize domestic and international research findings on 6G, focusing on its key enabling technologies, typical application scenarios, and development bottlenecks. The study reveals that technologies including terahertz communication, reconfigurable intelligent surfaces (RIS), integrated sensing and communication (ISAC), and space-air-ground-sea integrated networking serve as the core pillars of 6G. 6G is poised for deep integration into scenarios like intelligent immersive communication and the Industrial Internet, while hardware development, algorithm optimization, and the unification of standards remain primary challenges. Future work needs to coordinate progress in core technological breakthroughs, standardization and engineering deployment to achieve high-performance and ubiquitous intelligent connectivity.

Demand forecasting is of great significance in aviation supply chain management, and its precision affects the inventory level, procurement time, and operating expenses. However, the present forecasting techniques employed in the aviation sector rely on a single method that fails to account for demand variations, seasonal changes, and supply disruptions. From the aspect of technology management, this paper proposes an AI technology portfolio structure for demand forecasting in the aviation supply chain. By analyzing a Bombardier case, the research comprehensively examines the feasibility and management constraints of different AI techniques, such as deep learning, LSTM, and transformers, under various demand conditions. The framework focuses on choosing and organizing proper AI technique combinations based on the data maturity and the organizational and managerial complexities instead of concentrating on the particular algorithms. According to the literature concerning supply chain prediction and AI application, the main factors influencing the technology selection and the performance of its use are recognized. The results provide beneficial advice for the aviation supply chain managers to utilize AI forecasting technology in complex demand situations.

Wireless access technology is a key area of research since 6G wireless networks are anticipated to enable immersive communication, enormous IoT, intelligent sensing, and ubiquitous coverage. Utilizing the method of literature review, this paper examines Massive Multiple-Input Multiple-Output (Massive MIMO) and Non-Orthogonal Multiple Access (NOMA) as two complementary access technologies for 6G wireless networks and compares their roles in the spatial and power domains. The review finds that Massive MIMO improves spectrum efficiency, system capacity, and link reliability through large antenna arrays, beamforming, and spatial multiplexing, while NOMA increases access density and edge-user fairness through power-domain multiplexing and successive interference cancellation. Their convergence can better support high-capacity 6G access, but it also introduces asynchronous interference, channel state information overhead, and high-frequency coherence challenges. By connecting these challenges with Radio Access Network digital twins, AI-assisted scheduling, and integrated sensing and communication, the paper clarifies a practical research path for Massive MIMO-NOMA convergence in future 6G networks.

Robot sensors undertake basic tasks including positioning, motion control and environmental detection in robotic systems. Sensor signals will be affected by multiple interference factors during collection and transmission. Common disturbances include thermal noise, electromagnetic interference, mechanical vibration and digital quantization error. These negative factors lower signal-to-noise ratio and make measured data less accurate. This paper summarizes mainstream signal processing and noise elimination techniques applied to robot sensors. It takes signal-to-noise ratio as core evaluation standard and analyzes three practical filtering algorithms, namely mean filtering, low-pass filtering and Kalman filtering. The study classifies various errors in the sensor measurement process and discusses feasible methods to implement filtering code on embedded devices. Analysis results prove that combining hardware anti-interference design and software filtering processing can effectively restrain noise and upgrade signal quality. Kalman filtering fits dynamic state calculation and real-time tracking scenes. Mean filtering and low-pass filtering are suitable for embedded hardware with limited computing space and power. The research offers usable reference schemes for sensor signal processing design in practical robot development work.

Based on the pain points of traditional isolated island micro grids, such as insufficient reliability, strong fluctuations in renewable energy, and the possibility of load shedding under extreme conditions, this chapter focuses on the idea of integrating "Smart Grid 2.0 with traditional isolated island micro grids."First, the article gives the overall design structure of the intelligent isolated micro grid.Subsequently, in terms of power generation and energy storage, this paper summarizes the issues of power quality and energy storage configuration caused by the intermittency of generation methods such as photovoltaics and wind power, and lists the applicable technologies. On the transmission, distribution, and electricity usage side, the article explains measures such as solid-state transformers, multi-voltage level settings, and other advanced algorithms that can reduce size and losses, and improve safety and compatibility. On the user end, this paper constructs a model framework that can support demand response, load management, and fair billing through smart meters and higher-frequency sampling. Regarding the central information processing system, this paper establishes the corresponding central platforms responsible for real-time monitoring, risk assessment, fault self-healing, and cross-domain coordination.Finally, the article takes relevant examples to demonstrate the feasibility of the model presented in this paper.

Fluid splashing and irregular deformation of liquid columns widely exist in industrial fluid transport and hydraulic component operation. However, most existing studies adopt idealized models with stable liquid columns and regular boundary conditions, while neglecting the dynamic effects of turbulent disturbances on liquid column evolution. This conventional simplification omits numerous practical engineering fluid phenomena, among which the splashing behavior during water dispenser operation serves as a typical representative scenario. Based on turbulent disturbance theories, theoretical derivation and mathematical modeling of the liquid column morphology are carried out. The conclusion is that the optimal disturbance wavelength satisfies a mathematical correlation with the radius which is defined as the resonance-like effect. Furthermore, simplified modeling of practical engineering components is performed. Qualitative regulation methods under prominent turbulent disturbance effects are proposed correspondingly. Meanwhile, structural measurement and optimization approaches for experimental devices are illustrated, aiming to restrain the resonance-like effect between turbulent fluctuation and liquid column geometric characteristics. Experimental tests are implemented on a self-built laboratory platform, and the characteristic disturbance wavelength of the liquid column is measured. The results demonstrate that turbulent disturbance dominates the generation of irregular liquid column structures and fluid splashing. The proposed modeling method and suppression strategy exhibit good feasibility and effectiveness, providing a theoretical reference for the optimal design and safety control of engineering fluid systems.