NetWork
Experimental design of satellite power over-discharge protection system
ZHANG Rui;SHEN Zanwei;ZHANG Shiwen;SHU Guohua;[Objective] This study addresses the issue of insufficient interdisciplinary adaptability in electrical engineering experimental teaching within the context of new engineering disciplines. To resolve the structural contradiction between the current standardized teaching content system and students’ personalized development needs， a layered experimental teaching scheme based on a lithium-ion battery over-discharge protection system is designed. [Methods] The research is conducted within the engineering context of satellite power systems. The system comprises a voltage comparison module， a threshold comparison module， and a relay drive module. It is supported by a three-level experimental teaching system consisting of a “basic layer，” an “advanced layer，” and an “innovation layer，” forming a competency development pathway of “system cognition， professional deepening， and innovative breakthrough.” As a compulsory module for students from different majors， the basic experiment guides students to decompose system-level technical indicators (such as voltage detection range and over-discharge protection threshold) into achievable circuit module design parameters through problem decomposition and index analysis of engineering tasks， thereby strengthening theoretical cognition and basic practical ability. The advanced experiment and the innovative experiment implement differentiated designs according to professional characteristics. The advanced experiment introduces a “two-out-of-three” redundant architecture design based on the characteristics of device failure in extreme space environments. Through the reliability design of both the sampling end and the execution end， students’ understanding of hardware reliability is strengthened. The innovative experiment addresses the limitations of the above scheme in performing energy early warning and mitigating relay contact adhesion. By integrating a multi-level energy management software algorithm， the system achieves collaborative optimization of the hardware protection circuit and the software-based intelligent algorithm， with emphasis on enhancing students’ algorithm implementation capability in the field of embedded systems. Students with stronger learning capabilities can further carry out full-process engineering training. Through comprehensive practice from circuit design to system implementation， the final output conforms to the functional model required by engineering application standards. [Results] Using Multisim software， the designs of the basic and advanced experiments were tested. (1) When the battery voltage is 28 V， the corresponding output is 5 V. The measured output characteristics exhibit a good linear relationship within the range of 21–29.4 V， 4 V， meeting the voltage detection range requirement of Task 1. (2) When the battery voltage exceeds 21.011 7 V， the output is low (relay closed)， and when the battery voltage falls below 20.632 2 V， the output is high (relay activated)， satisfying the threshold level requirements in Task 2. (3) When any sampling channel device fails， the system voting output can still maintain the correct voltage value based on the remaining two normal sampling channels， meeting the Task 3 requirement of allowing one sampling failure. (4) When any drive circuit device fails， the transistor array can still ensure correct relay operation， thereby meeting the Task 3 requirement of allowing one drive failure. (5) The software algorithm in the innovative experiment was verified using a software parameter injection testing method. The results show that when the battery voltage drops to the first warning value， the system correctly triggers the energy warning and disconnects the high-power load. If the voltage continues to decrease to the second warning value， the system further disconnects non-essential loads and finally performs power-off protection， satisfying the multi-level voltage protection requirements specified in Task 4. [Conclusions] As a comprehensive practical component of the electrical engineering experimental course， the satellite power over-discharge protection system designed in this study is arranged at the end of the course with a total of 6 class hours. Students are required to integrate their prior knowledge of circuit analysis， instrument operation， Arduino/C programming， and related subjects to complete full-process training from theory to engineering implementation. Guided by engineering problems， the system establishes multi-level and extensible experimental tasks， achieving the organic integration of students’ independent choice and engineering capability development. It also provides personalized development pathways for students from different disciplinary backgrounds.
Development and performance evaluation of phase change microcapsule-based inhibitors for controlling gas hydrate dissociation in marine reservoirs
WANG Jintang;GUO Jianxun;HUANG Xianbin;WANG Xiaopu;LIAO Bo;LI Meichun;WANG Ren;LYU Kaihe;SUN Jinsheng;[Objective] Natural gas hydrates are a key resource for enhancing energy security and supporting the low-carbon transition in countries along the Maritime Silk Road. During drilling in hydrate-bearing formations， heat transfer from the drilling fluid to the formation is the main factor driving reservoir hydrate dissociation and borehole wall instability. To maintain stability in hydrate-bearing formations during drilling， a treatment agent was developed to regulate the temperature of the drilling fluid in the wellbore and reduce heat transfer from the fluid to the hydrate reservoir. To meet current engineering requirements for natural gas hydrate drilling and production， a high phase-change latent heat n-alkane was chosen as the core material， while environmentally friendly sodium alginate and carboxymethyl cellulose served as shell materials. Using a physical cross-linking mechanism， microencapsulated phase-change cold-storage materials with a core–shell structure (phase-change microcapsules [PCMCs]) were produced via an electrostatic spraying technique. [Methods] This method allowed precise control of droplet formation and solidification， facilitating accurate adjustment of microcapsule size distribution. The microstructure and surface morphology of the PCMCs were analyzed by scanning electron microscopy and a focused beam reflectance measurement system， while laser particle-size analysis determined their particle-size distribution. Differential scanning calorimetry was employed to study the phase-change behavior， latent heat properties， and thermal cycling stability of the PCMCs. Additionally， a thermal conductivity analyzer and a high-pressure natural gas hydrate evaluation system—capable of simulating hydrate reservoir pressure–temperature conditions—were used to systematically assess the thermal insulation capabilities of the PCMCs and their inhibitory effect on hydrate decomposition. [Results]Results demonstrated that the PCMCs maintained an intact spherical capsule shape with a clear core–shell interface， and had a particle size distribution ranging from 5.7 to 50.09 μm， with a median diameter of 21.26 μm， meeting the operational demands of offshore drilling. Moreover， the phase-change melting temperature was 13.88 ℃ with a latent heat of 171.51 J/g and an encapsulation efficiency of 89.5%. After 30 heating–cooling cycles， the PCMCs retained 90.6% of their initial latent heat， indicating excellent thermal reliability and suitability for repeated use during drilling， which helps lower operational costs. When added to drilling fluid， the PCMCs reduced the system’s bulk thermal conductivity by approximately 13%， extended the time to reach the target temperature by 26.3%， and decreased hydrate decomposition rates by 12.57% compared with the control fluid. Ultimately， these PCMCs effectively inhibit reservoir hydrate dissociation by lowering the thermal conductivity， slowing heat transfer from wellbore fluid to the formation， and absorbing heat during phase change to mitigate temperature increases. [Conclusions] This research has been applied in China’s third offshore natural gas hydrate trial production campaign， providing a theoretical basis and technical support for future development and utilization in hydrate-rich countries like Pakistan and Indonesia. It is also expected to promote further technical exchange and cooperation in gas hydrate exploration， development， and wellbore integrity among countries participating in the Belt and Road Initiative.
Experimental platform for power systems large-scale blackout restoration optimization considering multi-level scheduling collaboration
CHEN Changming;CHEN Feixiong;SHAO Zhenguo;[Objective] In recent years， frequent extreme natural disasters such as typhoons have posed severe threats to the secure and stable operation of power systems. Large-scale blackouts caused by these disasters can result in serious socio-economic losses and system instability. Consequently， post-disaster power grid restoration optimization has become an important research topic with both theoretical and practical significance. However， existing studies mainly focus on model construction and algorithm design， while lacking experimental platforms that can directly support teaching and provide students with intuitive and operational learning resources. To address this gap， this study designs an experimental platform for optimizing large-scale blackout restoration in power systems considering multi-level scheduling collaboration. The platform aims to transform complex research results into practical teaching tools， enabling students to understand restoration mechanisms and develop optimization modeling and engineering application skills under disaster scenarios. [Methods] The proposed platform simulates post-typhoon grid restoration processes under a multi-level dispatch framework and includes five core functional modules: power grid restoration safety risk assessment， national-level dispatch restoration simulation， provincial-level restoration simulation， regional-level restoration simulation， and multi-level coordinated restoration optimization. The risk assessment module evaluates restoration risks of transmission lines and substations based on the spatiotemporal characteristics of typhoon wind fields， providing quantitative indicators for subsequent optimization. The regional-， provincial-， and city-level modules model restoration processes for 500 kV， 220 kV， and 110 kV grids， respectively， including black-start unit scheduling， network topology restoration， load restoration， and inter-level coupling mechanisms. The multi-level coordination module establishes a hierarchical optimization framework in which dispatch centers at different levels exchange real-time data and coordinate decision-making to improve overall restoration efficiency. The experimental design adopts a layered case-based teaching approach. Students simulate a full-scale blackout scenario， beginning with local restoration and gradually progressing to inter-level coordination. Using MATLAB as the simulation environment， they conduct model construction， optimization solving， and result analysis through interactive experimentation. [Results] Experimental applications demonstrate that the proposed platform effectively integrates disaster simulation， restoration optimization， and teaching practice. In simulated typhoon scenarios， students can visualize the influence of wind speed distribution on restoration security and efficiency， analyze dynamic interactions among different dispatch levels， and understand how information exchange improves global optimization performance. The hierarchical optimization process， in which provincial dispatch collects regional load forecasts， coordinates with national dispatch for generation restoration， and iteratively refines restoration schemes， significantly improves overall restoration speed and reliability compared with independent single-level operations. Through the designed teaching cases， students can understand the mechanisms of risk assessment， black-start strategy formulation， and multi-level coordination while developing skills in mathematical modeling， simulation analysis， and practical problem-solving. The platform therefore bridges the gap between theoretical research and hands-on education and provides a systematic approach for integrating disaster restoration optimization into engineering training. [Conclusions] This study develops an experimental platform for power systems large-scale blackout restoration optimization considering multi-level scheduling collaboration， achieving the transformation of complex scientific models into modular teaching resources. By combining optimization modeling with case-based instruction， the platform improves students’ understanding of restoration optimization theory and strengthens their ability to apply these concepts in practical engineering contexts. The platform also validates the effectiveness of multi-level coordinated restoration strategies in improving restoration speed， efficiency， and system resilience after large-scale blackouts. In future work， the platform can be extended to scenarios such as AC–DC hybrid grid restoration， pre-disaster prevention， and emergency repair， thereby promoting deeper integration of research achievements with teaching innovation and talent cultivation in power system engineering.
Design of a comprehensive experiment for the construction of a berberine-based fluorescent probe for mercury ions
WU Chong;AN Rui;ZHANG Yan;LUO Guoyong;YANG Wude;[Objective] The detection of mercury ions (Hg2+) is of great significance because they are highly toxic environmental pollutants that pose serious threats to human health and can cause severe diseases. Over the past few decades， fluorescent probe-based detection methods have exhibited immense potential for Hg2+ detection owing to their superior characteristics， including high sensitivity， excellent selectivity， rapid response， simple operation， and capability for real-time visual detection. Berberine is a bioactive isoquinoline alkaloid mainly isolated from traditional Chinese medicinal herbs such as Rhizoma Coptidis and Cortex Phellodendri. In addition to its diverse pharmacological activities (e.g.， remarkable antibacterial and anti-inflammatory effects)， its unique molecular structure provides favorable photophysical properties. Specifically， berberine possesses an extended π-conjugated system that can generate stable fluorescence emission under appropriate excitation conditions， making it an ideal molecular scaffold for fluorescent probe design. Therefore， herein， a fluorescent probe based on the berberine skeleton was designed and synthesized for the rapid and highly sensitive detection of Hg2+. [Methods] Using berberine hydrochloride as the starting material， a fluorescent probe was constructed by introducing a phenyl carbonothioate group as the recognition unit for Hg2+. The molecular structure of the probe was characterized via nuclear magnetic resonance spectroscopy and high-resolution mass spectrometry (HRMS). The sensing performance of the probe toward Hg2+ was evaluated using fluorescence spectroscopy， and the recognition mechanism was further investigated using HRMS and infrared spectroscopy. The practical applicability of this probe for Hg2+detection was validated through recovery experiments using real water samples. [Results] The fluorescent probe B-PT was synthesized using berberine hydrochloride as the precursor， and its molecular structure was fully confirmed via spectroscopic characterization. In a THF/H2O (v/v， 9:1) mixed solvent system， B-PT enables the rapid detection of Hg2+ with a response time of <3 min. The probe itself exhibits extremely weak fluorescence; however， upon addition of Hg2+， a prominent emission peak appears at 550 nm. By contrast， no significant fluorescence changes were observed in the presence of other tested metal ions， including Na+， Cu2+， Mg2+， Cr3+， Ca2+， K+， Al3+， Zn2+， Ag+， Cd2+， Fe3+， Co2+， Ni2+， and Pb2+， demonstrating the high specificity of the probe toward Hg2+. This high specificity was further corroborated by interference experiments， which confirmed the excellent anti-interference capability of B-PT. The calibration curve revealed a good linear correlation between fluorescence intensity and Hg2+ concentration in the range of 5.0–60.0 μmol/L， enabling the quantitative determination of Hg2+ with a calculated detection limit of 1.6 × 10-8 mol/L. Mechanistic studies verified that the specific detection of Hg2+ originates from Hg2+-triggered desulfurization and hydrolysis of the phenyl carbonothioate moiety in B-PT， resulting in the formation of the strongly green-fluorescent product B-OH. Moreover， analysis of real water samples confirmed the stable and reliable sensing performance of B-PT， highlighting its great potential for practical environmental monitoring applications. [Conclusions] This experiment extends the application of berberine， a classic natural product， to the development of the fluorescent probe B-PT for the detection of Hg2+ in real environmental samples. The experimental design integrates practicality， scientific interest， and forward-looking value. It helps students deepen their understanding and integration of theoretical knowledge from natural product chemistry， organic chemistry， and analytical chemistry while strengthening their basic experimental skills and cultivating their scientific research literacy and innovative thinking. Teaching practice demonstrates that the integration of cutting-edge research achievements into comprehensive chemistry experiment teaching enhances students’ learning initiative and exploratory enthusiasm. It also promotes the organic integration of theoretical teaching and practical application， facilitates the systematic integration of multidisciplinary knowledge， and provides strong support for cultivating students’ comprehensive innovation abilities.
Construction and application of a physical similarity simulation experiment platform based on DIC and infrared thermal imaging technology
GAO Changsi;GAO Lin;WANG Yongying;MO Feilong;ZHAI Haoran;LI Sheng;TANG Rong;[Objective] A similar simulation experiment is a solid model experiment technology based on similarity theory. It can effectively simulate the overall deformation behavior of rock strata during the mining process and accurately reflect the structural relationships of coal and rock mass failure. It plays an important role in underground mining engineering research. In such experiments， the accurate capture and measurement of deformation parameters is particularly critical. [Methods] In this paper， based on digital image correlation (DIC) and infrared thermal imaging technology， an experimental platform for simultaneous monitoring of strain field and temperature field was constructed. The platform consists of three modules: a physical experiment frame model， a loading system， and a multi-source data acquisition system. The physical model was designed strictly according to similarity theory， satisfying the similarity criteria of geometry， mechanics， and motion， and can accurately simulate the actual rock structure. The loading system is used to reproduce changes in roof pressure induced by rock movement and to realize stress path control under different mining conditions. The data acquisition system integrates optical， thermal， and electrical sensing equipment; supports synchronous acquisition and fusion analysis of DIC full-field strain， infrared temperature field， and stress signals; and provides technical support for multi-physical field collaborative monitoring. [Results] Using this experimental platform， based on DIC and infrared thermal imaging technology， a physical similarity simulation experiment was carried out against the engineering background of gob-side entry retaining in a mine in Guizhou. The results are as follows: as the working face advanced to 36 m， 69 m， 93 m， and 100 m， the roof strata underwent the process of breaking， sinking， and stabilizing. There was high spatial consistency between the displacement field and the temperature field. The rock mass deformation and stress concentration areas shown in the displacement field corresponded to the high-temperature areas detected by infrared imaging， confirming that friction， collision， and dislocation of rock blocks are the main heat generation mechanisms. The mechanical behavior of rock mass revealed by DIC technology and the energy conversion process reflected by infrared thermal imaging were mutually verified， fully demonstrating the comprehensive advantages of multi-field coupling monitoring in revealing the failure mechanism of rock mass and providing a reliable basis for surrounding rock stability evaluation and disaster precursor identification. [Conclusions] The introduction of this experimental platform into research and teaching effectively improves students’ ability to comprehensively apply theoretical knowledge and advanced experimental methods to solve practical engineering problems. The teaching goal of “promoting learning by research and integrating learning with research” has been realized.
Development and application of low-dose TEM techniques for beam-sensitive materials
LIN Qingyun;ZENG Yuewu;WEI Xiao;[Objective] With the rapid development of novel functional materials (such as molecular sieves， metal–organic frameworks， and biomimetic materials)， high-resolution microstructural characterization of electron beam-sensitive materials faces severe challenges. Conventional transmission electron microscopy (TEM) techniques often induce severe radiation damage to these materials under high-energy electron beam irradiation， resulting in crystalline-to-amorphous phase transition. This can significantly impair atomic-resolution imaging and hinder the study of structure–property relationships. Addressing the fundamental challenge of minimizing beam damage while maintaining high-resolution imaging with excellent signal-to-noise ratio has become a scientific priority for the structural and chemical analysis of electron beam-sensitive materials using electron microscopy. Thus， this study aims to establish a cost-effective， low-dose technique for characterizing materials using conventional TEM through systematic optimization of electron-optical parameters and imaging conditions. [Methods] The investigations were conducted using a field-emission TEM (JEOL JEM-F200， Japan) equipped with a bottom-mounted CMOS camera (XAROSA-EMSIS， German). Stepwise univariate analysis was employed to investigate the key parameters governing the electron dose， including the acceleration voltage， electrostatic lens potentials， condenser lens current， C2 aperture size， and irradiation time. The beam current was quantitatively monitored in situ using an external picoammeter connected to the TEM’s Faraday cup. All measurement data were converted to the electron dose rate (e/?2s) to enable quantitative comparison for isolating parameter-specific effects and synergistic mechanisms. To improve the performance of the system for low-dose imaging， comprehensive technical optimizations were implemented， including precision TEM alignment (gun tilt/shift， condenser lens calibration， and stigmatism correction)， objective lens aberration correction under high-magnification， camera exposure parameter tuning and energy-dispersive spectrometer efficiency improvements. [Results] A complete low-dose TEM characterization system was successfully implemented through hierarchical parameter optimization. First， moderately reducing the acceleration voltage can significantly decrease the initial dose. Second， adjusting the current of the condenser lens (beam spot size reduction) and controlling the irradiation time are simple approaches for effectively suppressing electron beam damage. Finally， the electrostatic lens voltage and C2 aperture size should be adjusted according to the sample’s characteristics and characterization requirements. Multi-parameter synergistic regulation can achieve superior dose reduction compared to single-parameter adjustments. Through systematic optimization of electron-optical parameters and imaging conditions， high-resolution microscopic imaging with a high signal-to-noise ratio was achieved under low-dose conditions using conventional TEM. The developed method can successfully resolve the technical challenge of balancing the resolution and signal-to-noise ratio in traditional low-dose modes， thereby providing a reliable solution for atomic-scale structural analysis of electron beam-sensitive materials. [Conclusions] A cost-effective and highly efficient low-dose TEM methodology was developed through an innovative “methodology-over-hardware” approach， overcoming the technical limitations of conventional TEM in characterizing electron beam-sensitive materials. By establishing a dynamic equilibrium model between dose control and image quality， ordinary TEM can achieve low-damage characterization capabilities comparable to those of cryo-TEM without requiring additional hardware investments. This technology can provide customized low-dose testing solutions for various sensitive materials， significantly enhancing the efficiency of equipment and service capabilities of public testing platforms. The developed approach possesses extensive practical value and promotion prospects， offering a new paradigm for precise microscopic analysis of beam-sensitive materials.
Design and experimental evaluation of uniform magnets based on finite element analysis
YUAN Bo;REN Xiuyan;WU Dan;TIAN Yaqi;WANG Guobao;[Objective] The generation of a uniform magnetic field plays a pivotal role in various engineering applications and experimental endeavors， contributing to scientific progress and technological innovation. In the context of plasma experiments， a uniform magnetic field significantly enhances the intensity of gas discharge processes， effectively minimizes the diffusion losses of plasma particles to chamber walls， and markedly improves the overall efficiency of plasma generation. Plasma experiments have stringent requirements in terms of the three-dimensional distribution of the magnetic field and uniformity of magnetic induction intensity. To meet these demands， a dedicated magnet system must be designed to produce a magnetic induction intensity of 1 000 Gs with a uniformity of ±1% within a cylindrical spatial volume of Φ45 mm×150 mm. [Methods] The Helmholtz coil is an effective device for generating uniform magnetic fields over small， localized areas. Finite element modeling was utilized to systematically compute the magnetic induction intensity distributions for coils of varying diameters. The analysis revealed that increasing the coil radius improves magnetic field uniformity while correspondingly decreasing the overall magnetic induction intensity in a nonlinear correlation. This insight allows for informed trade-offs in optimizing the design of devices to balance cost and performance. By integrating the specified design requirements alongside considerations of cost- effectiveness and manufacturing feasibility， a viable engineering scheme was proposed. The three-dimensional distribution of the magnetic field was discussed in detail for a configuration featuring four pancakes per coil and a coil radius of 250 mm. Through iterative simulations， the final magnet design achieved a uniformity of 1 000 Gs±0.52%， demonstrating superior precision exceeding initial expectations. The key structural components of the comprehensive system are the magnet coils， water cooling mechanisms， power supply units， and adjustable supporting brackets. Detailed specifications were provided for the power supply， ensuring stable and efficient operation， as well as for the water cooling system， which maintains thermal stability to prevent overheating and ensure long-term reliability. [Results] To validate whether the magnetic field distribution meets the established design criteria， experiments were performed using a magnet system. Measurements were conducted using a high-precision three-axis Gauss meter， which provided accurate readings across the targeted volume. The results conclusively demonstrated that within the Φ45 mm×150 mm three-dimensional cylindrical space， a magnetic field uniformity of 1 000 Gs±0.66% was attained， fully complying with and even surpassing the required ±1% tolerance in practical implementation. The designed uniformity (±0.52%) was slightly superior to the experimental value (±0.66%)， where three primary contributing factors were identified and analyzed in depth. Overall， the experimental data underscore the high performance of the system and confirm the successful integration of all system components. [Conclusions] This paper details the process for optimizing the design of a uniform magnet system based on finite element calculations. Detailed analyses of three-dimensional magnetic field distributions for various structural configurations were presented， illustrating the nuanced interplay between design parameters and performance outcomes. Experimental analyses following the completion of machining and assembly yielded results that align closely with the designed parameters. Specifically， the measurements confirm that within the prescribed three-dimensional spatial domain (Φ45 mm×150 mm)， the magnetic field uniformity reaches 1 000 Gs±0.66%， where the distribution pattern and induction intensity fully satisfy design specifications. The magnet system has been successfully deployed in real-world applications. Future work can build upon this foundation to explore scalable designs， thereby creating a positive feedback cycle for advancing magnet design.
Improvement of method for preparing electron microscope samples in high-pressure research using diamond anvil cell
WANG Fei;XU Dan;ZHANG Jing;[Objective]The sealing limitations of the diamond anvil cell (DAC) prevent electron beams from penetrating the device during high-pressure experiments， restricting in-situ high-pressure characterization. To investigate the nanoscale microstructural changes of materials under pressure， only indirect analysis through comparison of post-decompression samples at different pressures using multiple transmission electron microscope (TEM) characterizations is feasible. This imposes stringent requirements on the thickness and reproducibility of TEM samples prepared from decompressed DAC specimens. Notably， DAC samples are extremely scarce and minute in size， rendering collection and manipulation highly challenging. Moreover， after DAC decompression， the phase state， content， and dimensions of substances in the sample cavity vary significantly depending on the sample itself and the pressure-transmitting medium. Conventional methods of preparing powder and bulk samples for TEM analysis， as well as commercial equipment for preparing TEM samples， are generally inapplicable， resulting in limited research progress on the electron microscopic characterization of high-pressure decompressed samples. Thus， improving TEM sample preparation techniques is crucial for enhancing the quality of high-pressure samples and the reliability of microstructural characterization. [Methods]In this study， titanium dioxide compressed at 10–30G Pa by DAC and then decompressed to ambient pressure was used as the sample for analysis. Decompressed samples obtained from high-pressure experiments employing methanol–ethanol as the pressure-transmitting medium were prepared for TEM analysis by applying the rinse-dispersion method. Decompressed samples obtained from high-pressure experiments employing the sample itself as the pressure-transmitting medium were prepared for TEM analysis using the puncture-dispersion and focused ion beam (FIB) methods. The recovery rate of the decompressed samples was comprehensively analyzed using scanning electron microscopy (SEM) and TEM to determine the quality of the TEM specimens and their hydrostatic pressure performance. The advantages and disadvantages of these methods were summarized， and an innovative approach was proposed in which ultraviolet (UV)-curable resin is used as both the pressure-transmitting medium and embedding agent. UV-curable resin is a colorless liquid at room temperature， capable of providing excellent hydrostatic pressure in high-pressure experiments. After decompression， it can be cured by UV light， enabling complete sample recovery and integration with the gasket， facilitating further sample preparation using existing commercial equipment such as ion thinning machines and dual-beam microscopes. [Results]The experimental results demonstrate that 1) uncured UV resin exhibits no distinct characteristic peaks in the high-pressure Raman spectra and delivers superior hydrostatic pressure， making it suitable as a pressure-transmitting medium for DAC in high-pressure experiments. 2) UV light of a specific wavelength can penetrate the diamond windows of the DAC to cure the UV resin in the sample cavity. 3) The cured UV resin integrates the sample with the gasket， improving the stability of the decompressed samples and the ease of preparing the TEM samples， thereby enhancing the compatibility of commercial TEM sample preparation equipment with high-pressure decompressed specimens. 4) TEM samples prepared from decompressed and cured UV resin can be effectively utilized for nanoscale microstructural characterization， including TEM morphology observation and high-resolution TEM (HRTEM) analysis. [Conclusions]These studies indicate that the use of UV-curable resin as a pressure-transmitting medium enables the simultaneous achievement of efficient recovery of decompressed samples and excellent hydrostatic pressure in high-pressure experiments. This method can improve the quality and stability of TEM samples derived from high-pressure decompressed materials， thereby providing technical support for advancing research on pressure-induced nanoscale microstructural changes.
Experiments on multi-UAV path planning Using DRL integrating graph neural networks and curriculum learning
Fu Mingjian;Chen Wentao;Zhuo Xiaoxin;Chen Hengsheng;Chen Fei;[Objective] With the widespread application of Unmanned Aerial Vehicles (UAVs) in disaster rescue， industrial inspection， and other scenarios， multi-UAV path planning in limited airspace faces dual challenges of avoiding dense static obstacles and improving operational efficiency. Traditional path planning methods based on environmental priors struggle to adapt to dynamically generated scenarios with randomly distributed obstacles. Existing reinforcement learning algorithms predominantly rely on simplified 2D planar assumptions， neglecting 3D spatial constraints for obstacle avoidance. This study proposes a collaborative decision-making framework integrating Graph Neural Network (GNN) architecture optimization and Progressive Curriculum Learning for multi-UAV path planning in 3D static dense obstacle environments. The key research contributions and innovations are summarized as follows: [Methods] Firstly， a 3D path planning model based on the Markov Decision Process (MDP) is constructed by incorporating altitude dimensions into state representations and designing node-type identification mechanisms. This enables UAVs to distinguish heterogeneous characteristics between themselves and obstacles. Addressing limitations of conventional GNNs in spatial relationship modeling， this work couples edge features (including relative velocity， position， and distance) with neighbor node features (containing relative centroid position， velocity， and type identifiers) and employs multilayer perceptrons to generate joint representations. This approach replaces the linear superposition of independently encoded features used in existing algorithms， thereby enhancing the network's capability to analyze complex obstacle spatial distributions. Secondly， a safety-efficiency-balanced reward function is formulated by integrating multidimensional metrics such as target proximity， first-arrival time， dwell duration， velocity alignment， collision risks， and proximity penalties. This design guides UAVs to achieve optimized trade-offs between obstacle avoidance and navigation objectives， improving trajectory rationality and policy convergence speed. Thirdly， a three-stage progressive training framework is developed， transitioning from sparse to dense obstacle scenarios. UAVs initially learn basic obstacle avoidance strategies in simplified environments， gradually progressing to moderate-difficulty environments， and ultimately generating cooperative paths balancing safety and efficiency in complex obstacle configurations. This methodology addresses suboptimal policy issues caused by excessive exploration space in high-dimensional environments. Finally， a 3D multi-UAV path planning test environment is established using the PyBullet high-fidelity physics simulation platform， featuring randomly distributed static obstacles with varying density levels. [Results] Experimental results demonstrate that the proposed Edge-Enhanced Informative Multi-Agent Proximal Policy Optimization (EC-InforMAPPO) framework outperforms baseline algorithms across all scenario difficulty levels. Its edge feature encoding mechanism， coupling relative motion parameters and spatial relationships， enhances trajectory safety in dense obstacle environments， offering a novel technical pathway for environmental perception modeling in multi-agent systems. Additionally， the progressive curriculum learning framework enhances policy stability in challenging scenarios. The Edge-Enhanced Informative Multi-Agent Proximal Policy Optimization with Curriculum Learning (EC-InforMAPPO-CL) achieves higher obstacle avoidance success rates and faster convergence compared to direct training using equivalent computational resources. This establishes a reusable training paradigm for reinforcement learning in high-dimensional state spaces. [Conclusions] This paper proposes a collaborative decision-making framework that combines edge feature coupling based on Graph Neural Networks with progressive curriculum learning， addressing the challenges in path planning for multiple UAVs in three-dimensional dense obstacle environments. This research provides new insights and technical support for intelligent collaborative navigation of multiple UAVs in complex environments， holding significant application potential and promotional value.
Experimental design for recognizing human action based on a CBAM-Transformer with Dual-Stream Cross-Fusion and Bi-Level programming
ZHANG Xiaoguang;LIU Jiaqing;MA Shuo;SUN Weiqi;SUN Chuan;YUN Xiao;[Objective] Traditional human action recognition methods often face limitations such as low accuracy， high model complexity， and challenges in practical deployment. To overcome these issues， this paper explores integrating sensor principles， digital signal processing， and deep learning techniques， aiming to enhance recognition performance while maintaining computational efficiency. The goal is to develop a robust， lightweight recognition framework suitable for real-time use in edge computing environments like smart homes， elder care， and security monitoring. [Methods] This paper introduces a novel human action recognition approach based on a dual-stream， cross-fusion， and bi-level programming model that incorporates a Convolutional Block Attention Module and Transformer (CBAM-Transformer). A frequency-modulated continuous wave radar platform is built to collect human motion data. The raw radar signals undergo preprocessing， including dimension reorganization， clutter suppression with a Butterworth filter， and time-frequency analysis， resulting in a dual-mode dataset with range and micro-Doppler spectrograms. The network architecture features a bi-level design: the upper layer uses a dual-stream CBAM-Transformer network to facilitate multi-domain feature interaction and global dependency modeling， while the lower layer employs a lightweight MobileNetV3 network. Knowledge distillation helps transfer representational power from the upper to the lower network， enabling model compression and faster inference. The model is trained using stochastic gradient descent with momentum and a cosine annealing learning rate. [Results] Testing on a self-collected dataset of 4，000 samples across eight action categories， the proposed method achieves a recognition accuracy of 96.73%. It also significantly reduces computational load， with only 5.1 million parameters and 1.45 TFLOPs of floating-point operations. Ablation studies confirm the contributions of each component: dual-stream input outperforms single-stream models; the progressive addition of cross-fusion， Transformer， and CBAM modules improves recognition accuracy. Comparative experiments show that the bi-level model maintains high accuracy while drastically cutting resource needs compared to the upper-layer network alone， striking a balance between accuracy and efficiency. [Conclusions] The CBAM-Transformer-based dual-stream， cross-fusion， and bi-level model effectively tackles challenges in human action recognition， such as data scarcity， limited feature representation， and high computational cost. This approach achieves notable improvements in recognition accuracy while remaining lightweight for real-time deployment in resource-constrained edge environments. The paper highlights the advantages of combining attention mechanisms， cross-modal fusion， and bi-level optimization for developing efficient and reliable action recognition systems， offering a practical， scalable solution for intelligent perception tasks.