NetWork
Research on the Pedagogical Path of AI Programming Assistants in EDA Software Development Education
HE Zhonghai;XU Zhifu;YU Wenjie;FAN Zehui;CAO Sheng;LIU Leyuan;ZHANG Xiaosong;[Objective] The teaching of large-scale Electronic Design Automation (EDA) software development faces significant challenges including massive codebases， complex architectures， and steep algorithm learning curves， which severely constrain students' engineering practice capabilities. While AI programming assistants such as GitHub Copilot and Cursor offer unprecedented opportunities to transform software engineering education， critical questions remain: How can these tools be rationally integrated into complex software development pedagogy? What are the optimal usage patterns that enhance learning without undermining independent problem-solving capabilities? This study systematically investigates the pedagogical pathways and application boundaries of AI programming assistants in EDA software development education. [Methods] Grounded in "progressive capability construction" and "intelligent augmentation enhancement" principles， this research designed a three-tier task system: source code analysis (tracing Yosys synthesis flow)， algorithm comprehension (reverse- engineering ABC's AIG rewriting algorithms)， and functional extension (developing Yosys statistical commands). A controlled experiment with 30 undergraduate students stratified by programming proficiency involved three groups: GitHub Copilot-assisted， Cursor-assisted (with Claude 3.5)， and non-AI-assisted control. The four-week experiment assessed task completion quality， time efficiency， comprehension depth through pre-post tests， and subjective experience via Likert-scale questionnaires. AI interaction frequencies were logged to analyze usage patterns. [Results] AI-assisted groups demonstrated substantial improvements: task quality increased 16.9% (Copilot) to 21.2% (Cursor)， with Cursor showing particular advantages in architecture analysis (20.3%) and algorithm comprehension (20.7%). Time efficiency gains were remarkable—Cursor reduced completion time by 71.0% and Copilot by 47.3%. Post-test scores measuring deep comprehension increased by 8.7 points (Copilot) and 10.8 points (Cursor)， representing 1.9× and 2.3× improvements over the control group's 4.6-point gain. Subjective metrics showed enhanced self-efficacy and reduced frustration. Critically， correlation analysis identified an "optimal usage range" of 20–22 interactions; students exceeding this threshold exhibited declining performance， suggesting excessive reliance undermines independent development capabilities. [Conclusions] This study establishes an empirically validated pathway integrating AI programming assistants into EDA education through progressive task design， moderate AI assistance (20–22 interaction optimal range)， and process-based monitoring. The findings reposition AI tools as cognitive scaffolds rather than knowledge providers， offering actionable insights for complex software engineering pedagogy and providing empirical evidence for AI integration in computing education.
High-temperature mechanical behavior of FKM and its influence on the sealing performance of downhole packers
HUANG Xiaoguang;SUI Zehao;HAO Yihe;LI Qidong;YAN Chuanliang;[Objective] The packer is a protective tool that is connected to the downhole string and used to seal the annular space between the tubing and casing， or the drill pipe and casing in wells. It enables interlayer isolation， fluid control， and risk reduction by facilitating well control. Fluororubber (FKM) has been widely used as an important material for the rubber cylinder of downhole packers because of its excellent hyperelastic properties. With the gradual expansion of China’s oil and gas exploitation to deep layers and deep sea， the high-temperature and high-pressure environments of downholes pose new challenges for ensuring that FKM packers provide safe and reliable sealing. Therefore， accurate control of the mechanical behavior of FKM in high-temperature environments is important for guaranteeing the effective sealing performance of downhole packers. [Methods] A general hyperelastic constitutive model of the rubber was established based on the strain energy density function. Through high-temperature uniaxial tensile and compression tests， the stress–strain curves of FKM samples in the temperature range of 100°C–200°C were obtained. The experimental results were analyzed using the two-parameter and five-parameter Mooney–Rivlin models as well as the Yeoh model， and the most suitable constitutive model for high-temperature conditions was determined. The evolution of stress relaxation in FKM at high temperature was characterized by the generalized Maxwell model and the Prony series. The evolution of stress relaxation was analyzed using high-temperature tests， and the Prony parameters were determined. An axisymmetric finite element model of the FKM packer was established. The effects of high temperature and stress relaxation on the contact stress of the rubber cylinder were studied through numerical simulation， and the sealing factor was introduced to characterize the overall sealing performance of the downhole packer. [Results] The five-parameter Mooney-Rivlin model was more accurate than the two-parameter Mooney–Rivlin model and the Yeoh model for characterizing the high-temperature mechanical behavior of FKM. For a given setting pressure， a higher temperature led to lower contact stress between the rubber cylinder and the inner wall of the casing， and the sealing performance of the downhole packer gradually decreased with increasing temperature. When the influence of stress relaxation was considered， the sealing performance further declined with increasing setting time. The sealing factor at 200 ℃ was 29% lower than that at 100 ℃. Finally， a model for predicting the normalized setting pressure of the downhole packer was constructed based on the high-temperature and stress relaxation effects. The model enables accurate calculation of the setting pressure to ensure long-term， stable sealing in high-temperature environments. [Conclusions] The behavior of FKM in the temperature range of 100 ℃-200 ℃ was systematically studied， and appropriate high-temperature hyperelasticity and stress relaxation models were constructed to effectively widen the applicable temperature range of FKM. The model for predicting the normalized setting pressure can ensure the sealing reliability of rubber cylinders in high-temperature environments. These data provide theoretical guidance for optimizing the structural design and setting scheme of downhole packers in high-temperature environments.
Construction of a simulation platform for unmanned aerial vehicle-based logistics
LIU Silu;HE Zongxing;ZHANG Zeqiang;[Objective] Against the backdrop of rapid smart city development， unmanned aerial vehicle (UAV) logistics has become an indispensable component of modern logistics systems due to its unique advantages of high flexibility， low cost， and high efficiency. This places higher demands on talent cultivation in the field of logistics engineering and management. However， traditional teaching methods are constrained by physical space and hardware costs and can struggle to effectively demonstrate the complete process that must integrate UAV technology， path planning， and scheduling optimization. This results in a significant disconnect between the teaching content and actual industry applications. Simulation technology has emerged as a pivotal tool for innovation in engineering education reform by leveraging its dual strengths of visualizing theoretical knowledge and enabling interactive operational workflows. Therefore， grounded in new engineering education concepts and the advanced philosophy of deep industry–education integration， this work establishes and implements an integrated simulation teaching platform for UAV-based logistics. The platform integrates key components， including UAV selection， mathematical modeling， optimization， and evaluation. [Method] Based on the operations research optimization theoretical framework and intelligent algorithms， this work systematically constructs the mathematical model， intelligent algorithm， and simulation environment for UAV-based logistics. Its three core principles are: (1) The system model. Based on the classical vehicle routing problem and its variants， we establish a mixed-integer programming model that comprehensively considers factors such as the quantity of goods delivered， the UAV type， the complexities of terrain， and airspace constraints. The model minimizes the total transportation distance cost and the total number of UAVs required， and further extends the scheduling optimization model of multimachine cooperative operations. (2) The algorithm solution. To address the complex NP-hard problems， various metaheuristic methods， including genetic algorithm， simulated annealing， and whale optimization algorithm， were used to solve the model and optimize the parameters. We introduce a deep reinforcement learning algorithm to achieve the innovative adaptive and intelligent decision-making ability of the system in uncertain environments. (3) Simulation verification. We built a virtual environment for UAV distribution with the integrated simulation engine， which can visually run the distribution schemes generated by a variety of algorithms (such as calculating the key performance indicators， including task completion time， total cost， or UAV utilization rate). Additionally， the simulation experimental platform for UAV-based logistics is composed of the following five core functional modules， forming complete closed-loop teaching: a system cognition module (for theoretical learning and scene familiarity)， scheme design module (for model construction and algorithm selection)， simulation debugging module (for parameter adjustment and process observation)， scheme verification module (for the operational scheme and data collection)， and comprehensive evaluation module (for multidimensional performance analysis and experimental report generation). [Result] The platform has three contributions to UAV knowledge: First， it establishes a mathematical model of path planning and scheduling optimization for UAV distribution， which provides a theoretical basis for quantitative analysis. Second， the platform not only integrates the traditional optimization algorithm but also introduces a reinforcement learning algorithm to effectively solve the complex problem model. Moreover， the platform has a friendly human–computer interaction interface that supports the whole process of visual control and real-time interaction， which improves the user experience and teaching effect. Additionally， a typical teaching case is introduced based on real goods delivery scenarios in mountainous areas. The results show that the UAV distribution scheme optimized based on this platform is more efficient than traditional vehicle distributions， which verifies the huge advantages of UAV logistics in special scenarios. [Conclusions] Through systematic simulation experiments on UAV-based goods delivery， this work enables students to thoroughly analyze the fundamental principles， core functions， and operational workflows of the delivery simulation system. It models the entire process of drone delivery system optimization—from cognition， analysis， debugging， and validation to evaluation. The platform also produces integrated， multistage， and multicategory adaptable simulations. This research provides critical technical support and a reusable teaching case for the deep application and promotion of simulation technology in intelligent logistics and holds significant reference value for advancing teaching model innovation and constructing high-level experimental platforms in logistics engineering， management science and engineering， and related disciplines at higher education institutions. This work has far-reaching implications for implementing new engineering education concepts and cultivating interdisciplinary talents equipped with systems thinking， innovation， and practicality.
Vortex-induced vibration suppression technology for flexible wind turbine towers
ZHANG Jiaming;WANG Wenrui;FU Deyi;GAO Kaijia;[Objective] As wind turbine tower height increases proportionally with rotor diameter expansion， the tower’s stiffness， constrained by economic costs， has not improved proportionally， resulting in significantly enhanced structural flexibility. This increased flexibility makes the system prone to substantial geometric deformation during operation. Such deformation intensifies the coupled vibration between the tower and blades， thereby weakening equipment stability. Therefore， suppressing tower vibrations under complex operational conditions， such as strong winds and turbulence， while ensuring stable turbine operation， has become a crucial research focus in wind turbine structural safety. Consequently， this study proposes a vortex-induced vibration suppression method designed for flexible wind turbine towers. [Methods] This study focuses on a 140-meter tower of a 3.3 MW wind turbine unit and establishes a finite element model. The wind turbine blades， rotor， drivetrain， and nacelle are simplified as concentrated mass blocks， with 19 concentrated mass points defined at 14 different heights from the tower base to the top. This approach simplifies the model and improves computational efficiency while ensuring the accuracy of the results. A modal analysis of the wind turbine tower was conducted， and the first two mode shapes were obtained and normalized. These results were compared with existing literature to validate the model’s accuracy. This study proposes a vortex-induced vibration suppression scheme by designing spoilers on the outer wall of the tower. Three spoiler arrangement schemes are proposed: double-helical， spaced-helical， and vertical-spaced. Simulations of the three spoiler arrangement schemes were conducted under the same conditions and compared with a tower model without spoilers. The suppression effect of vortex-induced vibrations was evaluated based on the drag coefficient and its standard deviation around the tower wall. To further optimize the scheme， the angle and spacing of the spoilers were adjusted， and flow field velocity data at selected monitoring points were extracted. The optimal parameters of the spoilers were determined based on the vortex-induced vibration frequency， obtained by applying a Fourier transform to the velocity data at the monitoring points. [Results] Computational fluid dynamics simulation results indicate that the double-helical spoilers and spaced-helical spoilers exhibit similar improvement effects， reducing the standard deviation of the drag coefficient by 79.85% and 77.50%， respectively， compared to the conventional tower. The vertical-spaced spoiler scheme shows a slightly lower improvement effect， with a reduction of 41.23%. Simulation results for varying the angle and spacing of the spoilers reveal that， for double-helical spoilers， the vortex-induced frequency of the tower remains lower than that of the conventional tower within the angle range of 40° to 80°. The lowest vortex-induced frequency occurs at 62°， which is 17.8% lower than that of the conventional tower. For spaced-helical spoilers， the vortex-induced frequency remains relatively low within the spacing range of 70 mm to 94 mm， with the lowest frequency occurring at 94 mm， representing a 16.5% reduction compared to the conventional tower. [Conclusions] Installing spoilers on the outer wall of a wind turbine tower can improve the drag coefficient and its standard deviation， demonstrating effectiveness in streamlining the flow field and suppressing vortex-induced vibrations. Optimizing parameters such as the angle and spacing of the spoilers can further reduce the frequency of vortex-induced vibrations and enhance the stability of flexible towers.
Development and evaluation of a VR-based simulation platform for wild mushroom identification and risk perception
YANG Yu;LIU Junhui;[Objective] While Yunnan Province is hailed as China’s “Kingdom of Wild Mushrooms” for its abundant wild mushroom resources， the risk of poisoning remains a severe public health challenge. Frequent poisoning by accidental ingestion of toxic wild mushrooms poses a grave threat to public health and social stability. Traditional safety education materials， such as posters and manuals， are often found lacking because two-dimensional images cannot capture the three-dimensional structure and spatial relationships of key identification features like annuli and volvas， resulting in a superficial understanding. These methods of identification lack interactivity and situational immersion， fail to simulate decision-making in real foraging scenarios， and their safety warnings remain abstract， scarcely triggering users’ embodied risk perception. Crucially， the absence of real-time， quantitative assessment tools for tracking cognitive processes and identification logic leads to inaccurate and subjective evaluation of training efficacy. To address these limitations， this study develops a VR-based simulation platform for wild mushroom identification and risk perception， using innovative immersive technology to strengthen public identification skills and safety awareness. [Methods] Built on a layered， modular architecture comprising hardware， data， software， application， and user layers， the platform adheres to the software engineering principle of “high cohesion and low coupling” to ensure stability and scalability. Key technologies include four core components: high-fidelity virtual scene construction using a hybrid approach of real-scene scanning and manual refinement， combined with physically based rendering and level of detail technology to balance image quality and performance， with ecological layout of forest scenes and functional design of kitchen scenes based on real data; a task-guided intelligent interactive VR system where users can grab， rotate， and scale mushroom models， complemented by an intelligent prompt mechanism triggered by either 2.5 seconds of focused attention on key features or spatial proximity of similar species for comparison embodied risk warning that uses audio–visual effects to simulate the consequences of poisoning and strengthen safety memory; and a quantitative evaluation model built on full-process operation data， incorporating identification accuracy， efficiency， observation detail， and logicality to realize process-oriented assessment. [Results] A controlled experiment was conducted with 60 participants with no background in botany or mycology. The subjects were randomly assigned to either an experimental group (n = 30)， which received at least 2 hours of VR platform training over one week， or a control group (n = 30)， which used traditional online learning methods. Post-test results showed significant improvements in both groups， but the experimental group achieved much greater gains—33.4 ± 9.1 points in theoretical tests and 39.5 ± 11.3 points in image recognition—than the control group’s 17.4 ± 8.7 and 16.3 ± 9.5 points， respectively. A strong positive correlation (r = 0.72) was observed between the experimental group’s identification accuracy and the duration of their observation of key features. Subjective feedback indicated high satisfaction， with immersion (4.8/5.0) and risk warning perception (4.7/5.0) receiving the highest ratings， confirming the platform’s effectiveness in improving memory and safety awareness. [Conclusions] This VR-based platform successfully integrates immersive environments， intelligent interaction， and process-oriented evaluation， effectively mitigating the shortcomings of traditional wild mushroom safety education. The platform significantly improves identification accuracy， safety awareness， and observation skills by facilitating situational cognitive experiences， active exploration opportunities， and emotional risk warnings. Its technical framework is highly transferable to other high-risk training fields such as food and drug safety， medical device identification， and industrial safety， featuring broad promotional value. Future developments will expand sample diversity， conduct long-term tracking of training effects， and optimize the platform with AI algorithms and haptic feedback devices， contributing to the overall improvement of social safety training efficiency.
Research prospects for smart laboratory safety based on cross-technological integration
YAO Zuofang;AO Yifang;ZHANG Jie;ZHANG Hanbing;YANG Junjie;SUN Yu;HE Juanxia;[Objective] With the rapid development of higher education in China， experiments are becoming increasingly diverse and new methods are constantly emerging. Experimental research is showing a trend toward interdisciplinary collaboration， and traditional safety management methods are proving inadequate for effective prevention of accidents， such as fires， explosions， and poisoning. In recent years， a series of laboratory accidents in universities have exposed shortcomings in the traditional management systems to identify and warn against dynamic risks. Consequently， integrating cutting-edge technologies， such as artificial intelligence， to establish an intelligent， proactive laboratory safety risk prevention and control system has become an urgent necessity for enhancing the effectiveness of laboratory safety management in higher education institutions. [Methods] To enhance the effectiveness of laboratory safety management and prevent various types of accidents， this study systematically reviews the current state of the application of the five-in-one technology—artificial intelligence， neural networks， image recognition， geographic information systems (GIS)， and visualization—in laboratory safety， and proposes the construction of an integrated technological framework comprising “AI-neural network-image recognition-GIS-visualization” integrated technological framework. Building upon this， a multitiered intelligent laboratory safety management framework comprising the following specific steps is designed: (1) a laboratory safety knowledge graph is constructed based on multisource data. The graph deploys the ALBERT-BiLSTM-CRF model for entity recognition and uses the Neo4j graph database for knowledge storage and associative reasoning; (2) an intelligent agent is developed for risk perception and digital-intelligent control. A closed-loop “perception-identification-decision-making” system is constructed， and the Dempster–Shafer evidence theory is integrated with dynamic risk assessment models and a cloud computing platform for real-time monitoring and intelligent discrimination of multidimensional information on personnel， equipment， environment， and procedures; (3) A university laboratory safety risk assessment and intelligent monitoring/early warning system is established， integrating multimodal sensing networks， dynamic knowledge graphs， and agent-driven decision-making to form a complete technical chain spanning data collection， risk modeling， and early-warning response. [Results] The five-in-one technology proposed in this paper offers an innovative approach to laboratory safety management in higher education institutions， reflecting the trend toward the deep integration of modern information technology and safety management. Although the proposed technology still faces challenges such as high implementation costs， data security risks， and a shortage of specialized personnel during roll-out， with continuous advancements in technologies， such as artificial intelligence， digital twins， and edge computing， safety inspection robots with enhanced intelligence， visualization， and automation capabilities are expected to be developed in the future. By integrating multimodal sensing， intelligent algorithms， and visual interaction， these robots will be capable of real-time monitoring of environmental parameters， equipment status， and personnel behavior， automatically performing risk assessments and issuing tiered alerts. This will ensure round-the-clock intelligent inspection support for laboratories. [Conclusions] This research not only drives the continuous development of laboratory safety management toward greater precision， proactivity， and universal accessibility but also provides a solid guarantee for the high?quality advancement of higher education and the safety of faculty and students during experimental research.
Scientific supervision of anesthetic and psychotropic drugs in local medical universities based on the PDCA cycle
LI Meirong;LI Dingxiang;HUANG Qingli;XU Hongyan;[Objective] Toxic and narcotic reagents are critical specialized substances widely used in pharmacology， toxicology， neuroscience， and other biomedical fields across medical colleges and research institutions. Due to their high toxicity， significant addictive potential， and elevated risk of abuse， they pose serious safety， regulatory， and ethical challenges. Managing these reagents constitutes a “safety red line” in university laboratories. In recent years， laboratory accidents related to the mismanagement of hazardous chemicals have increased， exposing deficiencies in current practices， such as unclear allocation of responsibilities， insufficient real?time monitoring， and fragmented procurement processes. These incidents underscore the urgent need to strengthen both regulatory frameworks and personnel professionalism to prevent abuse， diversion， and accidental exposure. [Methods] This study systematically reviews the major challenges in managing toxic and narcotic reagents in academic settings， including ambiguous approval workflows， a lack of digital tracking， and inadequate safety training. Drawing on the operational experience of the National Medical Products Administration Key Laboratory for Research and Evaluation of Anesthetic and Psychotropic Drugs at Xuzhou Medical University， we propose an integrated management model based on the PDCA (plan-do-check-act) cycle within the overarching framework of a “safety red line.” The model incorporates four iterative phases: planning (defining roles and standard operating procedures)， doing (implementing digital procurement and real?time inventory tracking)， checking (conducting internal audits and risk assessments)， and acting (correcting nonconformities and updating protocols). This paper elaborates on practical experiences and innovative strategies in reagent management and team development， emphasizing institutional mechanisms， technological integration (e.g.， role?based access control and blockchain?ready logging)， and human factors， such as safety culture and continuous education. [Results] The study demonstrates innovative approaches to building a safety management team tailored to the supervision of toxic and narcotic reagents. Through the PDCA cycle， a supportive environment has been established that enhances the safety and efficiency of teaching and research activities. Key outcomes include a clear hierarchical approval system (departmental review by academic affairs， science and technology， and security offices， followed by centralized qualification by the state assets management office)， fully information-based procurement， and mandatory documentation for regulatory submission to public security and drug administration authorities. The model provides strong institutional support， ensures regulatory compliance， and offers a replicable framework for other institutions. It emphasizes continuous training， clear allocation of responsibilities， and the use of digital tools for real?time monitoring and accountability. Since implementation， procedural violations have decreased significantly， and emergency response preparedness has improved. [Conclusion] Effective management of toxic and narcotic reagents requires a multifaceted approach integrating standardized processes， technological empowerment， and a people-oriented safety culture. The proposed PDCA?based model not only safeguards the lives and health of university faculty members and students but also ensures the smooth progression of laboratory activities. By reinforcing laboratory stability， it contributes to the broader social responsibilities of academic institutions. Future efforts should focus on continuous optimization， intelligent monitoring systems， and ongoing professional development to adapt to evolving regulatory demands and emerging research needs.
Construction and practice of an experimental project-driven total-factor collaborative safety access mechanism for university laboratories
BAI Li;LI Chunhui;WANG Peng;YU Hao;[Objective] University laboratories serve as the core carriers for scientific innovation and talent cultivation; however， they face significant safety challenges due to the complex nature of research and the high mobility of personnel. According to a statistical analysis of 137 laboratory safety accidents at Chinese universities and research institutes between 2004 and 2024， 62.04% of incidents stemmed from improper personnel operations. Additionally， over 80% involved a “lack of factor collaboration，” in which the failure to coordinate multiple safety elements expanded the scope of harm. Traditional management models often suffer from “single-point fragmented control，” in which personnel， hazardous materials， environmental conditions， and experimental projects are managed in isolation. This lack of synergy cannot address the dynamic， multidisciplinary nature of modern university research; therefore， a systematic governance mechanism is urgently required. This study constructs a “total-factor collaborative safety access mechanism” that integrates the social amplification of risk framework and synergistic theory to transform laboratory safety management from passive， fragmented control to active， systematic governance. [Methods] Based on the “order parameter” principle of synergistic and the environment， health， and safety integration model， this study constructs a closed-loop linkage mechanism characterized by “perception–assessment–linkage–feedback.” The core innovation lies in treating the “experimental project” as the system’s critical “order parameter.” The mechanism drives the precise adaptation of three other key access elements: personnel， items， and the environment. First， to ensure personnel access， a “three-level classification–dynamic adaptation” training system was developed. Based on the specific project’s risk level， personnel undergo stratified training (university， college， and laboratory levels). A dynamic reverification mechanism ensures that operator qualifications align with changing project risks. Second， for item access， a “full lifecycle–multidimensional tracing” system was implemented. RFID and QR code technologies were used to manage hazardous chemicals and equipment from procurement to disposal， ensuring strict compliance with project needs. Third， for environmental access， laboratories were classified into five categories (e.g.， chemical， biological， and mechanical) with a “planning–auditing–acceptance” control flow to ensure that physical conditions meet the safety requirements of the proposed projects. Finally， to assess overall project access， a quantitative risk assessment system comprising 4 primary and 12 secondary indicators was established. An information platform serves as the technical backbone， enabling real-time data sharing and automatically triggering safety protocols when project parameters change. [Results] The proposed mechanism was validated through practical application at Shandong University of Science and Technology. A specific case study involved the “nano-sulfide synthesis experiment” at the College of Chemical Engineering in September 2024. Due to a change in the experimental scheme involving the addition of hydrogen sulfide gas， the project’s risk level rose from “low risk” to “high risk.” The collaborative mechanism immediately triggered a dynamic reverification process for the 12 original operators. The assessment included a theoretical evaluation (40% weight) and practical operation (60% weight)， focusing on toxic gas handling and emergency response to leaks. The results showed that 11 operators passed the reverification; however， 1 operator failed the practical test for failing to check the air-tightness of the positive-pressure air breathing apparatus before use. Consequently， the system automatically suspended this operator’s laboratory access authority. The operator was required to complete 30 h of specialized remedial training and pass a secondary assessment to regain access. This case demonstrated the mechanism’s ability to identify specific unsafe behaviors and dynamically manage risks. [Conclusions] The “total-factor collaborative safety access mechanism” successfully overcomes the limitations of traditional siloed management by using the experimental project as the driving force for systemic safety. By integrating personnel， items， and environmental factors into a cohesive， project-driven framework， the mechanism achieves precise risk control and dynamic adaptation. Practical application proves that this approach effectively shifts safety management from “passive response after accidents” to “active defense before risks.” This study provides a replicable and scalable paradigm for safety governance in multidisciplinary comprehensive universities， particularly those with intensive， high-risk experimental activities in fields such as chemistry and biology.
Preparation of flexible pressure sensors for intelligent sensing terminals and interdisciplinary comprehensive experimental teaching design
XIA Shengyuan;GUO Baohong;SHEN Qingbin;SUN Hao;WANG Wu;[Objective] Rapid advances in intelligent wearables， mobile healthcare， and integrated energy systems have increased demand for flexible， highly sensitive， and environmentally friendly sensing terminals. Traditional pressure sensors， often based on metals or semiconductors， face significant limitations， including poor flexibility， limited sensitivity， complex fabrication， and recycling challenges， which hinder their use in next-generation intelligent systems. Concurrently， cultivating interdisciplinary talent with integrated innovation and practical engineering skills is urgent. However， current sensor-related experimental courses often lack comprehensive training that spans material synthesis， device fabrication， system integration， and application. This study aims to address these dual challenges by developing a high-performance， eco-friendly flexible pressure sensor and transforming the corresponding research outcomes into a structured interdisciplinary experimental teaching project， thereby bridging the gap between advanced research and engineering education. [Methods] A sandwich-structured flexible pressure sensor was designed and fabricated using reduced graphene oxide (rGO-impregnated fabric as the active sensing layer and laser-induced graphene (LIG) as the porous top and bottom electrodes. The rGO fabric was prepared via a simple soaking–thermal reduction process， in which a piece of cotton-linen blend fabric was immersed in a graphene oxide dispersion and subsequently thermally reduced at 200°C. The LIG electrodes were directly patterned onto polyimide (PI) films using a CO2 laser engraving system， creating a three-dimensional porous conductive network. The sensor was assembled by sandwiching the rGO fabric between two LIG/PI electrodes. The morphology and composition of the rGO fabric and LIG were characterized using scanning electron microscopy (SEM)， X-ray photoelectron spectroscopy， and Raman spectroscopy. The sensor’s electromechanical performance， including sensitivity and stability， was systematically evaluated using a universal testing machine coupled with a digital source meter. The sensor’s practical application potential was demonstrated by detecting static forces with standard weights and by monitoring dynamic physiological signals to capture human radial artery pulse waveforms. [Results] The fabricated sensor exhibited excellent overall performance. The rGO formed a continuous conductive coating on the fabric fibers， while the LIG exhibited a highly porous and interconnected structure， enabling efficient signal transduction. The sensor demonstrated a high sensitivity of 30.3 kPa-1 in the low-pressure range (0–5 kPa). It showed outstanding stability， with negligible performance degradation over 300 loading–unloading cycles at 5 kPa. The sensor could clearly distinguish static loads and reliably capture dynamic physiological signals. The pulse waveform displayed characteristic peaks， from which a heart rate of approximately 72 beats per minute was derived， consistent with the resting state of a healthy adult. This interdisciplinary experiment， integrating knowledge from electrical engineering， materials science， and biomedical engineering， was successfully implemented as a teaching module. It guided students through the complete research cycle， from design and fabrication to testing and application analysis. The teaching practice achieved important outcomes， including supporting student teams in obtaining National-level University Student Innovation Training Program projects and winning a provincial silver medal in the China International College Students’ Innovation Competition. [Conclusions] This work successfully developed an eco-friendly， low-cost， high-performance flexible pressure sensor based on rGO fabric and LIG porous electrodes. More importantly， it established an effective model for translating cutting-edge research into a comprehensive interdisciplinary experimental teaching project. This project addresses the shortcomings of traditional sensor experiments by offering students hands-on experience throughout the device development workflow. It effectively enhances students’ interdisciplinary integration capabilities， practical engineering skills， and innovative thinking， thereby offering a reproducible and scalable educational paradigm for cultivating interdisciplinary talent under the emerging engineering education initiative.
Harnessing STA-IR-MS for the experimental teaching of energy materials:A case study on the thermal stability of lithium-based polymer electrolyte materials
SHI Chuqi;ZHANG Weibin;YANG Qin;PAN Yikun;[Objective] The growing prominence of advanced analytical techniques in materials science has underscored the need to integrate coupled instrumentation systems into graduate education. Introducing synchronous thermal analysis (STA) combined with infrared spectroscopy (IR) and mass spectrometry (MS) into experimental teaching is an important initiative for cultivating interdisciplinary talent capable of addressing complex research challenges in energy materials development. [Methods] This study systematically examines the architectural principles and operational mechanisms of STA-FTIR-MS and STA-FTIR-GC-MS (gas chromatography-MS) coupled technologies， demonstrating their distinctive advantages in energy materials education through an integrated teaching experiment focused on the Thermal Stability of Lithium-based Polymer Electrolyte Materials.” The experiment was designed to cultivate comprehensive skills in multimodal data acquisition and interpretation and was structured around four fundamental components. These include establishing learning objectives to familiarize students with the working principles of STA， FTIR， MS， and GC–MS systems while demonstrating correlations among mass loss， thermal events， and gas evolution to develop capabilities in analyzing complex multidimensional datasets; implementing experimental procedures in which students prepare lithium-based polymer electrolyte samples and subject them to programmed heating under an inert atmosphere， with the STA module recording real-time mass changes and enthalpy variations and evolved gases simultaneously transferred via a heated transfer line to FTIR and MS/GC-MS systems for compositional identification and quantification; facilitating integrated data analysis in which trainees learn to synchronize thermogravimetric data with FTIR spectral profiles and MS/GC–MS to establish causal relationships between thermal decomposition stages and specific gas release events， while enabling direct comparison of the analytical capabilities of MS and GC-MS detection systems in characterizing thermal decomposition processes; and conducting teaching assessments through evaluation rubrics that examine laboratory performance， data interpretation accuracy， and final report quality， with particular emphasis on critical reasoning skills and scientific communication abilities. [Results] The integrated experiment enabled students to quantitatively correlate mass loss events with specific gas evolution profiles through synchronized data analysis. During programmed heating， participants identified characteristic decomposition stages—such as solvent evaporation， polymer chain degradation， and inorganic salt decomposition—by cross-referencing STA curves with IR absorption bands (e.g.， C＝O stretching at 1740 cm-1 for carbonate decomposition) and MS ion fragments or GC–MS chromatographic peaks. This approach revealed clear structure-property relationships between material composition and thermal behavior while demonstrating the complementary strengths of MS (rapid detection) and GC-MS (superior resolution for complex mixtures). Assessment based on operational proficiency， data interpretation accuracy， and scientific reporting demonstrated considerable improvement in students’ ability to extract meaningful insights from multidimensional datasets， propose mechanistic explanations for observed phenomena， and communicate findings effectively. The experiment has established a new pedagogical model for advanced instrumental training within the New Engineering Education framework， emphasizing critical thinking and technical problem-solving. [Conclusions] The integration of STA-IR-MS/GC-MS coupled technologies into energy materials laboratory teaching represents a successful reform that goes beyond traditional single-technique experiments. Through this thoughtfully designed project， students gained hands-on experience with state-of-the-art instrumentation and developed a researcher’s mindset. They learned to navigate analytical complexity， reconcile complementary datasets， and construct evidence-based scientific narratives. This pedagogical approach effectively bridges the gap between theoretical knowledge and practical research skills， fostering the interdisciplinary competencies required for innovation in advanced materials science. Moreover， this model offers a scalable framework for future curriculum development and can be adapted to other emerging fields where coupled characterization techniques are essential.