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Research and practice on the operation and management of the deep underground laboratory: A case study of China Jinping Underground Laboratory
LI Yasong;KANG Kai;LI Zaiqiang;[Objective] Deep underground laboratories use thick rock formations to block cosmic rays, thereby providing an ultra-low background radiation experimental environment for frontier fields such as particle physics, nuclear physics, and life sciences. They are ideal venues for cutting-edge basic scientific research. These laboratories exhibit unique characteristics in operational management, including extreme environmental conditions, interdisciplinary features, and complex risks. This paper takes the China Jinping Underground Laboratory as the research object. As a major national science and technology infrastructure facility, the laboratory possesses the deepest rock cover, the largest underground space, and the lowest cosmic ray flux in the world. Located 2,400 m underground, its total construction area and space volume are 40,000 m2 and 300,000 m3, respectively. It has attracted scientists from more than ten leading research institutions in China, including Tsinghua University, Shanghai Jiao Tong University, Beijing Normal University, and Sichuan University. [Methods] To address the core issues in laboratory operation and management, this research focuses on analyzing the key operational management challenges based on the deep-earth characteristics of Jinping Laboratory, such as pre-management and settlement guarantee for project access, long-term maintenance of the extremely low radiation background experimental environment, safety risk management and control during laboratory operation, and the construction and management of the operation support team. This paper proposes a series of efficient and advanced management measures for project access, background environment maintenance, safety risk management and control, and support team construction. These measures include:(1) a scientific approval and resource scheduling system with hierarchical coordination, covering project approval, access, exit, and equipment installation;(2) low background safeguard measures, including radon suppression, dedicated ventilation, and radiation monitoring;(3) a safety system tailored for deep underground scenarios, supporting experimental operation, earthquake monitoring, and fire prevention; and(4) a professional operation management and support team for integrated management and implementation support. [Results] Given the unique advantage of 2,400 m vertical rock coverage, Jinping Laboratory faces the challenge of reconstructing the management paradigm in an extreme deep-earth environment while creating an “ultra-low background” research environment. By systematically analyzing and addressing prominent issues in core dimensions such as scientific collaboration, background maintenance, safety control, and team building, this paper develops an operational management solution suitable for deep underground scenarios. This solution ensures safe, efficient, and reliable laboratory operation, helps increase high-level experimental results, and makes lab operation and management more efficient. [Conclusions] This research transcends the management thinking of traditional ground-based research facilities. Guided by research needs, based on safety and stability, and supported by technological innovation, it realizes the in-depth integration of scientific management and engineering operation and maintenance. The operational management practice of Jinping Laboratory will not only lay a solid foundation for its cutting-edge research but also contribute to the professional and refined operation and management of major science and technology infrastructure and provide a practical Chinese-style paradigm for operating and managing deep-earth laboratories worldwide.
Multisensor fusion-based localization system and experimental design
YIN Shu;SUN Hu;FU Minglei;ZHANG Wenan;[Objective] With the rapid development of intelligent robotics and autonomous driving technologies, accurate and robust vehicle localization has become one of the fundamental prerequisites for environmental perception, motion planning, and autonomous decision-making. However, in practical applications, localization systems operating in complex environments are frequently affected by abnormal measurements, multipath interferences, and sensor uncertainties, which considerably degrade localization accuracy and system reliability. In particular, Global Navigation Satellite System(GNSS)–based positioning methods are highly susceptible to environmental disturbances in urban canyons, indoor–outdoor transition zones, and occluded scenarios, resulting in severe localization drift or temporary signal loss. Therefore, multisensor fusion has become an important research direction for improving the localization accuracy, robustness, and environmental adaptability in intelligent unmanned systems. To enhance students' understanding of vehicle localization technologies and the theoretical foundations of fusion-based estimation, a multisensor-based localization experimental platform was designed as part of robotics-related courses, focusing on a collaborative localization system integrating multiple heterogeneous sensors. [Methods] This experiment introduces a fusion localization framework for an intelligent unmanned vehicle that integrates ultra-wideband(UWB), GNSS, and inertial measurement unit(IMU) sensors. This experiment aims to bridge the gap between theoretical learning and engineering implementation by enabling students to understand the practical workflow of sensor fusion localization, including data acquisition, state estimation, information fusion, and robustness analysis under disturbed environments. Specifically, GNSS measurements provide global positioning information, UWB measurements compensate for localization degradation under signal occlusion and interference, and IMU measurements enable continuous motion-state propagation and short-term state estimation. By exploiting the complementary characteristics of these heterogeneous sensor measurements, the proposed framework effectively integrates multidimensional information to improve localization accuracy, reliability, and environmental adaptability. Through the collaborative utilization of multisource sensor data, the system leverages the complementary properties among different measurements, thereby improving localization accuracy, estimation consistency, and robustness against abnormal observations and environmental disturbances. The experiment also enables students to gain a deeper understanding of the practical implementation of fusion filtering algorithms, including state prediction, measurement update, and uncertainty propagation in unmanned vehicle localization tasks. In addition, students can intuitively analyze the influence of sensor noise, measurement uncertainties, and environmental interference on localization performance through experimental observations and comparative analyses. [Results and Conclusions] Experimental results demonstrate that the proposed multisensor fusion localization scheme can effectively suppress the influence of abnormal measurements and compensate for localization degradation caused by environmental interference. Compared with single-sensor localization methods, the proposed fusion framework exhibits superior localization accuracy, stronger robustness, and improved stability under complex environments. The designed experimental platform not only provides an effective educational tool for robotics and intelligent vehicle courses but also offers practical guidance for understanding the engineering applications of multisensor fusion localization in autonomous systems.
Design of a visible light communication system for underwater wireless data transmission of UUV
WANG Zijian;SHAN Mingguang;[Objective] Unmanned underwater vehicles(UUVs) are widely used in marine environmental monitoring, underwater resource exploration, inspection, and cooperative operation. These applications require short-range underwater links with high transmission rates, high reliability, and strong anti-interference capability. In practical underwater systems, wireless charging and data transmission are often integrated on the same platform. Under this condition, conventional electromagnetic communication is easily affected by interference generated by wireless power transfer equipment, which limits transmission stability. By contrast, underwater wireless optical communication based on visible light offers advantages such as high bandwidth, low latency, and strong immunity to electromagnetic interference, making it suitable for short-range underwater transmission. However, in compact bidirectional underwater systems, local transmitting light sources may introduce backscatter self-interference, whereas the co-existence of communication and wireless charging modules may further cause electromagnetic coupling interference. To address these problems, this study proposes a visible light communication system for underwater wireless data transmission in UUVs under the co-existence of wireless charging and communication. [Methods] An underwater optical communication channel model is first established to analyze light propagation in water, with emphasis on scattering effects and backscatter self-interference in short-range transmission. Based on the channel characteristics, a combined interference suppression method is proposed. In the optical path, wavelength division is used to reduce mutual interference between transmitting and receiving channels, and narrowband optical filters are employed at the receiver to suppress undesired spectral components and weaken optical scattering interference. In the circuit and structural design, electromagnetic shielding is adopted to reduce the electromagnetic coupling interference introduced by the wireless charging system and surrounding electronic modules. On this basis, a miniaturized low-power bidirectional underwater visible light communication experimental system is implemented. The system employs an LED array as the optical transmitter and adopts a field-programmable gate array(FPGA) as the core digital processing platform for communication control and data processing. In addition, a packet loss retransmission mechanism based on the user datagram protocol(UDP) is introduced to improve transmission reliability in practical applications. Finally, experiments are carried out to evaluate the performance of the proposed system under the co-existence of underwater wireless charging and visible light communication. [Results] The experimental results show that the proposed system can achieve stable short-range underwater wireless data transmission for UUVs in a complex interference environment. The combined optical-electrical interference suppression method effectively reduces the influence of optical backscatter and electromagnetic coupling interference, thereby improving the stability of signal reception and data recovery. Within a communication distance of 20 cm, the system achieves stable bidirectional transmission at a rate of 2 Mb/s, and the measured bit error rate is only 8.07×10–7. The system maintains high transmission quality under the co-existence of wireless charging and communication. In addition, the UDP-based packet loss retransmission mechanism enhances the reliability of data transmission in practical applications. The experimental prototype also verifies the feasibility of miniaturized and low-power implementation, which is favorable for integration into compact underwater platforms. [Conclusions] By combining underwater channel analysis, wavelengthdivision light source design, narrowband optical filtering, electromagnetic shielding, and UDP-based packet retransmission, the proposed system improves communication reliability under wireless charging and data transmission. This study provides a practical reference for short-range high-speed underwater communication and energy-information collaborative transmission in UUV platforms.
Development of analog material and model experimental design based on the strength reduction method
AN Baixin;KONG Chao;QIU Wenge;ZHANG Jiezhen;[Objective] The rapid development of rail transit has led to increasingly complex tunnel spatial layouts, substantially elevating construction and operational risks. The strength reduction method has been widely applied in tunnel engineering; however, existing studies have predominantly relied on numerical analysis, whereas field evidence and experimental validation remain limited. To address these issues, we developed a rock–soil analog material in which shear strength parameters can be quantitatively reduced through controlled heating. By integrating laboratory-scale physical model tests with numerical simulations, we investigated the potential failure modes of tunnel groups under surrounding-rock strength degradation as well as the associated displacement–stress evolution. Taking the Hongyancun tunnel group in Chongqing, China, as an engineering background example, we aimed to provide targeted guidance for the design and risk management of tunnel groups. [Methods] The analog material was prepared using paraffin wax as the binder and quartz sand together with 400-mesh barite powder as aggregates. Temperature was introduced as an external control variable, enabling controlled reductions in the material's shear strength parameters through heating. The mechanical properties of the analog material were characterized using a ZJ-type strain-controlled direct shear apparatus and a universal testing machine, based on which the mass ratio of paraffin wax:quartz sand:barite powder was determined as 6:56.4:37.6. To achieve stable and precise control while heating the physical model, an in-house intelligent temperature-control device was developed for accurate heating and temperature regulation. Numerical simulations and laboratory excavation–heating tests on a tunnel group were conducted to analyze the failure patterns and the evolution of displacement and stress fields following quantitative reductions in the surrounding-rock strength. During the tests, displacement transducers and embedded strain blocks were installed to monitor displacement and stress responses throughout excavation and heating. [Results] Based on the strength-reduction concept, an analog material was successfully developed in which cohesion and internal friction angle decrease proportionally with increasing temperature, allowing the mechanical behavior of Grade III–V surrounding rock to be effectively reproduced. The developed intelligent temperature-control device enabled accurate heating and stable temperature regulation of the model material. The tunnel-group heating tests indicated that the sustained development of damage in the pillar rock(intervening rock mass) was the primary factor triggering collapse of the tunnel group. By contrast, even when localized failure occurred in noncritical regions, it typically did not directly lead to overall instability of the tunnel group. [Conclusions] Herein, we propose and validate a temperaturecontrolled physical modeling approach for quantitatively understanding reductions in the rock strength surrounding tunnels. The developed analog material and temperature-control system effectively capture potential failure zones and instability modes of tunnel groups under strength degradation during excavation and operation. The findings provide experimental evidence and practical references for optimizing the spatial configuration of complex tunnel groups, identifying critical locations, and managing risks throughout the construction–operation lifecycle.
Microgrid energy management strategy based on two-stage deep reinforcement learning
LU Lingxia;HU Mingjie;LUO Weiye;YU Miao;[Objective] With the gradual transformation of the global energy structure and the rapid development of renewable energy technologies, microgrid technology has emerged as an important and rapidly growing area in the energy sector. As the core of microgrid operation and control, the energy management system ensures the efficient use of renewable energy and the stable operation of microgrids through precise monitoring and intelligent control. Traditional energy management methods struggle to effectively handle the complex interrelationships among variables in microgrids, whereas deep reinforcement learning(DRL) enables intelligent decision-making by learning through interaction with the environment and adjusting strategies based on feedback signals. To address the high exploration cost and low training efficiency of existing DRL algorithms, this study proposes a microgrid energy management strategy based on a two-stage DRL framework. [Methods] The proposed strategy includes two stages: offline and online. First, in the offline stage, linear programming is used to obtain the optimal scheduling results of typical days to construct an expert experience library, and imitation learning is then used to pretrain the agents. This stage involves extracting key information from historical data, such as photovoltaic power, wind power, load demand, and electricity prices, and transforming the data into state–action pairs, thereby forming the pretraining foundation for the agents. Subsequently, in the online stage, the agents interact with the real environment to learn the optimal scheduling strategies for nontypical days. During this stage, having accumulated sufficient knowledge in the offline stage, the agents can significantly improve the environmental tracking accuracy and operational economy. A cliff-walk reward mechanism is introduced to ensure that the agents immediately stop exploring after making decisions that violate constraints, thereby reducing the training cost associated with invalid exploration. Concurrently, the proximal policy optimization(PPO) algorithm is introduced to meet the requirements of continuous action spaces and further improve the performance of the agents. [Results] The proposed algorithm has been validated in a typical microgrid system with three PV stations, one wind turbine, one storage system, and flexible loads. The simulation results show that the convergence speed is significantly improved, and the average daily operating cost is reduced by approximately 14% compared with that of single-stage PPO. A comparison with Double Deep Q-Network, Dueling Deep Q-Network, and Distributed Dueling Deep Q-Network further demonstrates the advantages of the proposed method in achieving optimal performance. [Conclusions] In the two-stage DRL, pretraining in the offline stage enables agents to learn general features and strategies, enabling them to adapt more quickly to new environments and tasks in subsequent missions. Training in the online stage helps agents avoid overfitting to task-specific training data, reducing reliance on such data, lowering the risk of overfitting, and ultimately endowing the strategies with better generalization capability and robustness. Overall, compared with single-stage DRL-based microgrid energy management algorithms, the proposed two-stage DRL-based strategy significantly improves the training efficiency and optimal performance of the agents.
Design of experiments for remote sensing image compression integrating latent space diffusion and residual compensation
ZHANG Lili;LIU Jinhe;GAO Yang;[Objective] Remote sensing images are rich in multi-scale objects， dense high-frequency textures， and distinct structural boundaries. Under low-bitrate compression， these images often suffer from distortion， over-smoothing， and loss of texture details due to quantization errors and strict bit budget constraints. This degradation collectively impairs rate–distortion performance and perceptual quality. Although existing deep learning-based image compression methods perform well at moderate bitrates， maintaining texture fidelity and structural consistency in complex remote sensing scenes under stringent bitrate constraints remains a significant challenge. Therefore， efficient low-bitrate remote sensing image compression is crucial for reducing storage costs， enhancing transmission efficiency， and supporting real-time downstream applications. [Methods] A generative modeling framework for remote sensing image compression is proposed that integrates adaptive convolution， a latent space diffusion model， and a latent residual prediction mechanism. The architecture consists of an encoder， a decoder， and a quantization and entropy model. Adaptive convolution is embedded in the encoder to modulate feature extraction based on local characteristics， thereby more effectively representing multi-scale objects and heterogeneous textures and improving latent compactness within a limited bit budget. A diffusion model is introduced into the latent space to learn more expressive latent distributions， enhancing the modeling of diverse texture patterns and complex structures. During reconstruction， this model alleviates over-smoothing at low bitrates and facilitates plausible detail recovery. In addition， a latent residual prediction module explicitly compensates for quantization errors by estimating correction terms from latent variables and injecting them into the reconstruction pathway. This process suppresses quantization-induced pseudo-textures and improves the recovery of edges and fine structures. The framework is trained end-to-end to balance bitrate and reconstruction quality， and its performance is evaluated from both rate–distortion and perceptual-consistency perspectives. [Results] Experiments conducted on the Dataset for Object Detection in Aerial Images(DOTA) and UC-Merced datasets demonstrate that HiLD-RS consistently outperforms conventional codecs and representative learned baselines. On DOTA， HiLD-RS achieves superior rate–distortion performance compared to strong learned baselines (e.g.， MGMNet， Cheng2020， and ELIC)， delivering approximately 6.1%–40.8% average bitrate savings (BD-rate reductions) and 0.27–2.16 dB average quality improvements in BD-peak signal-to-noise ratio (PSNR) over overlapping operating ranges. For instance， HiLD-RS achieves 33.67 dB at 0.175 1 bpp， whereas ELIC achieves 32.38 dB at 0.1988 bpp， corresponding to an 11.9% bitrate reduction while providing a 1.29 dB PSNR gain. Furthermore， HiLD-RS improves multi-scale structural similarity from 15.6543 to 16.8613 and reduces learned perceptual image patch similarity from 0.2411 to 0.2385， indicating simultaneous improvements in structural similarity and perceptual quality. Compared with traditional codecs such as Better Portable Graphics(BPG) and JPEG2000， HiLD-RS yields an even greater reduction of approximately 60% in BD-rate with approximately 4–4.6 dB higher PSNR. Overall， these results suggest that combining a latent diffusion prior with explicit decoder-side compensation can concurrently improve fidelity and perceptual quality under low-bitrate constraints， enabling more stable preservation of thin structures and high-frequency texture details. [Conclusions] HiLD-RS is an end-to-end framework for low-bitrate remote sensing image compression that integrates latent space diffusion modeling and decoder-side residual compensation. By jointly leveraging adaptive convolution， latent diffusion modeling， and residual compensation， the method effectively mitigates detail loss and quantization artifacts， substantially improving reconstruction quality for complex remote sensing scenes. The approach demonstrates strong generalization across various bitrates and scene types， consistently surpassing mainstream methods under identical settings. Performance varies with diffusion-step configurations and scene characteristics， highlighting the importance of scenario-adaptive parameter selection.
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Study on “digital intelligence + refinement” management mode for architectural laboratories
FEI Ying;[Objective] Architectural laboratories play a central role in practical teaching and scientific research in architecture， urban planning， and related disciplines. However， their management suffers from challenges related to complex spatial utilization patterns， diversity of used professional equipment， and discipline-specific safety risks. Traditional management modes are characterized by fixed partitioning， manual registration， and regular maintenance， which lead to low space utilization， a high idle rate of large-scale instruments， lagging early safety warnings， and poor adaptation to interdisciplinary experimental needs. To solve these problems， this study developed an integrated “digital intelligence + refinement” management mode for architectural laboratories. It aimed to break through efficiency bottlenecks with digital technologies and resolve landing pain points with refined systems to achieve the collaborative optimization of space utilization， equipment utilization， and safety management and provide a replicable reference for the management upgrading of similar laboratories in universities. [Methods] This research adopted a combined approach of case comparison and empirical iteration. First， through case comparison， management practices of architectural laboratories in comprehensive universities， engineering universities， and professional art academies were analyzed to identify common problems and disciplinary characteristics. Second， by taking the School of Architecture at Soochow University as the empirical object， a phased iterative practice was adopted. In the first stage， a building information modeling (BIM)-based three-dimensional (3D) modeling platform was built to link equipment parameters， operational procedures， and spatial demand rules. In the second stage， an intelligent closed-loop safety system， including intelligent cabinets for hazardous chemicals， artificial intelligence (AI) visual monitoring， and access control linkage， was deployed to realize whole-process traceability and real-time risk identification. In the third stage， refined supporting mechanisms were established， such as flexible spatial division， hierarchical safety responsibility， equipment full-life-cycle management， and professional team training. Mode effectiveness was dynamically evaluated and optimized using indicators such as the space utilization rate， safety incident rate， and equipment sharing rate. [Results] The empirical results showed that the proposed “digital intelligence + refinement” mode achieved significant application outcomes. BIM and Internet of Things technologies enabled dynamic spatial scheduling and real-time environmental monitoring. The waiting time for equipment in the model laboratory was reduced from more than 2 hours to 25 minutes， and the space could be quickly reorganized within 15 minutes to allow multi-scenario experimentation. In terms of safety management， the intelligent monitoring system accurately identified high-frequency， risky behaviors in architectural experiments with a recognition accuracy of 98% and realized graded early warning and closed-loop disposal. The standardized management of hazardous chemicals with low toxicity and high volatility eliminated illegal storage and misuse. For equipment management， a digital ledger documenting purchased and self-developed instruments was established to enable full-life-cycle tracing and priority-based maintenance， greatly reducing the failure rate of self-developed devices and improving the overall utilization rate of instruments. Meanwhile， the refined training and assessment system enhanced the comprehensive competence of technical staff in BIM operation， equipment calibration， and safety control， ensuring the sustainable implementation of digital systems. [Conclusions] The “digital intelligence + refinement” integrated management mode effectively solves the prominent issues in the management of architectural laboratories. It forms a 3D integrated system supported by digital technologies， guaranteed by refined systems， and adapted to discipline-specific scenarios， which considerably improves space utilization， reduces potential safety hazards， and optimizes resource allocation. The phased， iterative implementation path is especially suitable for architectural laboratories with complex functions and difficult reconstruction. In the future， the application of generative AI and digital twins can be further explored to achieve virtual–real integration， remote monitoring， and intelligent prediction， continuously improving the intelligence level and service capacity of architectural laboratory management.
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Long-term operation mechanism for the dual prevention system in university laboratories
FENG Lujun;ZHAO Yanwei;CHEN Guoliang;[Objective] University laboratories are high-risk sites with concentrated hazardous sources. In recent years， frequent laboratory safety accidents have revealed major weaknesses in safety management， including incomplete risk identification， ineffective implementation of control measures， and failure to rectify identified hazards. Under the mandatory requirements of national education authorities， most universities have established an institutional framework for a dual prevention system covering risk-based hierarchical control and safety hazard investigation and treatment. Nevertheless， a widespread bottleneck emerges in practical operation: the system suffers from “formal construction without effective application，” manifested as disjointed risk identification and on-site conditions， decoupled safety hazard inspection and rectification， and derailed accountability implementation and assessment. Existing domestic studies mostly focus on the structural construction of the dual prevention system， while systematic research on long-term stable operations and self-adaptive optimization is lacking. To fill this research gap， this paper aims to answer three core questions: where the driving force of the system originates， how to sustain its stable operation， and how to continuously improve its management efficiency. [Methods] This paper innovatively couples incentive theory， systems theory， and plan-do-check-act (PDCA) cycle theory to construct a three-level progressive framework consisting of motivation， guarantee， and optimization layers. Additionally， it proposes a five-in-one collaborative mechanism including motivation stimulation， technical support， process control， collaborative supervision， and continuous optimization. In the motivation layer， a grid-based accountability system， multidimensional incentive policies， and a near-miss incident no-blame reporting mechanism are designed to encourage internal participation from teachers and students. The guarantee layer integrates digital intelligent management platforms， artificial intelligence (AI) visual recognition， and Internet of Things real-time monitoring as technical support; builds a nested PDCA dual-cycle model combining annual macro and daily micro cycles for process control; and establishes a three-dimensional supervision network composed of school cross-inspection， third-party independent audit， and regular safety conferences. The optimization layer forms an endogenous experience explicit transformation channel through quantitative effectiveness evaluation， feedback from typical accident cases， and institutionalization of grassroots management experience. A two-year pilot test was carried out in chemical engineering laboratories of Nantong Institute of Technology to verify the practical effect of the proposed mechanism， with multiple quantitative indicators set for comparative analysis before and after implementation. [Results] The pilot operation data demonstrate remarkable optimization effects brought by the long-term operation mechanism. The average safety hazard rectification cycle is shortened from 12 to 7 days， a reduction of nearly 40%; the recurrence rate of repeated safety hazards drops sharply from 27% to 6%; and the number of voluntarily reported near-miss incidents rises from 2 to 15， representing considerable improvement in active safety awareness. In terms of personnel safety literacy， the standardized safety behavior score of teachers and students increases from 72 to 91， the awareness rate of safety management regulations rises from 67% to 89%， and the recognition proportion of individual main safety accountability grows from 69% to 87%. The mechanism effectively resolves the dilemma of superficial system operation caused by accountability vacuum， formalized inspection， mismatched resource allocation， insufficient safety culture， and isolated safety data. Meanwhile， the pilot reveals restrictive factors， including insufficient sustained funding for intelligent platforms and uneven execution efficiency across individual laboratories. [Conclusions] The three-layer framework and five-in-one collaborative mechanism constructed in this study can provide systematic theoretical support and replicable practical paths for universities to realize intrinsic laboratory safety. They fundamentally resolve the long-standing pain point of “built but unused” in the dual prevention system by endowing the system with endogenous driving force， standardized operation guarantee， and self-evolution capacity. Subsequent research will develop quantitative evaluation tools to measure the long-term operational effectiveness of the dual prevention system， conduct cross-university empirical research， and build an AI-driven dynamic risk early warning model to further improve the whole-chain laboratory safety governance system of higher education institutions.
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Study on the “four-chain integration” construction of experimental centers for economics and management at “Double First-Class” universities
DAI Yue;WANG Ke;[Objective] Under China’s “Double First-Class” Initiative for university establishment， experimental teaching demonstration centers for economics and management are expected to play a dual role in fostering first-class talent cultivation and promoting first-class scientific research transformation. However， a critical structural contradiction has emerged: the disconnect between the education， industrial， innovation， and talent chains. This disconnect manifests as outdated experimental curricula that lag behind the rapidly evolving demands of the digital economy， low efficiency of transforming scientific research achievements into teaching content， and superficial university–enterprise cooperation. This study aims to explore how the “four-chain integration” theoretical framework can be systematically applied to address these dilemmas and propose a replicable development model for experimental teaching centers in economics and management. [Methods] This study adopted a case study approach combined with theoretical analysis. First， it systematically decomposed problems pertaining to experimental teaching demonstration centers considering three dimensions: goal orientation， collaborative mechanism， and benefit-sharing mechanism. Second， it developed an innovative framework for “four-chain integration，” encompassing four strategic pathways: goal reconstruction， practical system innovation， collaborative mechanism optimization， and shared ecosystem building. The study considered the Experimental Teaching Center of Economics and Management at Nanjing University of Information Science and Technology as a case for analysis. It thoroughly analyzed the center’s practical measures， including co-building experimental courses with leading enterprises such as the JD Group， establishing a special fund to transform scientific research results into experimental courses， integrating artificial intelligence (AI) into business curricula by developing digital intelligence course modules， building a virtual simulation experimental teaching platform， and implementing a “dual appointment and dual assessment” system for teachers. Data were obtained from the center’s operational records， student achievement statistics， competition results， and feedback from cooperative enterprises over multiple years. [Results] The study offers several key findings. First， the “four-chain disconnect” is identified as a deep-rooted obstacle to the high-quality development of experimental teaching demonstration centers. Second， the proposed “four-chain integration” framework effectively addresses this disconnect. The case study results demonstrate important outcomes. (1) In terms of education-chain to industrial-chain integration， the co-built experimental classes have embedded real business scenarios into the talent cultivation process. (2) Regarding innovation-chain to education-chain transformation， the center’s annual curriculum update rate exceeds 10%， with four teaching cases incorporated into the master’s program in accounting and new provincial-level key textbooks published. (3) For talent-chain quality improvement， students supported by the center have achieved remarkable results. These achievements include five national-level and five provincial-level “College Student Innovation and Entrepreneurship Training Program” projects in 2022 alone as well as multiple first prizes in national competitions such as the China University Business Elite Challenge and the China Graduate Enterprise Management Innovation Competition. (4) In terms of AI-empowered teaching innovation， the center has developed digital intelligence course modules and co-constructed simulation teaching platforms， considerably enhancing students’ digital literacy. (5) The center’s practices have achieved significant recognition and demonstration impact， as evidenced by reports from mainstream media such as Guangming Daily， visits from more than ten universities， and receipt of provincial- and university-level teaching achievement awards. [Conclusions] This study concludes that “four-chain integration” is a core strategy for achieving the connotative development of experimental teaching demonstration centers in economics and management. Successful implementation requires strategic top-level design， innovative operational mechanisms， and execution-level resource synergy. The case of Nanjing University of Information Science and Technology validates the effectiveness and replicability of this model. By transforming the experimental teaching demonstration center from a traditional teaching service unit into a platform for regional innovation and integrating talent cultivation， scientific research transformation， and industrial services， “four-chain integration” offers a feasible pathway for upgrading experimental teaching demonstration centers into “Double First-Class” universities.
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Multi-Agent Collaborative Construction of Economics and Management Philosophy and Social Science Laboratories in Industry-Specific Engineering Universities
JIANG Yong;[Objective] Since the Ministry of Education launched its first batch of philosophy and social science laboratories in 2021， such laboratories have become pivotal platforms for research paradigm transformation and the construction of China’s autonomous knowledge system. However， industry-specific engineering universities specializing in geology， mining， and petroleum face formidable structural challenges in this initiative. Their dominant natural science and engineering orientation leaves philosophy and social science disciplines comparatively underdeveloped， yet the industries they serve pose urgent interdisciplinary questions demanding deep integration across the sciences， engineering， and the social sciences. This study addresses this gap by asking: How should such universities construct philosophy and social science laboratories with distinctive sectoral characteristics， and what are the underlying mechanisms， evolutionary pathways， and institutional safeguards? [Methods] This paper introduces Multi-Agent System (MAS) theory based on Large Language Models (LLMs) as both an analytical lens and an organizational design metaphor and constructs a novel DTG (Discipline Agent–Technology Agent–Governance Agent) three-dimensional collaborative model. Discipline Agents represent the diverse disciplinary contributors spanning economics， management， and industry-specific engineering fields， each maintaining independent knowledge bases while engaging in structured cross-disciplinary knowledge exchange. Technology Agents represent intelligent infrastructure functioning as catalysts that bridge disciplinary boundaries through data fusion and method migration. Governance Agents represent institutional mechanisms responsible for coordinating multi-stakeholder interests and monitoring performance. Their interactions operate through a hybrid centralized-distributed structure combining top-down strategic coordination with bottom-up disciplinary self-organization. The framework is empirically grounded through a case analysis of the Ministry of Education’s Social Science Laboratory of Mineral Resources Security Governance at China University of Geosciences (Beijing). [Results] The study yields four sets of findings. First， it identifies four structural dilemmas: disciplinary ecosystem imbalance manifested as a “strong engineering， weak humanities” resource allocation contradiction; superficial interdisciplinary integration that remains episodic rather than institutionalized; technology-research disconnection where engineering platforms fail to penetrate social science research paradigms beyond instrumental application; and governance fragmentation caused by departmental silos impeding cross-unit collaboration. Second， the DTG model reveals three operational mechanisms: a knowledge production mechanism driven by heterogeneous agent collision and fusion with Technology Agents serving as boundary spanners; an organizational coordination mechanism coupling micro-level self-organization with macro-level strategic oversight through a three-tier governance structure; and an evolutionary dynamics mechanism propelled by endogenous disciplinary imperatives and exogenous strategic， policy， and technological drivers. Third， the case analysis demonstrates three distinctive features of industry-embedded construction: an industry-problem orientation anchoring research in sectoral challenges; an engineering technology reverse-feeding mechanism converting technical advantages into social science data assets and methodological tools; and a government-industry-university-research integration pathway establishing closed-loop research cycles. Fourth， the study proposes a four-stage evolutionary model comprising a disciplinary foundation stage for anchor discipline identification， a technology empowerment stage for building an Industry Data Hub and LLM-powered research assistant platforms， a collaborative integration stage for cross-disciplinary team formation through dual principal investigator arrangements and disciplinary bridge-builder programs， and an ecosystem construction stage for building multilayer open innovation networks with tripartite outputs spanning theoretical innovation， policy advisory， and social services. [Conclusions] This paper contributes to the field in three respects. Theoretically， the DTG model transcends prevailing “technology instrumentalism” by reconceptualizing intelligent technologies as organizational mechanisms reshaping interdisciplinary knowledge production. Practically， the four-stage evolutionary model demonstrates that the “strong engineering， weak humanities” profile contains latent comparative advantages unavailable to comprehensive universities. Empirically， the MOE Social Science Laboratory of Mineral Resources Security Governance case validates the DTG framework’s explanatory and practical value. The study recommends establishing differentiated evaluation standards for industry-specific universities， promoting sectoral data sharing between government agencies and laboratories， creating interdisciplinary talent programs including bridge-builder positions， and reforming evaluation systems to incorporate originality， interdisciplinarity， and practical impact as core assessment dimensions.
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Study on the design and analysis methods of orthogonal experiment
Liu Ruijiang,Zhang Yewang,Wen Chongwei,Tang Jian(School of Pharmaceutics,Jiangsu University,Zhenjiang 212013,China)The importance of orthogonal experimental design and analysis is introduced briefly.The principle and characteristic are expounded.The design methods of orthogonal experiment and analysis methods of orthogonal experimental results are analyzed in detail,which afford fully systemic methods for orthogonal experimental design and analysis.Problems in orthogonal experimental design and analysis and development of software for orthogonal experimental design and analysis are also pointed out in the end.
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Research on statistical analyses and countermeasures of 100 laboratory accidents
Li Zhihong;Training Department,Kunming Fire Command School;This paper summarizes 100typical cases of laboratory accidents from 2001and analyzes the cases in fields of accident type,accident link,accident cause,dangerous substance category,etc.The result shows as follows:the fire disasters and explosive accidents are the main types of laboratory accidents;the dangerous chemicals,instruments and equipment,and pressure vessels are main dangerous substances;the instruments and equipment and reagent application processes are the main links of accidents;the violation of rules,improper operation,carelessness,wire short circuit and aging are the main reasons of accidents.It also puts forward the countermeasures and suggestions for the prevention and control of laboratory accidents in the following aspects:establishing complete safety management system,actively promoting standard construction of laboratory safety,strengthening laboratory safety education and training,and formulating and improving emergency plans for laboratory accidents.
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Promotion of reform and innovation on integration of theory teaching and experimental teaching by virtual simulation experiment teaching
XIONG Hongqi;Based on the concept of experimental teaching and its importance, the connotation of virtual simulation experimental teaching is expounded upon. On this basis, this paper puts forward six balance principles that virtual simulation experimental teaching should follow to promote the upgrading and reconstruction of traditional experimental teaching and elaborates the reform idea of virtual simulation experiment teaching for the overall optimization and innovation of theory teaching. The brief analysis is carried out on that the introduction of virtual simulation experimental teaching is conducive to promoting innovation and entrepreneurship education into the whole process of professional education.
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Research and application of BOPPPS teaching method in MOOC teaching design
WU Changdong;JIANG Hua;CHEN Yongqiang;School of Electrical Engineering and Electronic Information,Xihua University;School of Information Science and Technology,Southwest Jiaotong University;On the basis of introducing the connotation of BOPPPS(bridge-in,objective,pre-assessment,participatory learning,post-assessment and summary)model,this paper explores upon the guiding role of the BOPPPS teaching model in MOOC teaching design.Based on the BOPPPS model,MOOC teaching design of"Series feedback voltage stabilization circuit"is carried out.This provides some reference for improving the quality of MOOC teaching design,stimulating students' learning interest and motivation,and promoting teachers' reform of teaching content design.
[Downloads: 3,883 ] [Citations: 273 ] [Reads: 181 ] HTML PDF Cite this article
The application of studying fluorescence spectroscopy on protein
Yin Yanxia,Xiang Benqiong,Tong Li(College of Life Science,Beijing Normal University,Beijing 100875,China)Fluorescence spectroscopy is very important for studying protein structure and conformation changes.The concept and principle of fluorescence spectroscopy are introduced at first,then the application of studying fluorescence spectroscopy on protein is explained.
[Downloads: 5,412 ] [Citations: 270 ] [Reads: 1414 ] HTML PDF Cite this article
The CNC machine tool with systematic work process and its application of teaching design
Li Yanxian(Department of Mechanical and Electronic Engineering,Nanjing Communications Institute of Technology,Nanjing 211188,China)According to professional training objectives and the main jobs of the structure of vocational skills and knowledge required to "CNC machine tools and spare parts" for the carrier,taking the CNC programming and operation of capacity-building as the center,this paper shows the design of the "knowledge of CNC machine tools,observation and analysis of CNC lathes,CNC milling machine to observe and analyze the processing center,programming and processing stepped shaft,threaded shaft of the programming and processing,hand wheel slot programming and processing,convex programming and processing of the template,the base of the programming and processing"of 9 items,25 learning environment,67 tasks,and one of the "convex template programming and processing" learning environment for the teaching unit design.
[Downloads: 383,527 ] [Citations: 7 ] [Reads: 170 ] HTML PDF Cite this article
Study on the design and analysis methods of orthogonal experiment
Liu Ruijiang,Zhang Yewang,Wen Chongwei,Tang Jian(School of Pharmaceutics,Jiangsu University,Zhenjiang 212013,China)The importance of orthogonal experimental design and analysis is introduced briefly.The principle and characteristic are expounded.The design methods of orthogonal experiment and analysis methods of orthogonal experimental results are analyzed in detail,which afford fully systemic methods for orthogonal experimental design and analysis.Problems in orthogonal experimental design and analysis and development of software for orthogonal experimental design and analysis are also pointed out in the end.
[Downloads: 55,909 ] [Citations: 3,450 ] [Reads: 1377 ] HTML PDF Cite this article
Construction and actualization of new experimental teaching system for chemical specialty
YANG Jin-tian(Institute of Life Science,Huzhou Normal College,Huzhou 313000,China)The new system of chemical experiment teaching is constructed,and the comprehensive experiments,open experiments and research-oriented experiments are set up to improve the degree of source sharing,the efficiency of using equipment and the quality of experimental teaching,hence efficiently optimizing the practical abilities and fostering innovative spirit for the undergraduates are achieved.
[Downloads: 24,314 ] [Citations: 11 ] [Reads: 1398 ] HTML PDF Cite this article
Research on statistical analyses and countermeasures of 100 laboratory accidents
Li Zhihong;Training Department,Kunming Fire Command School;This paper summarizes 100typical cases of laboratory accidents from 2001and analyzes the cases in fields of accident type,accident link,accident cause,dangerous substance category,etc.The result shows as follows:the fire disasters and explosive accidents are the main types of laboratory accidents;the dangerous chemicals,instruments and equipment,and pressure vessels are main dangerous substances;the instruments and equipment and reagent application processes are the main links of accidents;the violation of rules,improper operation,carelessness,wire short circuit and aging are the main reasons of accidents.It also puts forward the countermeasures and suggestions for the prevention and control of laboratory accidents in the following aspects:establishing complete safety management system,actively promoting standard construction of laboratory safety,strengthening laboratory safety education and training,and formulating and improving emergency plans for laboratory accidents.
[Downloads: 10,675 ] [Citations: 569 ] [Reads: 162 ] HTML PDF Cite this article
Practice and thinking of education of“College Students' Innovative and Entrepreneurial Training Program”based on tutor system
Qian Xiaoming;Rong Huawei;Qian Jingzhu;Office of Academic Affairs,Nanjing University of Technology;The innovation and entrepreneurship education has been included in the teaching and education program of college schools."College Students' Innovative and Entrepreneurship Training Program "has become an"Excellent Program"as one of the most important reform tasks in Ministry of Education.The tutor system is an effective way of innovative education and pilot training for both college schools and students.Students learn the method of innovation researches and technique of entrepreneurial process through the program.In the meanwhile,teachers in college schools find a new stage to improve their teaching ability.This article focuses on the project,practice and feasibility of the"College Students' Innovative and Entrepreneurial Training Program "under the tutor system.
[Downloads: 10,111 ] [Citations: 225 ] [Reads: 1391 ] HTML PDF Cite this article