Recently,the Standardization Administration of China issued Announcement No.1 of 2026,officially approving the establishment of the Subcommittee 1 on Nonwoven Material of National Technical Committee 606 on Technical ...Recently,the Standardization Administration of China issued Announcement No.1 of 2026,officially approving the establishment of the Subcommittee 1 on Nonwoven Material of National Technical Committee 606 on Technical Textiles of Standardization Administration of China(SAC/TC606/SC1)and the Subcommittee 2 on Filtration and Separation Textiles of National Technical Committee 606 on Technical Textiles of Standardization Administration of China(SAC/TC606/SC2).The formation of these two subcommittees marks a crucial step in the standardization development of China's industrial textiles sector in specialized fields.展开更多
With the continuous advancement and maturation of technologies such as big data,artificial intelligence,virtual reality,robotics,human-machine collaboration,and augmented reality,many enterprises are finding new avenu...With the continuous advancement and maturation of technologies such as big data,artificial intelligence,virtual reality,robotics,human-machine collaboration,and augmented reality,many enterprises are finding new avenues for digital transformation and intelligent upgrading.Industry 5.0,a further extension and development of Industry 4.0,has become an important development trend in industry with more emphasis on human-centered sustainability and flexibility.Accordingly,both the industrial metaverse and digital twins have attracted much attention in this new era.However,the relationship between them is not clear enough.In this paper,a comparison between digital twins and the metaverse in industry is made firstly.Then,we propose the concept and framework of Digital Twin Systems Engineering(DTSE)to demonstrate how digital twins support the industrial metaverse in the era of Industry 5.0 by integrating systems engineering principles.Furthermore,we discuss the key technologies and challenges of DTSE,in particular how artificial intelligence enhances the application of DTSE.Finally,a specific application scenario in the aviation field is presented to illustrate the application prospects of DTSE.展开更多
Based on the requirements of local high-quality economic development and addressing the critical task of transformation and upgrading in the tea industry,this paper systematically discusses the necessity and feasibili...Based on the requirements of local high-quality economic development and addressing the critical task of transformation and upgrading in the tea industry,this paper systematically discusses the necessity and feasibility of constructing an optimal industrialization operation system driven by the dual wheels of"branding+standardization".The article first clarifies the connotation of high-quality development and the synergistic mechanism between branding and standardization.It then analyzes the current situation and bottlenecks of China's tea industry development.Subsequently,it proposes a dual-wheel drive strategy where branding enhances value and standardization guarantees quality,and designs a systematic implementation plan involving industrial chain synergy optimization and integrated support from government,industry,academia,research,and application.On this basis,strategies and suggestions are proposed,encompassing the starting point,standard focal points,key effort areas,innovation points,and target achievement points.The aim is to promote the tea industry to break through homogeneous competition,achieve value ascent,and provide important industrial support for regional high-quality development through the construction of the aforementioned system.展开更多
The rapid evolution of industrial robots from automation tools to intelligent systems marks a pivotal shift in manufacturing practices within the framework of Industry 4.0.Industrial robots,once limited to repetitive ...The rapid evolution of industrial robots from automation tools to intelligent systems marks a pivotal shift in manufacturing practices within the framework of Industry 4.0.Industrial robots,once limited to repetitive tasks on assembly lines,are now increasingly powered by advanced technologies such as Artificial Intelligence(AI),machine learning,and the Internet of Things(IoT),enabling them to perform complex,adaptive tasks in real-time.This paper explores the technological advancements that have transformed industrial robots,highlighting the integration of AI,smart sensors,and autonomous systems.Furthermore,it examines the implications of this paradigm shift for industries,human-robot collaboration,and the workforce.While intelligent robots promise greater efficiency,flexibility,and safety in manufacturing,challenges regarding implementation,economic impact,and ethical considerations remain significant.The paper concludes by looking at the future trends in robotics and their potential to reshape the global industrial landscape.展开更多
The“government-industry-university-research”model has significant practical significance in promoting the development of industries in colleges and universities and improving the quality of talent cultivation.This p...The“government-industry-university-research”model has significant practical significance in promoting the development of industries in colleges and universities and improving the quality of talent cultivation.This paper first provides a brief explanation of the concept and significance of the“government-industry-university-research”model,then conducts an in-depth analysis of the problems faced by the development of university industries,and finally proposes effective solutions to the problems faced by the development of university industries,hoping to provide some references and lessons for promoting the continuous development of university industries and the integration of industry and education.展开更多
Extraction unit operation is the first step in traditional Chinese medicine(TCM)product manufacturing,and it is crucial in determining the quality of the produced medicine.However,due to a lack of effective multimodal...Extraction unit operation is the first step in traditional Chinese medicine(TCM)product manufacturing,and it is crucial in determining the quality of the produced medicine.However,due to a lack of effective multimodal monitoring and adjustment strategies,achieving high quality and efficiency remains a challenge.In this work,we proposed an artificial intelligence(AI)-based robot platform for the multi-objective optimization of the extraction process.First,a perception intelligence method for multimodal process monitoring was established to track active ingredient transfer and production changes during the extraction process.Second,a digital twin model was developed to reconstruct the field information,which interacted with real-time monitoring data.Furthermore,the model performed real-time inference to predict future production process states by using the reconstructing information.Finally,according to the predicted process states,the autonomous decision-making robot implemented multi-objective optimization,ensuring efficient process adjustments for global optimization.Experimental and industrial results demonstrated that the platform could effectively infer component transfer dynamics,monitor temperature variations,and identify boiling states,ensuring product quality while reducing energy consumption.This pharmaceutical robot could promote the integration of AI and pharmaceutical engineering,thereby accelerating the iterative development and improvement of China’s pharmaceutical industry.展开更多
Objective To empirically analyze the relationship between Government R&D funding and R&D investment of the enterprises in different sub industries of pharmaceutical industry,and to provide reference for the de...Objective To empirically analyze the relationship between Government R&D funding and R&D investment of the enterprises in different sub industries of pharmaceutical industry,and to provide reference for the development of policies related to R&D funding input.Methods Granger causality test was performed using the data of relevant indicators in different sub industries of China’s pharmaceutical industry from 1995 to 2019 based on the theory of covariance.Results and Conclusion The funding of R&D from the government had a significant positive effect on their R&D funding inputs to enterprises with chemo products,Chinese patent products,and biological products.It means the improvement of government funding was beneficial in promoting the R&D investment from various sub industries of pharmaceutical industry.The order of this influence was biological products,chemo products,and Chinese patent drugs.As to chemical drugs and biological products,the government’s R&D funding and enterprises R&D funding input showed a good trend of mutual promotion in a certain lag period.The government can fully leverage its funding to promote the investment of all sub industries of pharmaceutical industry.Meanwhile,regulatory mechanisms should be refined for government funding.For the inheritance,innovation,and development of traditional Chinese medicine,the government should give more policy support than financial support.展开更多
In the Tianshan region,a complete textile industry chain has been established,covering the entire process from cotton cultivation and chemical fiber production,through spinning,weaving,dyeing,and finishing,and further...In the Tianshan region,a complete textile industry chain has been established,covering the entire process from cotton cultivation and chemical fiber production,through spinning,weaving,dyeing,and finishing,and further extending to apparel,home textiles,and industrial textiles.In November 2025,the first list of five characteristic textile and apparel industry clusters in Xinjiang was officially announced,marking a new stage in the clustering of Xinjiang's textile and apparel industry.Data shows that the total output value of Xinjiang's cotton and textile and apparel industry chain has exceeded 220 billion yuan.With the nation's largest cotton production,a complete industrial chain system,and strong synergistic effects,Xinjiang has become a leading and highly competitive textile industry hub in China.展开更多
In recent years,against the backdrop of profound geopolitical changes,the European Union(EU)has been deeply mired in an industrial predicament,showing signs of shrinking production,capital outflows,relocation of produ...In recent years,against the backdrop of profound geopolitical changes,the European Union(EU)has been deeply mired in an industrial predicament,showing signs of shrinking production,capital outflows,relocation of production capacity,and lagging development in future industries.The primary causes of this situation include the energy supply crisis stemming from the decoupling from Russian energy,the impact of America's unilateral trade policy,structural flaws within the EU itself,and the intensification of global industrial competition.To overcome these challenges,the EU has introduced a series of new industrial policies,aiming at revitalizing its industries by strengthening support for local manufacturing,enhancing protective mechanisms for its domestic production,building diversified supply chains,and prioritizing the development of military industry.However,due to multiple obstacles such as heavy external dependence,significant funding gaps,internal divisions,and strategic short-sightedness,the EU still faces a long and arduous journey in its industrial revival.展开更多
The increasing interconnection of modern industrial control systems(ICSs)with the Internet has enhanced operational efficiency,but alsomade these systemsmore vulnerable to cyberattacks.This heightened exposure has dri...The increasing interconnection of modern industrial control systems(ICSs)with the Internet has enhanced operational efficiency,but alsomade these systemsmore vulnerable to cyberattacks.This heightened exposure has driven a growing need for robust ICS security measures.Among the key defences,intrusion detection technology is critical in identifying threats to ICS networks.This paper provides an overview of the distinctive characteristics of ICS network security,highlighting standard attack methods.It then examines various intrusion detection methods,including those based on misuse detection,anomaly detection,machine learning,and specialised requirements.This paper concludes by exploring future directions for developing intrusion detection systems to advance research and ensure the continued security and reliability of ICS operations.展开更多
As attack techniques evolve and data volumes increase,the integration of artificial intelligence-based security solutions into industrial control systems has become increasingly essential.Artificial intelligence holds...As attack techniques evolve and data volumes increase,the integration of artificial intelligence-based security solutions into industrial control systems has become increasingly essential.Artificial intelligence holds significant potential to improve the operational efficiency and cybersecurity of these systems.However,its dependence on cyber-based infrastructures expands the attack surface and introduces the risk that adversarial manipulations of artificial intelligence models may cause physical harm.To address these concerns,this study presents a comprehensive review of artificial intelligence-driven threat detection methods and adversarial attacks targeting artificial intelligence within industrial control environments,examining both their benefits and associated risks.A systematic literature review was conducted across major scientific databases,including IEEE,Elsevier,Springer Nature,ACM,MDPI,and Wiley,covering peer-reviewed journal and conference papers published between 2017 and 2026.Studies were selected based on predefined inclusion and exclusion criteria following a structured screening process.Based on an analysis of 101 selected studies,this survey categorizes artificial intelligence-based threat detection approaches across the physical,control,and application layers of industrial control systems and examines poisoning,evasion,and extraction attacks targeting industrial artificial intelligence.The findings identify key research trends,highlight unresolved security challenges,and discuss implications for the secure deployment of artificial intelligence-enabled cybersecurity solutions in industrial control systems.展开更多
As the annual production of industrial hemp in China increases and its global market share grows,its multipurpose development has become an important trend for future development.The cellulose mass fraction of industr...As the annual production of industrial hemp in China increases and its global market share grows,its multipurpose development has become an important trend for future development.The cellulose mass fraction of industrial hemp was found to be as high as 59.36%by chemical composition determination,providing a possibility for the production of nanocellulose.To broaden the application field of industrial hemp,the 2,2,6,6-tetramethylpiperidine-1-oxyl radical(TEMPO)-oxidized nanocellulose(TCNF),sulfuric acid hydrolyzed nanocellulose(SCNC),and lignincontaining hydrolyzed nanocellulose(LCNC)were prepared by multi-step chemical purification pretreatment combined with TEMPO oxidation and sulfuric acid hydrolysis,respectively.They were characterized by Fourier transform infrared(FTIR)spectroscopy,X-ray diffraction(XRD),and thermogravimetric analysis(TGA).The effects of the sodium hypochlorite volume,sodium hydroxide mass fraction in the pretreatment process,and acid hydrolysis reaction time on the Zeta potential and particle size of the prepared nanocellulose were investigated.The absolute value of the Zeta potential of SCNC could reach 29.59 mV,and the particle size was small.The suspension could still maintain good dispersion stability after standing for 24.0 h under the same dispersion conditions.The basic functional group composition and crystal morphology of TCNF,SCNC,and LCNC did not change compared with the raw hemp,and the highest crystallinity increased from 24.6%to 68.1%.Due to the introduction of ester and carboxyl groups,the initial degradation temperature and the temperature at the maximum mass loss rate of the nanocellulose were lower than those of the raw hemp,but the nanocellulose still maintained the thermal stability for practical applications.展开更多
“The silk of Suzhou,Hangzhou,Jiaxing and Huzhou are unrivalled in the world;all under heaven draw their supply from it.”So observed the Ming Dynasty(1368-1644)agronomist Xu Guangqi in his Complete Treatise on Agricu...“The silk of Suzhou,Hangzhou,Jiaxing and Huzhou are unrivalled in the world;all under heaven draw their supply from it.”So observed the Ming Dynasty(1368-1644)agronomist Xu Guangqi in his Complete Treatise on Agriculture,capturing the undisputed dominance of east China in sericulture centuries ago.展开更多
Industrial Cyber-Physical Systems(ICPSs)play a vital role in modern industries by providing an intellectual foundation for automated operations.With the increasing integration of information-driven processes,ensuring ...Industrial Cyber-Physical Systems(ICPSs)play a vital role in modern industries by providing an intellectual foundation for automated operations.With the increasing integration of information-driven processes,ensuring the security of Industrial Control Production Systems(ICPSs)has become a critical challenge.These systems are highly vulnerable to attacks such as denial-of-service(DoS),eclipse,and Sybil attacks,which can significantly disrupt industrial operations.This work proposes an effective protection strategy using an Artificial Intelligence(AI)-enabled Smart Contract(SC)framework combined with the Heterogeneous Barzilai-Borwein Support Vector(HBBSV)method for industrial-based CPS environments.The approach reduces run time and minimizes the probability of attacks.Initially,secured ICPSs are achieved through a comprehensive exchange of views on production plant strategies for condition monitoring using SC and blockchain(BC)integrated within a BC network.The SC executes the HBBSV strategy to verify the security consensus.The Barzilai-Borwein Support Vectorized algorithm computes abnormal attack occurrence probabilities to ensure that components operate within acceptable production line conditions.When a component remains within these conditions,no security breach occurs.Conversely,if a component does not satisfy the condition boundaries,a security lapse is detected,and those components are isolated.The HBBSV method thus strengthens protection against DoS,eclipse,and Sybil attacks.Experimental results demonstrate that the proposed HBBSV approach significantly improves security by enhancing authentication accuracy while reducing run time and authentication time compared to existing techniques.展开更多
In industrial manufacturing,efficient surface defect detection is crucial for ensuring product quality and production safety.Traditional inspectionmethods are often slow,subjective,and prone to errors,while classicalm...In industrial manufacturing,efficient surface defect detection is crucial for ensuring product quality and production safety.Traditional inspectionmethods are often slow,subjective,and prone to errors,while classicalmachine vision techniques strugglewith complex backgrounds and small defects.To address these challenges,this study proposes an improved YOLOv11 model for detecting defects on hot-rolled steel strips using the NEU-DET dataset.Three key improvements are introduced in the proposed model.First,a lightweight Guided Attention Feature Module(GAFM)is incorporated to enhance multi-scale feature fusion,allowing the model to better capture and integrate semantic and spatial information across different layers,which improves its ability to detect defects of varying sizes.Second,an Aggregated Attention(AA)mechanism is employed to strengthen the representation of critical defect features while effectively suppressing irrelevant background information,particularly enhancing the detection of small,low-contrast,or complex defects.Third,Ghost Dynamic Convolution(GDC)is applied to reduce computational cost by generating low-cost ghost features and dynamically reweighting convolutional kernels,enabling faster inference without sacrificing feature quality or detection accuracy.Extensive experiments demonstrate that the proposed model achieves a mean Average Precision(mAP)of 87.2%,compared to 81.5%for the baseline,while lowering computational cost from6.3Giga Floating-point Operations Per Second(GFLOPs)to 5.1 GFLOPs.These results indicate that the improved YOLOv11 is both accurate and computationally efficient,making it suitable for real-time industrial surface defect detection and contributing to the development of practical,high-performance inspection systems.展开更多
Modern industrial environments require uninterrupted machinery operation to maintain productivity standards while ensuring safety and minimizing costs.Conventional maintenance methods,such as reactive maintenance(i.e....Modern industrial environments require uninterrupted machinery operation to maintain productivity standards while ensuring safety and minimizing costs.Conventional maintenance methods,such as reactive maintenance(i.e.,run to failure)or time-based preventive maintenance(i.e.,scheduled servicing),prove ineffective for complex systems with many Internet of Things(IoT)devices and sensors because they fall short in detecting faults at early stages when it is most crucial.This paper presents a predictive maintenance framework based on a hybrid deep learning model that integrates the capabilities of Long Short-Term Memory(LSTM)Networks and Convolutional Neural Networks(CNNs).The framework integrates spatial feature extraction and temporal sequence modeling to accurately classify the health state of industrial equipment into three categories,including Normal,Require Maintenance,and Failed.The framework uses a modular pipeline that includes IoT-enabled data collection along with secure transmission methods to manage cloud storage and provide real-time fault classification.The FD004 subset of the NASA C-MAPSS dataset,containing multivariate sensor readings from aircraft engines,serves as the training and evaluation data for the model.Experimental results show that the LSTM-CNN model outperforms baseline models such as LSTM-SVM and LSTM-RNN,achieving an overall average accuracy of 86.66%,precision of 86.00%,recall of 86.33%,and F1-score of 86.33%.Contrary to the previous LSTM-CNN-based predictive maintenance models that either provide a binary classification or rely on synthetically balanced data,our paper provides a three-class maintenance state(i.e.,Normal,Require Maintenance,and Failed)along with threshold-based labeling that retains the true nature of the degradation.In addition,our work also provides an IoT-to-cloud-based modular architecture for deployment.It offers Computerized Maintenance Management System(CMMS)integration,making our proposed solution not only technically sound but also practical and innovative.The solution achieves real-world industrial deployment readiness through its reliable performance alongside its scalable system design.展开更多
Zuoquan County has revitalized local villages with the fruits of their collected labor.GLOWING red apples hang heavily on branches in the orchards on the southern outskirts of Tongyu Town,Zuoquan County,Shanxi Provinc...Zuoquan County has revitalized local villages with the fruits of their collected labor.GLOWING red apples hang heavily on branches in the orchards on the southern outskirts of Tongyu Town,Zuoquan County,Shanxi Province,an area where a decade ago weeds and rocks covered a rather barren landscape.展开更多
Industrial robot dynamics lay the foundation for high-precision and high-speed control, and accurate identification of dynamic parameters is essential for precise dynamic calculations. The choice of friction models is...Industrial robot dynamics lay the foundation for high-precision and high-speed control, and accurate identification of dynamic parameters is essential for precise dynamic calculations. The choice of friction models is a critical component in the identification of industrial robot dynamics. Traditional static friction models struggle to capture the hysteresis effects caused by robot joint elasticity and clearances, leading to large torque prediction errors when the joint velocity crosses zero. Due to the presence of hysteresis effects, the joint velocity crosses zero in the forward direction, and the reverse direction will have different friction patterns. Although the hysteresis effects can be modeled as an ordinary differential equation(ODE), it is difficult to determine the ODE structure that achieves both generalization and accuracy to describe the hysteresis effects of the friction model. To address this issue, we propose the neural hysteresis friction(NHF), which uses neural ODE to model the hysteresis effects in a data-driven manner, thereby mitigating the current inadequacies in the study of dynamic friction characteristics. The experiments on a real 6-axis industrial robot demonstrate that our proposed method can accurately model the friction dynamics during directional switching and outperform other modeling methods. Velocity tracking control experiments show that NHF can effectively reduce tracking errors when the velocity crosses zero.展开更多
Bottom-up and top-down endogenous automobile clusters exhibit distinct evolutionary traits and driving mechanisms,yet their comparative analysis remains understudied.Therefore,using Taizhou automobile industry cluster...Bottom-up and top-down endogenous automobile clusters exhibit distinct evolutionary traits and driving mechanisms,yet their comparative analysis remains understudied.Therefore,using Taizhou automobile industry cluster(TAIC)and Wuhu automobile industry cluster(WAIC)as cases,using historical statistical data and field interview data from the 1980s to 2023,combined with qualitative research methods of thematic and diachronic analysis,and quantitative research methods of social network analysis,we compare both endogenous automobile clusters’evolutionary traits and driving mechanisms.The results confirm both clusters undergo multi-scale spatial reconfiguration,organizational complexification,and intelligent networking technological transformation,yet diverge fundamentally:TAIC evolves through market-driven progressive expansion,transitioning from single to dual-core structures via private enterprise networking,with innovation following market-integrated logic and institutional thickness built on demand-driven evolution.Conversely,WAIC follows planned expansion,maintaining state-led hierarchical single-core stability through policy-driven breakthrough innovation and supply-dominated institutional construction-though both ultimately require formal-informal system synergy.Their coevolution is driven by dynamic interactions of path dependence(weakening influence),learning-innovation(strengthening influence),and relationship selection(inverted U-shaped trajectory),with divergent development paths rooted in TAIC’s grassroots self-organization genes versus WAIC’s top-level design genes,amplified by core enterprises’strategic disparities.The research findings can not only provide decision-making support for China’s industrial upgrading,but also contribute China’s insights to global economic governance.展开更多
Industrial waste salts are commonly used to make value-added snow-melting agents to ensure traffic safety in northern China during winter and spring after snowfall.However,heavy metals in industrial waste salts may po...Industrial waste salts are commonly used to make value-added snow-melting agents to ensure traffic safety in northern China during winter and spring after snowfall.However,heavy metals in industrial waste salts may pose certain environmental risks.Snow-melting agents and snow samples were collected and analyzed from highways,arterial roads,footbridges,and other locations in Beijing after the snowstorm in December 2023.It was found that the main component of snow-melting agents was sodium chloride with high concentrations of Cu,Mn,and Zn,which are not regulated in the current policies,despite the recent promotion of environmentally friendly snow-melting agents.The Pb,Zn and Cr contents of some snow samples exceeded the limitation value of surface water quality standards,potentially affecting the soil and water environment near roadsides,although the snow-melting agents comply with relevant standards,which indicates the policy gap in the management of recycled industrial salts.We reviewed and analyzed the relevant standards for snow-melting agents and industrial waste salts proposed nationally and internationally over the past 30 years.Through comparative analysis,we proposed relevant policy recommendations to the existing quality standards of snow-melting agents and the management regulations of industrial waste salts,and the formulation of corresponding usage strategies,aimed at reducing the potential environmental release of heavy metals from the use of snow-melting agents,thereby promoting more sustainable green urban development and environmentally sound waste management.展开更多
文摘Recently,the Standardization Administration of China issued Announcement No.1 of 2026,officially approving the establishment of the Subcommittee 1 on Nonwoven Material of National Technical Committee 606 on Technical Textiles of Standardization Administration of China(SAC/TC606/SC1)and the Subcommittee 2 on Filtration and Separation Textiles of National Technical Committee 606 on Technical Textiles of Standardization Administration of China(SAC/TC606/SC2).The formation of these two subcommittees marks a crucial step in the standardization development of China's industrial textiles sector in specialized fields.
基金Supported by Beijing Municipal Natural Science Foundation of China(Grant No.24JL002)China Postdoctoral Science Foundation(Grant No.2024M754054)+2 种基金National Natural Science Foundation of China(Grant No.52120105008)Beijing Municipal Outstanding Young Scientis Program of Chinathe New Cornerstone Science Foundation through the XPLORER PRIZE。
文摘With the continuous advancement and maturation of technologies such as big data,artificial intelligence,virtual reality,robotics,human-machine collaboration,and augmented reality,many enterprises are finding new avenues for digital transformation and intelligent upgrading.Industry 5.0,a further extension and development of Industry 4.0,has become an important development trend in industry with more emphasis on human-centered sustainability and flexibility.Accordingly,both the industrial metaverse and digital twins have attracted much attention in this new era.However,the relationship between them is not clear enough.In this paper,a comparison between digital twins and the metaverse in industry is made firstly.Then,we propose the concept and framework of Digital Twin Systems Engineering(DTSE)to demonstrate how digital twins support the industrial metaverse in the era of Industry 5.0 by integrating systems engineering principles.Furthermore,we discuss the key technologies and challenges of DTSE,in particular how artificial intelligence enhances the application of DTSE.Finally,a specific application scenario in the aviation field is presented to illustrate the application prospects of DTSE.
基金Supported by General Project of Philosophy and Social Sciences Research in Universities of Jiangsu Province,2024(2024SJYB1650).
文摘Based on the requirements of local high-quality economic development and addressing the critical task of transformation and upgrading in the tea industry,this paper systematically discusses the necessity and feasibility of constructing an optimal industrialization operation system driven by the dual wheels of"branding+standardization".The article first clarifies the connotation of high-quality development and the synergistic mechanism between branding and standardization.It then analyzes the current situation and bottlenecks of China's tea industry development.Subsequently,it proposes a dual-wheel drive strategy where branding enhances value and standardization guarantees quality,and designs a systematic implementation plan involving industrial chain synergy optimization and integrated support from government,industry,academia,research,and application.On this basis,strategies and suggestions are proposed,encompassing the starting point,standard focal points,key effort areas,innovation points,and target achievement points.The aim is to promote the tea industry to break through homogeneous competition,achieve value ascent,and provide important industrial support for regional high-quality development through the construction of the aforementioned system.
文摘The rapid evolution of industrial robots from automation tools to intelligent systems marks a pivotal shift in manufacturing practices within the framework of Industry 4.0.Industrial robots,once limited to repetitive tasks on assembly lines,are now increasingly powered by advanced technologies such as Artificial Intelligence(AI),machine learning,and the Internet of Things(IoT),enabling them to perform complex,adaptive tasks in real-time.This paper explores the technological advancements that have transformed industrial robots,highlighting the integration of AI,smart sensors,and autonomous systems.Furthermore,it examines the implications of this paradigm shift for industries,human-robot collaboration,and the workforce.While intelligent robots promise greater efficiency,flexibility,and safety in manufacturing,challenges regarding implementation,economic impact,and ethical considerations remain significant.The paper concludes by looking at the future trends in robotics and their potential to reshape the global industrial landscape.
文摘The“government-industry-university-research”model has significant practical significance in promoting the development of industries in colleges and universities and improving the quality of talent cultivation.This paper first provides a brief explanation of the concept and significance of the“government-industry-university-research”model,then conducts an in-depth analysis of the problems faced by the development of university industries,and finally proposes effective solutions to the problems faced by the development of university industries,hoping to provide some references and lessons for promoting the continuous development of university industries and the integration of industry and education.
基金funded by the National Key Research and Development Program of China(2024YFC3506900)the Special Project for Technological Innovation in New Productive Forces of Modern Chinese Medicines(24ZXZKSY00010 and 24ZXZKSY00040)the Innovation Team and Talents Cultivation Program of National Administration of Traditional Chinese Medicine(ZYYCXTD-D-202002)。
文摘Extraction unit operation is the first step in traditional Chinese medicine(TCM)product manufacturing,and it is crucial in determining the quality of the produced medicine.However,due to a lack of effective multimodal monitoring and adjustment strategies,achieving high quality and efficiency remains a challenge.In this work,we proposed an artificial intelligence(AI)-based robot platform for the multi-objective optimization of the extraction process.First,a perception intelligence method for multimodal process monitoring was established to track active ingredient transfer and production changes during the extraction process.Second,a digital twin model was developed to reconstruct the field information,which interacted with real-time monitoring data.Furthermore,the model performed real-time inference to predict future production process states by using the reconstructing information.Finally,according to the predicted process states,the autonomous decision-making robot implemented multi-objective optimization,ensuring efficient process adjustments for global optimization.Experimental and industrial results demonstrated that the platform could effectively infer component transfer dynamics,monitor temperature variations,and identify boiling states,ensuring product quality while reducing energy consumption.This pharmaceutical robot could promote the integration of AI and pharmaceutical engineering,thereby accelerating the iterative development and improvement of China’s pharmaceutical industry.
文摘Objective To empirically analyze the relationship between Government R&D funding and R&D investment of the enterprises in different sub industries of pharmaceutical industry,and to provide reference for the development of policies related to R&D funding input.Methods Granger causality test was performed using the data of relevant indicators in different sub industries of China’s pharmaceutical industry from 1995 to 2019 based on the theory of covariance.Results and Conclusion The funding of R&D from the government had a significant positive effect on their R&D funding inputs to enterprises with chemo products,Chinese patent products,and biological products.It means the improvement of government funding was beneficial in promoting the R&D investment from various sub industries of pharmaceutical industry.The order of this influence was biological products,chemo products,and Chinese patent drugs.As to chemical drugs and biological products,the government’s R&D funding and enterprises R&D funding input showed a good trend of mutual promotion in a certain lag period.The government can fully leverage its funding to promote the investment of all sub industries of pharmaceutical industry.Meanwhile,regulatory mechanisms should be refined for government funding.For the inheritance,innovation,and development of traditional Chinese medicine,the government should give more policy support than financial support.
文摘In the Tianshan region,a complete textile industry chain has been established,covering the entire process from cotton cultivation and chemical fiber production,through spinning,weaving,dyeing,and finishing,and further extending to apparel,home textiles,and industrial textiles.In November 2025,the first list of five characteristic textile and apparel industry clusters in Xinjiang was officially announced,marking a new stage in the clustering of Xinjiang's textile and apparel industry.Data shows that the total output value of Xinjiang's cotton and textile and apparel industry chain has exceeded 220 billion yuan.With the nation's largest cotton production,a complete industrial chain system,and strong synergistic effects,Xinjiang has become a leading and highly competitive textile industry hub in China.
文摘In recent years,against the backdrop of profound geopolitical changes,the European Union(EU)has been deeply mired in an industrial predicament,showing signs of shrinking production,capital outflows,relocation of production capacity,and lagging development in future industries.The primary causes of this situation include the energy supply crisis stemming from the decoupling from Russian energy,the impact of America's unilateral trade policy,structural flaws within the EU itself,and the intensification of global industrial competition.To overcome these challenges,the EU has introduced a series of new industrial policies,aiming at revitalizing its industries by strengthening support for local manufacturing,enhancing protective mechanisms for its domestic production,building diversified supply chains,and prioritizing the development of military industry.However,due to multiple obstacles such as heavy external dependence,significant funding gaps,internal divisions,and strategic short-sightedness,the EU still faces a long and arduous journey in its industrial revival.
文摘The increasing interconnection of modern industrial control systems(ICSs)with the Internet has enhanced operational efficiency,but alsomade these systemsmore vulnerable to cyberattacks.This heightened exposure has driven a growing need for robust ICS security measures.Among the key defences,intrusion detection technology is critical in identifying threats to ICS networks.This paper provides an overview of the distinctive characteristics of ICS network security,highlighting standard attack methods.It then examines various intrusion detection methods,including those based on misuse detection,anomaly detection,machine learning,and specialised requirements.This paper concludes by exploring future directions for developing intrusion detection systems to advance research and ensure the continued security and reliability of ICS operations.
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(RS-2023-00242528,50%)supported by the Korea Internet&Security Agency(KISA)through the Information Security Specialized University Support Project(50%).
文摘As attack techniques evolve and data volumes increase,the integration of artificial intelligence-based security solutions into industrial control systems has become increasingly essential.Artificial intelligence holds significant potential to improve the operational efficiency and cybersecurity of these systems.However,its dependence on cyber-based infrastructures expands the attack surface and introduces the risk that adversarial manipulations of artificial intelligence models may cause physical harm.To address these concerns,this study presents a comprehensive review of artificial intelligence-driven threat detection methods and adversarial attacks targeting artificial intelligence within industrial control environments,examining both their benefits and associated risks.A systematic literature review was conducted across major scientific databases,including IEEE,Elsevier,Springer Nature,ACM,MDPI,and Wiley,covering peer-reviewed journal and conference papers published between 2017 and 2026.Studies were selected based on predefined inclusion and exclusion criteria following a structured screening process.Based on an analysis of 101 selected studies,this survey categorizes artificial intelligence-based threat detection approaches across the physical,control,and application layers of industrial control systems and examines poisoning,evasion,and extraction attacks targeting industrial artificial intelligence.The findings identify key research trends,highlight unresolved security challenges,and discuss implications for the secure deployment of artificial intelligence-enabled cybersecurity solutions in industrial control systems.
基金Fundamental Research Funds for the Central Universities of China(No.2232024G-01)Textile Vision Basic Research Program,China(No.J202305)。
文摘As the annual production of industrial hemp in China increases and its global market share grows,its multipurpose development has become an important trend for future development.The cellulose mass fraction of industrial hemp was found to be as high as 59.36%by chemical composition determination,providing a possibility for the production of nanocellulose.To broaden the application field of industrial hemp,the 2,2,6,6-tetramethylpiperidine-1-oxyl radical(TEMPO)-oxidized nanocellulose(TCNF),sulfuric acid hydrolyzed nanocellulose(SCNC),and lignincontaining hydrolyzed nanocellulose(LCNC)were prepared by multi-step chemical purification pretreatment combined with TEMPO oxidation and sulfuric acid hydrolysis,respectively.They were characterized by Fourier transform infrared(FTIR)spectroscopy,X-ray diffraction(XRD),and thermogravimetric analysis(TGA).The effects of the sodium hypochlorite volume,sodium hydroxide mass fraction in the pretreatment process,and acid hydrolysis reaction time on the Zeta potential and particle size of the prepared nanocellulose were investigated.The absolute value of the Zeta potential of SCNC could reach 29.59 mV,and the particle size was small.The suspension could still maintain good dispersion stability after standing for 24.0 h under the same dispersion conditions.The basic functional group composition and crystal morphology of TCNF,SCNC,and LCNC did not change compared with the raw hemp,and the highest crystallinity increased from 24.6%to 68.1%.Due to the introduction of ester and carboxyl groups,the initial degradation temperature and the temperature at the maximum mass loss rate of the nanocellulose were lower than those of the raw hemp,but the nanocellulose still maintained the thermal stability for practical applications.
文摘“The silk of Suzhou,Hangzhou,Jiaxing and Huzhou are unrivalled in the world;all under heaven draw their supply from it.”So observed the Ming Dynasty(1368-1644)agronomist Xu Guangqi in his Complete Treatise on Agriculture,capturing the undisputed dominance of east China in sericulture centuries ago.
文摘Industrial Cyber-Physical Systems(ICPSs)play a vital role in modern industries by providing an intellectual foundation for automated operations.With the increasing integration of information-driven processes,ensuring the security of Industrial Control Production Systems(ICPSs)has become a critical challenge.These systems are highly vulnerable to attacks such as denial-of-service(DoS),eclipse,and Sybil attacks,which can significantly disrupt industrial operations.This work proposes an effective protection strategy using an Artificial Intelligence(AI)-enabled Smart Contract(SC)framework combined with the Heterogeneous Barzilai-Borwein Support Vector(HBBSV)method for industrial-based CPS environments.The approach reduces run time and minimizes the probability of attacks.Initially,secured ICPSs are achieved through a comprehensive exchange of views on production plant strategies for condition monitoring using SC and blockchain(BC)integrated within a BC network.The SC executes the HBBSV strategy to verify the security consensus.The Barzilai-Borwein Support Vectorized algorithm computes abnormal attack occurrence probabilities to ensure that components operate within acceptable production line conditions.When a component remains within these conditions,no security breach occurs.Conversely,if a component does not satisfy the condition boundaries,a security lapse is detected,and those components are isolated.The HBBSV method thus strengthens protection against DoS,eclipse,and Sybil attacks.Experimental results demonstrate that the proposed HBBSV approach significantly improves security by enhancing authentication accuracy while reducing run time and authentication time compared to existing techniques.
基金supported in part by the National Natural Science Foundation of China(Grant No.62071123)in part by the Natural Science Foundation of Fujian Province(Grant Nos.2024J01971,2022J05202)in part by the Young and Middle-Aged Teacher Education Research Project of Fujian Province(Grant No.JAT210370).
文摘In industrial manufacturing,efficient surface defect detection is crucial for ensuring product quality and production safety.Traditional inspectionmethods are often slow,subjective,and prone to errors,while classicalmachine vision techniques strugglewith complex backgrounds and small defects.To address these challenges,this study proposes an improved YOLOv11 model for detecting defects on hot-rolled steel strips using the NEU-DET dataset.Three key improvements are introduced in the proposed model.First,a lightweight Guided Attention Feature Module(GAFM)is incorporated to enhance multi-scale feature fusion,allowing the model to better capture and integrate semantic and spatial information across different layers,which improves its ability to detect defects of varying sizes.Second,an Aggregated Attention(AA)mechanism is employed to strengthen the representation of critical defect features while effectively suppressing irrelevant background information,particularly enhancing the detection of small,low-contrast,or complex defects.Third,Ghost Dynamic Convolution(GDC)is applied to reduce computational cost by generating low-cost ghost features and dynamically reweighting convolutional kernels,enabling faster inference without sacrificing feature quality or detection accuracy.Extensive experiments demonstrate that the proposed model achieves a mean Average Precision(mAP)of 87.2%,compared to 81.5%for the baseline,while lowering computational cost from6.3Giga Floating-point Operations Per Second(GFLOPs)to 5.1 GFLOPs.These results indicate that the improved YOLOv11 is both accurate and computationally efficient,making it suitable for real-time industrial surface defect detection and contributing to the development of practical,high-performance inspection systems.
文摘Modern industrial environments require uninterrupted machinery operation to maintain productivity standards while ensuring safety and minimizing costs.Conventional maintenance methods,such as reactive maintenance(i.e.,run to failure)or time-based preventive maintenance(i.e.,scheduled servicing),prove ineffective for complex systems with many Internet of Things(IoT)devices and sensors because they fall short in detecting faults at early stages when it is most crucial.This paper presents a predictive maintenance framework based on a hybrid deep learning model that integrates the capabilities of Long Short-Term Memory(LSTM)Networks and Convolutional Neural Networks(CNNs).The framework integrates spatial feature extraction and temporal sequence modeling to accurately classify the health state of industrial equipment into three categories,including Normal,Require Maintenance,and Failed.The framework uses a modular pipeline that includes IoT-enabled data collection along with secure transmission methods to manage cloud storage and provide real-time fault classification.The FD004 subset of the NASA C-MAPSS dataset,containing multivariate sensor readings from aircraft engines,serves as the training and evaluation data for the model.Experimental results show that the LSTM-CNN model outperforms baseline models such as LSTM-SVM and LSTM-RNN,achieving an overall average accuracy of 86.66%,precision of 86.00%,recall of 86.33%,and F1-score of 86.33%.Contrary to the previous LSTM-CNN-based predictive maintenance models that either provide a binary classification or rely on synthetically balanced data,our paper provides a three-class maintenance state(i.e.,Normal,Require Maintenance,and Failed)along with threshold-based labeling that retains the true nature of the degradation.In addition,our work also provides an IoT-to-cloud-based modular architecture for deployment.It offers Computerized Maintenance Management System(CMMS)integration,making our proposed solution not only technically sound but also practical and innovative.The solution achieves real-world industrial deployment readiness through its reliable performance alongside its scalable system design.
文摘Zuoquan County has revitalized local villages with the fruits of their collected labor.GLOWING red apples hang heavily on branches in the orchards on the southern outskirts of Tongyu Town,Zuoquan County,Shanxi Province,an area where a decade ago weeds and rocks covered a rather barren landscape.
基金supported by the National Natural Science Foundation of China (Grant No.52188102)。
文摘Industrial robot dynamics lay the foundation for high-precision and high-speed control, and accurate identification of dynamic parameters is essential for precise dynamic calculations. The choice of friction models is a critical component in the identification of industrial robot dynamics. Traditional static friction models struggle to capture the hysteresis effects caused by robot joint elasticity and clearances, leading to large torque prediction errors when the joint velocity crosses zero. Due to the presence of hysteresis effects, the joint velocity crosses zero in the forward direction, and the reverse direction will have different friction patterns. Although the hysteresis effects can be modeled as an ordinary differential equation(ODE), it is difficult to determine the ODE structure that achieves both generalization and accuracy to describe the hysteresis effects of the friction model. To address this issue, we propose the neural hysteresis friction(NHF), which uses neural ODE to model the hysteresis effects in a data-driven manner, thereby mitigating the current inadequacies in the study of dynamic friction characteristics. The experiments on a real 6-axis industrial robot demonstrate that our proposed method can accurately model the friction dynamics during directional switching and outperform other modeling methods. Velocity tracking control experiments show that NHF can effectively reduce tracking errors when the velocity crosses zero.
基金Under the auspices of National Natural Science Foundation of China(No.42571219)Key Project of Zhejiang Province Soft Science Research Plan(No.2023C25014)。
文摘Bottom-up and top-down endogenous automobile clusters exhibit distinct evolutionary traits and driving mechanisms,yet their comparative analysis remains understudied.Therefore,using Taizhou automobile industry cluster(TAIC)and Wuhu automobile industry cluster(WAIC)as cases,using historical statistical data and field interview data from the 1980s to 2023,combined with qualitative research methods of thematic and diachronic analysis,and quantitative research methods of social network analysis,we compare both endogenous automobile clusters’evolutionary traits and driving mechanisms.The results confirm both clusters undergo multi-scale spatial reconfiguration,organizational complexification,and intelligent networking technological transformation,yet diverge fundamentally:TAIC evolves through market-driven progressive expansion,transitioning from single to dual-core structures via private enterprise networking,with innovation following market-integrated logic and institutional thickness built on demand-driven evolution.Conversely,WAIC follows planned expansion,maintaining state-led hierarchical single-core stability through policy-driven breakthrough innovation and supply-dominated institutional construction-though both ultimately require formal-informal system synergy.Their coevolution is driven by dynamic interactions of path dependence(weakening influence),learning-innovation(strengthening influence),and relationship selection(inverted U-shaped trajectory),with divergent development paths rooted in TAIC’s grassroots self-organization genes versus WAIC’s top-level design genes,amplified by core enterprises’strategic disparities.The research findings can not only provide decision-making support for China’s industrial upgrading,but also contribute China’s insights to global economic governance.
基金supported by the National Natural Science Foundation of China(No.22176200)the Industrial Innovation Entrepreneurial Team Project of Ordos 2021.
文摘Industrial waste salts are commonly used to make value-added snow-melting agents to ensure traffic safety in northern China during winter and spring after snowfall.However,heavy metals in industrial waste salts may pose certain environmental risks.Snow-melting agents and snow samples were collected and analyzed from highways,arterial roads,footbridges,and other locations in Beijing after the snowstorm in December 2023.It was found that the main component of snow-melting agents was sodium chloride with high concentrations of Cu,Mn,and Zn,which are not regulated in the current policies,despite the recent promotion of environmentally friendly snow-melting agents.The Pb,Zn and Cr contents of some snow samples exceeded the limitation value of surface water quality standards,potentially affecting the soil and water environment near roadsides,although the snow-melting agents comply with relevant standards,which indicates the policy gap in the management of recycled industrial salts.We reviewed and analyzed the relevant standards for snow-melting agents and industrial waste salts proposed nationally and internationally over the past 30 years.Through comparative analysis,we proposed relevant policy recommendations to the existing quality standards of snow-melting agents and the management regulations of industrial waste salts,and the formulation of corresponding usage strategies,aimed at reducing the potential environmental release of heavy metals from the use of snow-melting agents,thereby promoting more sustainable green urban development and environmentally sound waste management.
